Search results

Experiments are the backbone of the development process of data-driven predictive models for scientific applications. The quality of the experiments directly impacts the model performance. Uncertainty inherently affects experiment measurements and is often missing in the available data sets due to its estimation cost. For similar reasons, experiments are very few compared to other data sources. Discarding experiments based on the missing uncertainty values would preclude the development of predictive models. Data profiling techniques are fundamental to assess data quality, but some data quality dimensions are challenging to evaluate without knowing the uncertainty. In this context, this paper aims to predict the missing uncertainty of the experiments.

This work presents a methodology to forecast the experiments’ missing uncertainty, given a data set and its ontological description. The approach is based on knowledge graph embeddings and leverages the task of link prediction over a knowledge graph representation of the experiments database. The validity of the methodology is first tested in multiple conditions using synthetic data and then applied to a large data set of experiments in the chemical kinetic domain as a case study.

The analysis results of different test case scenarios suggest that knowledge graph embedding can be used to predict the missing uncertainty of the experiments when there is a hidden relationship between the experiment metadata and the uncertainty values. The link prediction task is also resilient to random noise in the relationship. The knowledge graph embedding outperforms the baseline results if the uncertainty depends upon multiple metadata.

The employment of knowledge graph embedding to predict the missing experimental uncertainty is a novel alternative to the current and more costly techniques in the literature. Such contribution permits a better data quality profiling of scientific repositories and improves the development process of data-driven models based on scientific experiments.

GraphQL is a new Open API specification that allows clients to send queries and obtain data flexibly according to their needs. However, a high-complexity GraphQL query may lead to an excessive data volume of the query result, which causes problems such as resource overload of the API server. Therefore, this paper aims to address this issue by predicting the response data volume of a GraphQL query statement.

This paper proposes a GraphQL response data volume prediction approach based on Code2Vec and AutoML. First, a GraphQL query statement is transformed into a path collection of an abstract syntax tree based on the idea of Code2Vec, and then the query is aggregated into a vector with the fixed length. Finally, the response result data volume is predicted by a fully connected neural network. To further improve the prediction accuracy, the prediction results of embedded features are combined with the field features and summary features of the query statement to predict the final response data volume by the AutoML model.

Experiments on two public GraphQL API data sets, GitHub and Yelp, show that the accuracy of the proposed approach is 15.85% and 50.31% higher than existing GraphQL response volume prediction approaches based on machine learning techniques, respectively.

This paper proposes an approach that combines Code2Vec and AutoML for GraphQL query response data volume prediction with higher accuracy.

When a large number of project proposals are evaluated to allocate available funds, grouping them based on their similarities is beneficial. Current approaches to group proposals are primarily based on manual matching of similar topics, discipline areas and keywords declared by project applicants. When the number of proposals increases, this task becomes complex and requires excessive time. This paper aims to demonstrate how to effectively use the rich information in the titles and abstracts of Turkish project proposals to group them automatically.

This study proposes a model that effectively groups Turkish project proposals by combining word embedding, clustering and classification techniques. The proposed model uses FastText, BERT and term frequency/inverse document frequency (TF/IDF) word-embedding techniques to extract terms from the titles and abstracts of project proposals in Turkish. The extracted terms were grouped using both the clustering and classification techniques. Natural groups contained within the corpus were discovered using k-means, k-means++, k-medoids and agglomerative clustering algorithms. Additionally, this study employs classification approaches to predict the target class for each document in the corpus. To classify project proposals, various classifiers, including k-nearest neighbors (KNN), support vector machines (SVM), artificial neural networks (ANN), classification and regression trees (CART) and random forest (RF), are used. Empirical experiments were conducted to validate the effectiveness of the proposed method by using real data from the Istanbul Development Agency.

The results show that the generated word embeddings can effectively represent proposal texts as vectors, and can be used as inputs for clustering or classification algorithms. Using clustering algorithms, the document corpus is divided into five groups. In addition, the results demonstrate that the proposals can easily be categorized into predefined categories using classification algorithms. SVM-Linear achieved the highest prediction accuracy (89.2%) with the FastText word embedding method. A comparison of manual grouping with automatic classification and clustering results revealed that both classification and clustering techniques have a high success rate.

The proposed model automatically benefits from the rich information in project proposals and significantly reduces numerous time-consuming tasks that managers must perform manually. Thus, it eliminates the drawbacks of the current manual methods and yields significantly more accurate results. In the future, additional experiments should be conducted to validate the proposed method using data from other funding organizations.

