GENDER CLASSIFICATION VIA HUMAN JOINTS USING CONVOLUTIONAL NEURAL NETWORK

With the growing demand for gender-related data on diverse applications, including security systems for ascertaining an individual’s identity for border crossing, as well as marketing purposes of digging the potential customer and tailoring special discounts for them, gender classification has become an essential task within the field of computer vision and deep learning. There has been extensive research conducted on classifying human gender using facial expression, exterior appearance (e.g., hair, clothes), or gait movement. However, within the scope of our research, none have specifically focused gender classification on two-dimensional body joints. Knowing this, we believe that a new prediction pipeline is required to improve the accuracy of gender classification on purely joint images. In this paper, we propose novel yet simple methods for gender recognition. We conducted our experiments on the BBC Pose and Short BBC pose datasets. We preprocess the raw images by filtering out the frame with missing human figures, removing background noise by cropping the images and labeling the joints via the C5 (model applied transfer learning on the RestNet-152) pre- trained model. We implemented both machine learning (SVM) and deep learning (Convolution Neural Network) methods to classify the images into binary genders. The result of the deep learning method outperformed the classic machine learning method with an accuracy of 66.5%

Using Business Intelligence to Improve DBA Productivity

The amount of data collected and used by companies has grown rapidly in size over the last decade. Business leaders are now using Business Intelligence (BI) systems to make effective business decisions against large amounts of data. The growth in the size of data has been a major challenge for Database Administrators (DBAs). The increase in the number and size of databases at the speed they have grown has made it difficult for DBA teams to provide the same level of service that the business requires they provide. The methods that DBAs have used in the last several decades can no longer be performed with the efficiency needed over all of the databases they administer. This paper presents the first BI system to improve DBA productivity and providing important data metrics for Information Technology (IT) managers. The BI system has been well received by Sherwin Williams Database Administrators. It has i) enabled the DBA team to quickly determine which databases needed work by a DBA without manually logging into the system; ii) helped the DBA team and its management to easily answer other business users' questions without using DBAs' time to research the issue; and iii) helped the DBA team to provide the business data for unanticipated audit request

Integrated stakeholder analysis for effective urban flood management in a medium-sized city in China: a case study of Zhuji, Zhejiang province

Over recent decades, the stakeholder arena for urban flood management has become well recognised as being complex and dynamic. Various stakeholders are involved before, during and after a flooding event, all of which have different interests and demands. Therefore, an initial stakeholder identification and analysis stage is required before detailed stakeholder engagement strategies can be developed and employed. Drawing on urban flood management in Zhuji, a typical medium-sized city that has suffered urban flooding in China, this research project used a mixed-method research methodology within a single case-study approach to explore the current stakeholder arena for urban flood management in a medium-sized Chinese city. By combining stakeholder salience analysis with social network analysis, this study tries to create a more nuanced insight into the stakeholder arena, so that stakeholder participation in urban flood management can be improved. This thesis produces several findings. First, it provides empirical evidence to show that traditional one-dimensional stakeholder analysis methods such as the level of interest and influence; cooperation and competition; cooperation and threat; and stakeholder interest and power cannot provide an in-depth understanding of a complex and dynamic stakeholder arena, as exists for urban flood management. By way of contrast, the proposed stakeholder analysis approach, which combines both stakeholder salience and network analyses, can create a multi-dimensional understanding of urban flood management stakeholders and allows the initial problem space to be recast into a more detailed or nuanced understanding of the problems presented. This improved understanding of the stakeholder arena and the related problem space provides a more solid information foundation upon which new stakeholder and community engagement practices can be developed. Second, this thesis argues that the Mitchell et al. (1997) salience model experiences limitations in practice. Only five of the seven salience groups were identified in the present research project, with both the Dangerous and Demanding stakeholder groups missing. This indicates that the identification of urban flood management stakeholders in a medium-sized Chinese city is highly dependent on their legitimate claims. Third, the social network analysis used in this project not only explores the relationships between stakeholders, but also provides an opportunity to present other one-dimensional stakeholder attitudes. This enhancement of the data beyond one-dimensional visual representations to dynamic and interactive processes not only better assists policy-makers in developing new and improved engagement practices, it also allows engagement practitioners to educate stakeholders and interactively improve understanding of the situation among those stakeholders. This understanding, in turn, is assumed to facilitate collaborative problem solving