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In the last few years, the size of Linked Open Data (LOD) describing artworks, in general or domain-specific Knowledge Graphs (KGs), is gradually increasing. This provides (art-)historians and Cultural Heritage professionals with a wealth of information to explore. Specifically, structured data about iconographical and iconological (icon) aspects, i.e. information about the subjects, concepts and meanings of artworks, are extremely valuable for the state-of-the-art of computational tools, e.g. content recognition through computer vision. Nevertheless, a data quality evaluation for art domains, fundamental for data reuse, is still missing. The purpose of this study is filling this gap with an overview of art-historical data quality in current KGs with a focus on the icon aspects.

This study’s analyses are based on established KG evaluation methodologies, adapted to the domain by addressing requirements from art historians’ theories. The authors first select several KGs according to Semantic Web principles. Then, the authors evaluate (1) their structures’ suitability to describe icon information through quantitative and qualitative assessment and (2) their content, qualitatively assessed in terms of correctness and completeness.

This study’s results reveal several issues on the current expression of icon information in KGs. The content evaluation shows that these domain-specific statements are generally correct but often not complete. The incompleteness is confirmed by the structure evaluation, which highlights the unsuitability of the KG schemas to describe icon information with the required granularity.

The main contribution of this work is an overview of the actual landscape of the icon information expressed in LOD. Therefore, it is valuable to cultural institutions by providing them a first domain-specific data quality evaluation. Since this study’s results suggest that the selected domain information is underrepresented in Semantic Web datasets, the authors highlight the need for the creation and fostering of such information to provide a more thorough art-historical dimension to LOD.

Customer experience management (CXM), which aims to achieve and maintain customers' long-term loyalty, has attracted the attention of many organizations. Improving customer experience management in organizations requires that, first, their relevant capabilities be evaluated. The present study aimed to offer a set of key performance indicators for evaluating customer experience management in commercial banks.

The study, first, attempted to identify the components of evaluating customer experience management by reviewing the related literature and conducting interviews with experts. Then, the extracted components were transformed into assessable metrics using the goal question metric method, and the key performance indicators relevant to customer experience management in commercial banks were selected according to the experts' opinions and the Fuzzy Delphi method.

According to the findings of the study, 21 key performance indicators were identified for customer experience management in commercial banks, and customer satisfaction, the mean number of calls to resolve an issue in customer journey touchpoints, the NPS, and the ratio of the budget allocated to the CXM department to the budget of the marketing department were found as the most significant performance indicator according to banking experts.

The present study was among the first research projects intended to evaluate CXM and offer key performance indicators that could help the managers of commercial banks assess the maturity levels of their CXM.

This manuscript aims to consider traditional success metrics in patient-oriented research (POR) using insights from the Helping Enable Access and Remove Barriers To Support for Young Adults with Mental Health-Related Disabilities (HEARTS) study.

Through collective reflexivity, this manuscript underscores the inadequacy of current evaluation standards that focus primarily on quantifiable outputs.

The findings suggest that significant systemic challenges persist, including ageism and discrimination, which undermine the efforts of POR.

This manuscript argues for an expanded evaluation encompassing traditional metrics and integrating emotional, experiential and community impact measures. Such an approach is crucial to capturing POR's comprehensive effects and fostering a research environment that values inclusivity, supports well-being and ensures responsive and equitable research practices. Thus, aligning with the transformative goals of POR, aiming to enhance the quality and impact of health research and reflect the profound personal and communal transformations that are as significant as the outcomes they facilitate.

This manuscript represents an emancipatory approach to POR, distinguished by its authentic co-authorship model. Uniquely, it is composed in collaboration with young adults who are experts in experience and coresearchers. These co-authors bring invaluable first-hand insights that both critique and enrich our understanding, enabling them to actively shape the discourse and direction of future POR research. This collaboration ensures the development of more relevant, grounded and transformative approaches in mental health research, thereby enhancing the pertinence and impact of these findings in real-world settings.

This study aims to propose a collaborative knowledge-based ontological research model for designing a collaborative product development process (PDP) while considering different design for X techniques.

