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Expert profiling plays an important role in expert finding for collaborative innovation in research social networking platforms. Dynamic changes in scientific knowledge have posed significant challenges on expert profiling. Current approaches mostly rely on knowledge of other experts, contents of static web pages or their behavior and thus overlook the insight of big social data generated through crowdsourcing in research social networks and scientific data sources. In light of this deficiency, this research proposes a big data-based approach that harnesses collective intelligence of crowd in (research) social networking platforms and scientific databases for expert profiling.

A big data analytics approach which uses crowdsourcing is designed and developed for expert profiling. The proposed approach interconnects big data sources covering publication data, project data and data from social networks (i.e. posts, updates and endorsements collected through the crowdsourcing). Large volume of structured data representing scientific knowledge is available in Web of Science, Scopus, CNKI and ACM digital library; they are considered as publication data in this research context. Project data are located at the databases hosted by funding agencies. The authors follow the Map-Reduce strategy to extract real-time data from all these sources. Two main steps, features mining and profile consolidation (the details of which are outlined in the manuscript), are followed to generate comprehensive user profiles. The major tasks included in features mining are processing of big data sources to extract representational features of profiles, entity-profile generation and social-profile generation through crowd-opinion mining. At the profile consolidation, two profiles, namely, entity-profile and social-profile, are conflated.

(1) The integration of crowdsourcing techniques with big research data analytics has improved high graded relevance of the constructed profiles. (2) A system to construct experts’ profiles based on proposed methods has been incorporated into an operational system called ScholarMate (www.scholarmate.com).

One shortcoming is currently we have conducted experiments using sampling strategy. In the future we will perform controlled experiments of large scale and field tests to validate and comprehensively evaluate our design artifacts.

The business implication of this research work is that the developed methods and the system can be applied to streamline human capital management in organizations.

The proposed approach interconnects opinions of crowds on one’s expertise with corresponding expertise demonstrated in scientific knowledge bases to construct comprehensive profiles. This is a novel approach which alleviates problems associated with existing methods. The authors’ team has developed an expert profiling system operational in ScholarMate research social network (www.scholarmate.com), which is a professional research social network that connects people to research with the aim of “innovating smarter” and was launched in 2007.

Patent trade recommendations necessitate recommendation interpretability in addition to recommendation accuracy because of patent transaction risks and the technological complexity of patents. This study designs an interpretable knowledge-aware patent recommendation model (IKPRM) for patent trading. IKPRM first creates a patent knowledge graph (PKG) for patent trade recommendations and then leverages paths in the PKG to achieve recommendation interpretability.

First, we construct a PKG to integrate online company behaviors and patent information using natural language processing techniques. Second, a bidirectional long short-term memory network (BiLSTM) is utilized with an attention mechanism to establish the connecting paths of a company — patent pair in PKG. Finally, the prediction score of a company — patent pair is calculated by assigning different weights to their connecting paths. The semantic relationships in connecting paths help explain why a candidate patent is recommended.

Experiments on a real dataset from a patent trading platform verify that IKPRM significantly outperforms baseline methods in terms of hit ratio and normalized discounted cumulative gain (nDCG). The analysis of an online user study verified the interpretability of our recommendations.

A meta-path-based recommendation can achieve certain explainability but suffers from low flexibility when reasoning on heterogeneous information. To bridge this gap, we propose the IKPRM to explain the full paths in the knowledge graph. IKPRM demonstrates good performance and transparency and is a solid foundation for integrating interpretable artificial intelligence into complex tasks such as intelligent recommendations.

The uncertainty of the interval variable is represented by interval factor, and the interval variable is described as its mean value multiplied by its interval factor. Based on interval arithmetic rules, an analytical method of interval finite element for uncertain structures but not probabilistic structure or fuzzy structure is presented by combining the interval analysis with finite element method. The static analysis of truss with interval parameters under interval load is studied and the expressions of structural interval displacement response and stress response are deduced. The influences of uncertainty of one of structural parameters or load on the displacement and stress of the structure are examined through examples and some significant conclusions are obtained.

This chapter explores the protest self-immolations since 2009 of over 100 Tibetans in China. It investigates whether these events have ecological as well as social causes and may thus be relevant to the emerging discipline of ‘EcoHealth’.

Targeted literature review and reflective analysis, presented as a narrative.

Chinese citizens identifying as Tibetan have experienced substantial ethnically based discrimination for over 60 years, manifest as attempted cultural destruction, pervasive disrespect and linguistic suppression. Tibetans, now a minority in much of their former territory, have witnessed and at times been forced to participate in ecological destruction, much of it led by Chinese settlers, endorsed by occupying authorities. Tibetans have for decades protested against the Chinese they regard as invaders and occupiers, but Tibetan acts of protest self-immolation are a recent response. Academic analysis has been scarce, particularly by Chinese scholars. Until now, EcoHealth practitioners have also denied any relevance, as if in a waltz led by the Chinese government.

