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In order to solve the problem that the performance of the existing local feature descriptors in uncontrolled environment is greatly affected by illumination, background, occlusion and other factors, we propose a novel face recognition algorithm in uncontrolled environment which combines the block central symmetry local binary pattern (CS-LBP) and deep residual network (DRN) model.

The algorithm first extracts the block CSP-LBP features of the face image, then incorporates the extracted features into the DRN model, and gives the face recognition results by using a well-trained DRN model. The features obtained by the proposed algorithm have the characteristics of both local texture features and deep features that robust to illumination.

Compared with the direct usage of the original image, the usage of local texture features of the image as the input of DRN model significantly improves the computation efficiency. Experimental results on the face datasets of FERET, YALE-B and CMU-PIE have shown that the recognition rate of the proposed algorithm is significantly higher than that of other compared algorithms.

The proposed algorithm fundamentally solves the problem of face identity recognition in uncontrolled environment, and it is particularly robust to the change of illumination, which proves its superiority.

The face recognition problem has a long history and a significant practical perspective and one of the practical applications of the theory of pattern recognition, to automatically localize the face in the image and, if necessary, identify the person in the face. Interests in the procedures underlying the process of localization and individual’s recognition are quite significant in connection with the variety of their practical application in such areas as security systems, verification, forensic expertise, teleconferences, computer games, etc. This paper aims to recognize facial images efficiently. An averaged-feature based technique is proposed to reduce the dimensions of the multi-expression facial features. The classifier model is generated using a supervised learning algorithm called a back-propagation neural network (BPNN), implemented on a MatLab R2017. The recognition rate and accuracy of the proposed methodology is comparable with other methods such as the principle component analysis and linear discriminant analysis with the same data set. In total, 150 faces subjects are selected from the Olivetti Research Laboratory (ORL) data set, resulting 95.6 and 85 per cent recognition rate and accuracy, respectively, and 165 faces subjects from the Yale data set, resulting 95.5 and 84.4 per cent recognition rate and accuracy, respectively.

Averaged-feature based approach (dimension reduction) and BPNN (generate supervised classifier).

The recognition rate is 95.6 per cent and recognition accuracy is 85 per cent for the ORL data set, whereas the recognition rate is 95.5 per cent and recognition accuracy is 84.4 per cent for the Yale data set.

Averaged-feature based method.

The difficulties that MR poses for comparativists were anticipated 40 years ago in Sidney Verba's essay “Some Dilemmas of Comparative Research”, in which he called for a “disciplined configurative approach…based on general rules, but on complicated combinations of them” (Verba, 1967, p. 115). Charles Ragin's (1987) book The Comparative Method eloquently spelled out the mismatch between MR and causal explanation in comparative research. At the most basic level, like most other methods of multivariate statistical analysis MR works by rendering the cases invisible, treating them simply as the source of a set of empirical observations on dependent and independent variables. However, even when scholars embrace the analytical purpose of generalizing about relationships between variables, as opposed to dwelling on specific differences between entities with proper names, the cases of interest in comparative political economy are limited in number and occupy a bounded universe.² They are thus both knowable and manageable. Consequently, retaining named cases in the analysis is an efficient way of conveying information and letting readers evaluate it.³ Moreover, in practice most producers and consumers of comparative political economy are intrinsically interested in specific cases. Why not cater to this interest by keeping our cases visible?

Predictive analytics is the science of decision-making that eliminates guesswork out of the decision-making process and applies proven scientific procedures to find right solutions. Predictive analytics provides ideas on the occurrences of future downtimes and rejections thereby aids in taking preventive actions before abnormalities occur. Considering these advantages, predictive analytics is adopted in various diverse fields such as health care, finance, education, marketing, automotive, etc. Predictive analytics tools can be used to predict various behaviors and patterns, thereby saving the time and money of its users. Many open-source predictive analysis tools namely R, scikit-learn, Konstanz Information Miner (KNIME), Orange, RapidMiner, Waikato Environment for Knowledge Analysis (WEKA), etc. are freely available for the users. This chapter aims to reveal the best accurate tools and techniques for the classification task that aid in decision-making. Our experimental results show that no specific tool provides the best results in all scenarios; rather it depends upon the datasets and the classifier.

This paper aims to find the current situation of research data services by academic libraries and summarize some strategies for university libraries to reference. Recent years have seen an increasing number of university libraries extended their traditional roles and provided research data services.

This paper selected 87 libraries of the top 100 universities listed in the World’s Best Universities released by the USA News in October 2012 as samples and conducted a Web site investigation to check if there were any research data services provided. In addition, it made an interview with the Wuhan University Library’s Research Data Service Workgroup to understand the procedure, difficulties and experiences of their research data service. Based on the survey and interview, it analyzed the current status and difficulties of research data services in university libraries and proposed some strategies for others to reference.