This study follows a thematic literature analysis to identify the key design concepts needed to assess environmental, service, safety, manufacture and assembly, supply chain and quality concerns in developing a collaborative PDP.

The proposed model provides a guide for methodology, engineering and ontology evaluation metrics (verification, assessment and validation). The findings benefit both practitioners and managers because they address the key knowledge taxonomy needed to assist them in storing information, promoting teamwork and making decisions in a collaborative PDP while incorporating various design for X approaches and product life cycles.

This study introduces a novel knowledge-based collaborative ontological research model, which is specifically designed to tackle the challenges of developing collaborative products in the contemporary landscape. The model presents a significant and valuable contribution to the field by introducing an ontological approach for acquiring, representing and leveraging knowledge in a computer-interpretable format to support the design of collaborative products. In addition, it provides a comprehensive guide for evaluating the effectiveness and efficacy of the ontology developed.

As per the vision of promoting agricultural collectives, the government of India promoted the farmer producer organization (FPO). However, with the fast growth of FPOs, there is an issue with performance measurement. This study is aimed at the development of performance metrics for the FPOs.

In the first stage, we selected the measures from a secondary literature review and identified 11 parameters. Further, the Delphi round was conducted in the second stage with 26 experts working with FPOs and they were asked to rank these parameters. Based on the weightage of each parameter, the most important parameters were decided. The mean ranks and deviations of the performance parameters were analyzed. The hypothesis test and Kendall’s coefficient of concordance have been further used to validate the performance parameters. In the third stage, based on the inputs from the experts, a questionnaire was designed, and the data was collected from chief executive officers (CEOs) of the FPOs to identify the most important performance parameters.

The experts identified governance, financial support and professional management as important measures for FPOs. In the second round of the study, finance and governance were identified as the most important factors. It is important to note that finance and governance were the two most important factors in making an FPO successful. Finally, a 100-point metric was developed in seven major heads.

This study will be advantageous for all the stakeholders involved in the promotion of FPOs, including FPOs themselves, funding agencies providing funds to FPOs, skill-building organizations, etc.

This paper is one of its kind to develop a 100 points metrics for performance evaluation of FPOs.

Software defect prediction (SDP) is a critical aspect of software quality assurance, aiming to identify and manage potential defects in software systems. In this paper, we have proposed a novel hybrid approach that combines Grey Wolf Optimization with Feature Selection (GWOFS) and multilayer perceptron (MLP) for SDP. The GWOFS-MLP hybrid model is designed to optimize feature selection, ultimately enhancing the accuracy and efficiency of SDP. Grey Wolf Optimization, inspired by the social hierarchy and hunting behavior of grey wolves, is employed to select a subset of relevant features from an extensive pool of potential predictors. This study investigates the key challenges that traditional SDP approaches encounter and proposes promising solutions to overcome time complexity and the curse of the dimensionality reduction problem.

The integration of GWOFS and MLP results in a robust hybrid model that can adapt to diverse software datasets. This feature selection process harnesses the cooperative hunting behavior of wolves, allowing for the exploration of critical feature combinations. The selected features are then fed into an MLP, a powerful artificial neural network (ANN) known for its capability to learn intricate patterns within software metrics. MLP serves as the predictive engine, utilizing the curated feature set to model and classify software defects accurately.

The performance evaluation of the GWOFS-MLP hybrid model on a real-world software defect dataset demonstrates its effectiveness. The model achieves a remarkable training accuracy of 97.69% and a testing accuracy of 97.99%. Additionally, the receiver operating characteristic area under the curve (ROC-AUC) score of 0.89 highlights the model’s ability to discriminate between defective and defect-free software components.

Experimental implementations using machine learning-based techniques with feature reduction are conducted to validate the proposed solutions. The goal is to enhance SDP’s accuracy, relevance and efficiency, ultimately improving software quality assurance processes. The confusion matrix further illustrates the model’s performance, with only a small number of false positives and false negatives.

The paper aims to enhance Arabic machine translation (MT) by proposing novel approaches: (1) a dimensionality reduction technique for word embeddings tailored for Arabic text, optimizing efficiency while retaining semantic information; (2) a comprehensive comparison of meta-embedding techniques to improve translation quality; and (3) a method leveraging self-attention and Gated CNNs to capture token dependencies, including temporal and hierarchical features within sentences, and interactions between different embedding types. These approaches collectively aim to enhance translation quality by combining different embedding schemes and leveraging advanced modeling techniques.