Attempts to identify rational causes for Tibetan self-immolation conflict with themes of liberation and fairness central to Communist Chinese ideology. Most Chinese analysis of Tibetan self-immolation is superficial, nationalistic and unsympathetic. Also disturbing is the reaction to these issues shown by the International Association of Ecology and Health. It is suggested that this illustrates a failure to translate rhetoric of ‘speaking truth to power’ to reality, a retreat from idealism common to many social movements.

Increasing human demand on a limited biosphere necessitates a deepened understanding of eco-social factors. Practitioners concerned with sustaining our civilisation are encouraged to explore the integrated dimensions revealed by this case study.

This paper examines diverging views on the Chongqing model, the policy experiment led by Bo Xilai from 2007 to 2012 that was famous for its “red songs” and the campaign against organized crime. It has impressed both the supporters of socialist identity of China and the supporters of liberal identity and led to an intense debate concerning China’s path of development. This paper attempts to discuss and clarify to what extent the Chongqing model represented a genuine socialist experiment and the implications of the model for China’s future.

With the analysis of the causes of corruption, this study aims to investigate specific anti-corruption measures that can be implemented to reform the political system and the social climate of China.

This study examines 97 severe corruption cases of high-ranking officials in China, which occurred between 2012 and 2015. As this insinuates that both institutional and social corruption are major problems in China, the analysis delves into multiple facts of corruption, including different types, four primary underlying causes, and suggestions regarding the implementation of three significant governmental shifts that focus on investigation, prevention tactics and legal regulations.

China’s corruption is not only individual-based but also it has developed into institutional corruption and social corruption. Besides human nature and instinct, the causes of corruption can be organised into four categories, namely, social customs, social transitions, institutional designs and institutional operations. For the removed high-ranking officials, the formation of interest chains was an important underlying cause behind their corruption.

This study makes a significant contribution to the literature because this study provides a well-rounded approach to a complex issue by highlighting the significance of democracy and the rule of law as ways to regulate human behaviour to combat future corruption.

This paper aims to make a call for the establishment of a new research journal: a likely title for which would be Chinese Public Management. The background to this is clearly set out here for posterity’s sake. The review of selected papers unveils an emerging trend amongst Chinese researchers for undertaking deeper, cluster-based analyses. See, for example, the insights presented in this issue concerning competitiveness in China’s automobile industry.

A diary-like account has been made of the series of recent events that sparked the author’s interest in creating a journal to be known as Chinese Public Management. Why is there currently this focus on empirical research for public policy? From the author’s five years’ work for a serial visiting professorship across China, he has found that there are now well-established, substantial databases dedicated to the subject. Even more importantly – as this paper illustrates – a growing community of scholars has become keen to embark upon an in-depth, quantitative research. Perhaps, for the new journal, we would need an editor concentrating specifically on databases. Furthermore, undertaking scholarly work that is still of practical relevance for guiding authorities in their formulations of public policy will add a whole new dimension to the available research.

There is scope for a new endeavor that documents management research within the public sector in China. This may be seen as a sister journal for “Chinese Management Studies” that focuses on the other, much larger Chinese sector, that is, governmental organizations.

This paper documents the emergence of the necessity for a new journal about management in China.

Credit risk assessment has gained importance in recent years due to global financial crisis and credit crunch. Financial institutions therefore seek the support of credit rating agencies to predict the ability of creditors to meet financial persuasions. The purpose of this paper is to construct neural network (NN) and fuzzy support vector machine (FSVM) classifiers to discriminate good creditors from bad ones and identify a best classifier for credit risk assessment.

This study uses artificial neural network, the most popular AI technique used in the field of financial applications for classification and prediction and the new machine learning classification algorithm, FSVM to differentiate good creditors from bad. As membership value on data points influence the classification problem, this paper presents the new FSVM model. The instances membership is computed using fuzzy c-means by evolving a new membership. The FSVM model is also tested on different kernels and compared and the classifier with highest classification accuracy for a kernel is identified.

The paper identifies a standard AI model by comparing the performances of the NN model and FSVM model for a credit risk data set. This work proves that that FSVM model performs better than back propagation-neural network.

The proposed model can be used by financial institutions to accurately assess the credit risk pattern of customers and make better decisions.

This paper has developed a new membership for data points and has proposed a new FCM-based FSVM model for more accurate predictions.