Of the 87 university libraries investigated, 50 libraries have offered research data services. Most of the services can be divided into six aspects: research data introduction, data management guideline, data curation and storage service, data management training, data management reference and resource recommendation. Among these services, research data introduction is the most frequently provided (47.13 per cent), followed by data curation and storage services (43.68 per cent), data management guideline (42.53 per cent), data management reference (41.38 per cent), resource recommendation (41.38 per cent) and data management training (24.14 per cent). The difficulties met by research data service of Chinese academic libraries are also concluded.

Through Web site investigation and interview with the Wuhan University Library’s Research Data Service, this paper presented an overall picture of research data services in university libraries and identified the difficulties and experiences of research data services of the Wuhan University Library. Based on some successful examples, it put forward some strategies for university libraries to reference. This study is very useful for academic libraries to promote their research data services.

The DataArts dataset, although it covers mostly arts organizations, has emerged as an alternative source of data for nonprofit research. Most existing studies use the IRS 990 data, which is considered a reliable source for research. We evaluate the reliability of the DataArts dataset by comparing the consistency of the values reported to the DataArts Cultural Data Profile (CDP) and to the 990 forms. We: 1) examine correlations between the same measures in each dataset, 2) assess the cumulative distribution of differences between the two datasets and 3) compare the results of the same empirical model conducted with the DataArts dataset and 990 data, respectively. We conclude that the DataArts dataset is an adequate and reliable source of financial and performance information, but researchers should be aware of a few limitations.

To discuss some of the work of Heinz von Foerster with regard to multidimensional visualization.

An introduction to multidimensional visualization, followed with the connections derived from the Biological Computer Laboratory.

Visualization provides insight through images. Considers the steps involved in interacting and learning so that this will lead the individual into their own “concept formation”.

Studies aspects of Heinz von Foerster's work that are of importance for understanding multidimensional visualization.

During the 20th century, the United States rapidly developed its research capacity by fostering a broad base of institutions of higher education led by a small core of highly productive research universities. By the latter half of the century, scientists in a greatly expanded number of universities across the United States published the largest annual number of scholarly publications in STEM+ fields from one nation. This expansion was not a product of some science and higher education centralized plan, rather it flowed from the rise of mass tertiary education in this nation. Despite this unprecedented productivity, some scholars suggested that universities would cease to lead American scientific research. This chapter investigates the ways that the United States’ system of higher education underpinned American science into the 21st century.

The authors present a historical and sociological case study of the development of the United States’ system of higher education and its associated research capacity. The historical and sociological context informs our analysis of data from the SPHERE team dataset, which was compiled from the Thomson Reuters’ Science Citation Index Expanded (SCIE) database.

We argue that American research capacity is a function of the United States’ broad base of thousands of public and broadly accessible institutions of higher education plus its smaller, elite sector of “super” research universities; and that the former serve to culturally support the later. Unlike previous research, we find that American higher education is not decreasing its contributions to the nation’s production of STEM+ scholarship.

The chapter provides empirical analyses, which support previous sociological theory about mass higher education and super research universities.

For small businesses, the strategic objective of going green may be a gendered process. Male and female entrepreneurs, due to their gender roles, respond differently to intrinsic motivations and extrinsic pressures to go green. This study aims to investigate whether women-run or men-run firms are more likely to go green due to intrinsic motivations versus extrinsic pressures. Moreover, it examines how the effect of gender on going green is moderated by market competition and gender inequality.

This study employs a dataset of small businesses in 40 countries, mostly developing, in Eastern Europe, Western Asia and Northern Africa.

Women-run firms are more likely to go green due to both intrinsic motivations and extrinsic pressures compared to men-run firms. Notably, market competition weakens the positive effect of female ownership on firm going green while gender inequality amplifies the relationship.

This research is one of the first to examine the gendered process of going green in small businesses. Using the social feminist and institutional theories to understand how male and female entrepreneurs go green for different types of motivations, this research expands understanding of the green transition of small businesses.

In large parts of the world, income inequality has been rising in recent decades. Other regions have experienced declining trends in income inequality. This raises the question of which mechanisms underlie contrasting observed trends in income inequality around the globe. To address this research question in an empirical analysis at the aggregate level, we examine a global sample of 73 countries between 1981 and 2010, studying a broad set of drivers to investigate their interaction and influence on income inequality. Within this broad approach, we are interested in the heterogeneity of income inequality determinants across world regions and along the income distribution. Our findings indicate the existence of a small set of systematic drivers across the global sample of countries. Declining labour income shares and increasing imports from high-income countries significantly contribute to increasing income inequality, while taxation and imports from low-income countries exert countervailing effects. Our study reveals the region-specific impacts of technological change, financial globalisation, domestic financial deepening and public social spending. Most importantly, we do not find systematic evidence of education’s equalising effect across high- and low-income countries. Our results are largely robust to changing the underlying sources of income Ginis, but looking at different segments of income distribution reveals heterogeneous effects.