Recent works on MT in general and Arabic MT in particular often pick one type of word embedding model. In this paper, we present a novel approach to enhance Arabic MT by addressing three key aspects. Firstly, we propose a new dimensionality reduction technique for word embeddings, specifically tailored for Arabic text. This technique optimizes the efficiency of embeddings while retaining their semantic information. Secondly, we conduct an extensive comparison of different meta-embedding techniques, exploring the combination of static and contextual embeddings. Through this analysis, we identify the most effective approach to improve translation quality. Lastly, we introduce a novel method that leverages self-attention and Gated convolutional neural networks (CNNs) to capture token dependencies, including temporal and hierarchical features within sentences, as well as interactions between different types of embeddings. Our experimental results demonstrate the effectiveness of our proposed approach in significantly enhancing Arabic MT performance. It outperforms baseline models with a BLEU score increase of 2 points and achieves superior results compared to state-of-the-art approaches, with an average improvement of 4.6 points across all evaluation metrics.

The proposed approaches significantly enhance Arabic MT performance. The dimensionality reduction technique improves the efficiency of word embeddings while preserving semantic information. Comprehensive comparison identifies effective meta-embedding techniques, with the contextualized dynamic meta-embeddings (CDME) model showcasing competitive results. Integration of Gated CNNs with the transformer model surpasses baseline performance, leveraging both architectures' strengths. Overall, these findings demonstrate substantial improvements in translation quality, with a BLEU score increase of 2 points and an average improvement of 4.6 points across all evaluation metrics, outperforming state-of-the-art approaches.

The paper’s originality lies in its departure from simply fine-tuning the transformer model for a specific task. Instead, it introduces modifications to the internal architecture of the transformer, integrating Gated CNNs to enhance translation performance. This departure from traditional fine-tuning approaches demonstrates a novel perspective on model enhancement, offering unique insights into improving translation quality without solely relying on pre-existing architectures. The originality in dimensionality reduction lies in the tailored approach for Arabic text. While dimensionality reduction techniques are not new, the paper introduces a specific method optimized for Arabic word embeddings. By employing independent component analysis (ICA) and a post-processing method, the paper effectively reduces the dimensionality of word embeddings while preserving semantic information which has not been investigated before especially for MT task.

This paper aims to explore factors contributing to music popularity using machine learning approaches.

A dataset comprising 204,853 songs from Spotify was used for analysis. The popularity of a song was predicted using predictive machine learning models, with the results showing the superiority of the random forest model across key performance metrics.

The analysis identifies crucial genre and audio features influencing music popularity. Additionally, genre specific analysis reveals that the impact of music features on music popularity varies across different genres.

The findings offer valuable insights for music artists, digital marketers and music platform researchers to understand and focus on the most impactful music features that drive the success of digital music, to devise more targeted marketing strategies and tactics based on popularity predictions, and more effectively capitalize on popular songs in this digital streaming age.

While previous research has explored different factors that may contribute to the popularity of music, this study makes a pioneering effort as the first to consider the intricate interplay between genre and audio features in predicting digital music popularity.

The target of this research is to develop a mathematical model which combines the Resource-Constrained Multi-Project Scheduling Problem (RCMPSP) and the Multi-Skilled Resource-Constrained Project Scheduling Problem (MSRCPSP). Due to the importance of resource management, the proposed formulation comprises resource leveling considerations as well. The model aims to simultaneously optimize: (1) the total time to accomplish all projects and (2) the total deviation of resource consumptions from the uniform utilization levels.

The K-Means (KM) and Fuzzy C-Means (FCM) clustering methods have been separately applied to discover the clusters of activities which have the most similar resource demands. The discovered clusters are given to the scheduling process as priori knowledge. Consequently, the execution times of the activities with the most common resource requests will not overlap. The intricacy of the problem led us to incorporate the KM and FCM techniques into a meta-heuristic called the Bi-objective Symbiosis Organisms Search (BSOS) algorithm so that the real-life samples of this problem could be solved. Therefore, two clustering-based algorithms, namely, the BSOS-KM and BSOS-FCM have been developed.