This study presents the application of word embedding methods to effectively use the rich information in the titles and abstracts of Turkish project proposals. Existing research studies focus on the automatic grouping of proposals; traditional frequency-based word embedding methods are used for feature extraction methods to represent project proposals. Unlike previous research, this study employs two outperforming neural network-based textual feature extraction techniques to obtain terms representing the proposals: BERT as a contextual word embedding method and FastText as a static word embedding method. Moreover, to the best of our knowledge, there has been no research conducted on the grouping of project proposals in Turkish.

The main purpose of this study was to examine the effectiveness of embedded social media channels and determine whether the embedded social media channels enhance the overall experience of travelers using the hotel Web sites.

A true-experimental, between-group and post-test-only design was used to address the primary research questions. Two privately accessible complete versions of the Web site (one with embedded social media channels and one without them) were designed for the experiment. The uses and gratifications approach was used to test the proposed hypotheses. Data were analyzed using ANOVA.

The results of this study revealed that embedded social media channels on the hotel Web site enhanced travelers’ social gratifications of perceived social interaction. Apart from these benefits for travelers seeking social gratifications, embedded social media channels did not enhance the overall experience (content and process gratifications) of travelers using the Web site.

In the case of embedded social media on hotel Web sites, this study suggests that hotel managers measure return on engagement to examine the effectiveness of embedded social media, instead of return on investment.

The study revealed that the emergence of embedded social media channels and their integration on hotel Web sites will have significant influence on travelers who seek social gratifications.

The findings of this study offer new empirical evidence that embedded social media channels enhance only travelers’ perceived social interaction during their first visit to the hotel Web site.

In heterogeneously segmented markets, collaborating with product users in product innovation is important for business success. End user innovators and embedded user innovators differ in terms of their prior embeddedness in the target industry. The purpose of this study is twofold. First, the authors empirically compare these two types of user innovators in terms of their diffusion channel selection. Second, the authors analyze how the technological advances of their innovations affect this difference.

Using an online questionnaire survey, this study collected a sample of 237 user-generated innovations in Japan and analyzed several hypotheses using quantitative statistical approaches.

The analysis shows that embedded user innovators are more likely than end user innovators to transfer their innovations to producers rather than peers. As the technological advances of their innovations increase, end user innovators' likelihood of transferring their innovation to producers increases more significantly than that of embedded user innovators.

This is the first paper to investigate the difference between end user innovators and embedded user innovators with respect to their diffusion channel selection as well as the moderating role of technological advances. The findings bring new perspectives to the domains of user–producer collaboration and technology transfer.

The purpose of this paper is to develop an innovative information hiding algorithm.

The proposed algorithm is based on image histogram statistics. Cumulative‐peak histogram regions are utilized to hide multiple bits of the secret message by performing histogram bin substitution. The embedding capacity, otherwise known as payload, and peak signal to noise ratio (PSNR), as well as security, are the main metrics used to evaluate the performance of the proposed algorithm.

According to the obtained results, the proposed algorithm shows high embedding capacity and security at comparable PSNR compared with existing hiding information techniques.

The simplicity, security, random distribution of embedding pixels, and on‐demand high capacity are the key advantages of the proposed approach.

The purpose of this paper is to develop a coherent and robust methodology for social impact measurement of social enterprises (SEs) that would provide the conceptual and practical bases for training and embedding.

The paper presents a holistic impact measurement model for SEs, called social impact for local economies (SIMPLEs). The SIMPLE impact model and methodology have been tried and tested on over 40 SEs through a series of three day training courses, and a smaller number of test cases for embedding. The impact model offers a five‐step approach to impact measurement called SCOPE IT; MAP IT; TRACK IT; TELL IT and EMBED IT. These steps help SE managers to conceptualise the impact problem; identify and prioritise impacts for measurement; develop appropriate impact measures; report impacts and embed the results in management decision making.

Preliminary qualitative feedback from participants reveals that while the SIMPLE impact training delivers positive learning experiences on impact measurement and prompts, in the majority of cases, the intensions to implement impact systems, the majority feels the need for follow up embedding support. Paricipant's see value in adopting the SIMPLE approach to support business planning processes. Feedback from two SEs which has receives in‐house facilitates embedding support clearly demonstrates the benefits of working closely with an organisation's staff team to enable effective implementation.

