Index-Building in a System of Interdependent Variables: The Penalty for Bottleneck

Author(s):  
Zoltan J. Acs ◽  
Gábor Rappai ◽  
László Szerb
Keyword(s):  
2019 ◽  
Vol 4 (2) ◽  
pp. 179-202 ◽  
Author(s):  
Chang Zhang ◽  
Ruiqin Wu

International competition over soft power has largely transformed from image promotion and cultural diplomacy to benchmark setting. Benchmarks breed discourses and discourses embody power. The article argues that the soft power index building has turned into a battlefield where different values, norms and development models struggle for legitimacy through quasi-scientific validations. By critically examining the methods employed by two soft power indexes, Portland Soft Power 30 Index and China National Image Global Survey, this article unpacks the mechanisms by which institutions from western and emerging (Brazil, Russia, India, China and South Africa (BRICS)) states embed political values, interests and agendas in the selection of data, indicators and treatments of data. The article finds that while the soft power indexes originating from Western organizations largely normalized liberal values and the current international hierarchy, the Chinese national image survey provides a more self-reflective approach to soft power measurement.


Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Bo Ding ◽  
Lei Tang ◽  
Yong-jun He

Recently, 3D model retrieval based on views has become a research hotspot. In this method, 3D models are represented as a collection of 2D projective views, which allows deep learning techniques to be used for 3D model classification and retrieval. However, current methods need improvements in both accuracy and efficiency. To solve these problems, we propose a new 3D model retrieval method, which includes index building and model retrieval. In the index building stage, 3D models in library are projected to generate a large number of views, and then representative views are selected and input into a well-learned convolutional neural network (CNN) to extract features. Next, the features are organized according to their labels to build indexes. In this stage, the views used for representing 3D models are reduced substantially on the premise of keeping enough information of 3D models. This method reduces the number of similarity matching by 87.8%. In retrieval, the 2D views of the input model are classified into a category with the CNN and voting algorithm, and then only the features of one category rather than all categories are chosen to perform similarity matching. In this way, the searching space for retrieval is reduced. In addition, the number of used views for retrieval is gradually increased. Once there is enough evidence to determine a 3D model, the retrieval process will be terminated ahead of time. The variable view matching method further reduces the number of similarity matching by 21.4%. Experiments on the rigid 3D model datasets ModelNet10 and ModelNet40 and the nonrigid 3D model dataset McGill10 show that the proposed method has achieved retrieval accuracy rates of 94%, 92%, and 100%, respectively.


1992 ◽  
Vol 21 (2) ◽  
pp. 103-103 ◽  
Author(s):  
Wayne Davison

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Sun-Young Ihm ◽  
Aziz Nasridinov ◽  
Young-Ho Park

A rapid development in wireless communication and radio frequency technology has enabled the Internet of Things (IoT) to enter every aspect of our life. However, as more and more sensors get connected to the Internet, they generate huge amounts of data. Thus, widespread deployment of IoT requires development of solutions for analyzing the potentially huge amounts of data they generate. A top-kquery processing can be applied to facilitate this task. The top-kqueries retrievektuples with the lowest or the highest scores among all of the tuples in the database. There are many methods to answer top-kqueries, where skyline methods are efficient when considering all attribute values of tuples. The representative skyline methods are soft-filter-skyline (SFS) algorithm, angle-based space partitioning (ABSP), and plane-project-parallel-skyline (PPPS). Among them, PPPS improves ABSP by partitioning data space into a number of spaces using hyperplane projection. However, PPPS has a high index building time in high-dimensional databases. In this paper, we propose a new skyline method (called Grid-PPPS) for efficiently handling top-kqueries in IoT applications. The proposed method first performs grid-based partitioning on data space and then partitions it once again using hyperplane projection. Experimental results show that our method improves the index building time compared to the existing state-of-the-art methods.


Author(s):  
Siobhan K. Yilmaz ◽  
Alok K. Bohara ◽  
Swati Thapa

Throughout the developing world, girls face hardships surrounding menstruation, often resulting in poor emotional wellbeing and missing school. Providing ways to keep girls in school will increase their educational and earning potentials, which will ultimately trickle down to improving the economic standing of nations in the next generation. Informed by the Transactional Model of Stress and Coping, this work evaluates the roles that cultural and school environments play in appraisals of menstruation as a major life stressor for adolescent females and the impacts of emotional stress on missing school. Using primary survey data from schools in Nepal, robust results are found to support the theoretical framework based on conditional mixed-process (CMP) estimation with fixed effects, utilizing multiple index building techniques. Strong cultural norms during menstruation appear to increase the probability of girls self-reporting emotional stress, while the presence of hygiene supporting infrastructure at schools reduces this outcome. Furthermore, there is strong support for the finding that the presence of emotional stress during menstruation increases the likelihood of not only missing school but also for an extended period of time. Our findings motivate increasing government policies to provide stronger hygiene infrastructure in schools to improve successful coping skills and attendance rates.


2018 ◽  
Vol 19 (0) ◽  
pp. 88-102
Author(s):  
Julián David Cortés-Sánchez

After more than half a century of armed conflict, Colombia is moving towards a post-conflict period. National and regional strategies aimed to strengthen institutional capacities, promote productive entrepreneurship and reduce organized violence and crime, are crucial lines of action for the alleviation of current (and future) grievances among ex-combatants, and Colombian society in general. This study presents an exploratory analysis on institutional strength, peacebuilding, and productive entrepreneurship in Colombia. Three composite indices based upon international assessments or seminal studies were developed, namely: Institutional Strength Index; Building Peace Index (based on the Negative Peace Index and Positive Peace Index); and Productive Entrepreneurship Index. The results showed a significant correlation between Institutional Strength Index and Productive Entrepreneurship Index. Population is the variable with the most significant correlation with productive entrepreneurship, employment, GDP, industrial sophistication, innovation, crime and certain types of violence (sexual and domestic).


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