Comparisons between the BSOS-KM, BSOS-FCM and the BSOS method without any clustering approach show that the clustering techniques could enhance the optimization process. Another hybrid clustering-based methodology called the NSGA-II-SPE has been added to the comparisons to evaluate the developed resource leveling framework.

The practical importance of the model and the clustering-based algorithms have been demonstrated in planning several construction projects, where multiple water supply systems are concurrently constructed.

Reviewing the literature revealed that there was a need for a hybrid formulation that embraces the characteristics of the RCMPSP and MSRCPSP with resource leveling considerations. Moreover, the application of clustering algorithms as resource leveling techniques was not studied sufficiently in the literature.

This study aims to evaluates a public policy program that finances projects for the development of innovative technological solutions. This paper analyzed the influence of human and social capital on the development of the projects, under the perspective of the policy’s effectiveness and efficiency. This specific policy adopted the funding model of economic subsidy by means of grants, which shows the significant engagement of the public sector in applying nonrefundable resources more directly through loans, assuming the role of an entrepreneurial state, according to Mazzucato (2011, 2018) and Tavani and Zamparelli (2020).

This is a quantitative-descriptive study, according to Marconi and Lakatos (2017). This study is descriptive, for presenting information on innovation projects funded by FUNCAP (Ceará Foundation for Support to Scientific and Technological Development). In addition, this study is quantitative, by establishing multivariate relationships among the variables that relate to human capital and social capital, which are relevant to technological and innovative development, and by introducing variables on technological evolution, proposed as measures of the program’s effectiveness (DTRL, MkTRL) and efficiency (ETRL).

This paper sought to contribute on public policies for innovation, more specifically on analyzing variables that may affect the development of technological and innovative projects in knowledge-intensive companies. The authors studied capitals potentially important for these companies in the development of innovative projects. Specifically, the authors sought to understand the importance of human capital and how it reflects in technical and scientific knowledge of the project team and of social capital and how it reflects the connection and social relationship among different team members. The results presented that the degree of efficiency of the public funding program depends on how much the teams of the benefited projects have accumulated knowledge, skills and technical capacities – the so-called teams’ human capital.

It is important to address the research sample as a research limitation, which had 72 responses obtained, from a submission rate of 284. Another study limitation is on the qualitative analysis of the topics addressed from the companies and policymakers perspectives, considering that the quantitative nature of the study does not allow for a deeper understanding of the qualitative perspective of the actors involved in the phenomenon studied. As recommendations for future studies, it is suggested to conduct qualitative studies on the aspects studied here. In this sense, it is possible to conduct case studies for specific companies, or policymakers, to clarify and deepen the relationships between the themes addressed here.

As for the practical implications of the research, both for managers of public funding programs and for company managers, the benefits of human capital, related to innovative project development teams, are important in programs that deal with technological development projects. In practice, this means that the greater the human capital of academic background of the members of the supported project teams, the more efficient the projects are in the process of developing their technologies by using the resources provided (Ashford, 2000; Chen et al., 2008; Lerro et al., 2014).

Hence, the authors conclude that the evaluated innovation-funding program through grants achieved acceptable results in terms of promoting the technological evolution of the benefited projects and bringing the technologies closer to the market. Its efficiency was the least favorable result, showing that the program needs to focus on improving this specific aspect. Within the investigated program, the issue that needs enhancement (efficiency – ETRL) was the one that presented significant relationships with the human and social capital of the benefited projects’ teams. Thus, it is possible that, by selecting more projects that have teams with high capital, the efficiency of the public policy, in this case the development of projects with high technological and innovative potential, will be possibly reached.

The findings strengthen the need for innovation public policies designed and implemented in a systemic way in the science, technology and innovation ecosystem, to provide a technological infrastructure and human capital necessary for developing projects with high technological and innovative potential (Ergas, 1987; Audretsch and Link, 2012; Caloghirou et al., 2015; Edler and Fagerberg, 2017; Silvio et al., 2019).