Some key future research challenges are identified as follows: systematically research progress in implementation after training for those participants that do not have facilitated embedding; to further test and develop embedding processes and models (using SIMPLE and other methods) with more SE organisations to identify best practices.

The SIMPLE fills a gap as a tool for holistic impact thinking that offers try and test accessible steps, with robust measures. The innovative steps take SEs through all key impact thought processes from conceptualisation to embedding guidance, feeding into business planning and strategic decision‐making processes. The comparison between the limitations of stand alone impact training and the benefits of facilitated embedding processes is instructive.

This paper aims to explore the benefits of embedding a records manager into a team of university administrators to help them address their information management needs.

The paper describes an experience that was inspired by reports of successful experiences with embedded librarianship. The literature on records management culture and embedded librarianship is reviewed to identify best practices and criteria for success. These criteria are used to design and implement a pilot project where, rather than hiring a consultant, a records manager is embedded into a quality assurance team working at a large university in Canada.

The project is a success in conventional terms (e.g. active files reduced; duplicates deleted; inactive files archived; naming conventions, version control and access rights applied); however, similar results could have been achieved using a consultant. More interesting are the added benefits achieved through embedding. Added benefits included identifying workflow inefficiencies, identifying terminological inconsistencies, iterative training opportunities and useful knowledge sharing outside the project’s scope. The argument is made that an embedded information professional is better able to appreciate the organizational culture, which in turn facilitates the establishment of trusted relationships and produces an overall added value for the entire team.

There is very little, if any, current literature that explores the value of embedding a records manager into a team, rather than simply hiring a consultant to address information management needs. The outcome of this pilot project will benefit those who are seeking to develop a model for embedding an information professional into their organization to gain an added value.

The purpose of this paper is to investigate the availability of embedded metadata within images of digital cultural collections. It is designed to examine a proposed hypothesis that most digitally derived images of cultural resources are stripped of their metadata once they are placed on the web.

A sample of 603 images were selected randomly from four cultural portals which aggregate digitized cultural collections, then four steps in the data collection process took place to examine image metadata via the web-based tool and windows application.

The study revealed that 28.5 percent of the analyzed images contained metadata, no links exist between image embedded metadata and its metadata record or the pages of the websites analyzed, and there is a significant usage of Extensible Metadata Platform to encode embedded metadata within the images.

The findings of the study may encourage heritage digital collection providers to reconsider their metadata preservation practices and policies to enrich the content of embedded metadata. In addition, it will raise awareness about the potential and value of embedded metadata in enhancing the findability and exchange of digital collections.

This study is ground breaking in that it is one of the early studies, especially in the Arab world, which aim to recognize the use of image embedded metadata within cultural heritage digital collections on the web.

This article aims to explore the character of an emerging model of corporate social responsibility (CSR)‐oriented societal governance in an exchange theoretical perspective and to examine the distinctive characteristics of the relations between civil society, business and government in the new model and the drivers behind it.

By analyzing typical roles and role‐sets in political, commercial and regulatory exchange, the article pin‐points characteristics of the embedded relational governance/CSR model contrasted against liberal governance and the Keynesian welfare state. The analysis is stylized and conceptually based, in line with the Weberian ideal type concept and brings out stylized juxtapositions of the three governance models based on previous studies.

An emerging model of corporate social responsibility (CSR) or embedded relational governance seems to share the basic market orientation of the liberal model, yet, at the same time, sharing many of the social and collective goals of the welfare state. This combination is apparently achieved by embedding the social dimension into civil society and self‐regulatory market processes. Finally, the paper reflects on the drivers behind the new governance approach, in the context of a globalizing economy. The paper argues that NGO‐driven communicative intermediation interfacing with an increasing CSR and corporate governance focus in financial evaluation may serve to retain some of the social agenda from the welfare state, under the CSR‐ or embedded‐relational model, an agenda that seemed to be gradually losing out with the global competitive exposure of the welfare state.

The article presents a stylized analytical framework of CSR/embedded relational governance that lays a basis for further exploration and systematic testing through comparative empirical studies.

The paper brings out the interplay between political, regulatory and commercial processes and gives a broader understanding of the societal implications of CSR.

Original contributions of this paper: first, the analytical formulation of the societal governance implications of CSR; second, the exchange theoretical conceptualization of this mode of societal governance.