Faces of Time: Developing Protocol for the Crowdsourced Annotation of Time Magazine Images

This project served to aid with the development of a research project that aims to analyze the progression of the appearance and context of women’s faces in Time Magazine images from 1960 through 1990. This will be accomplished by using Mechanical Turk to employ crowdsourced labor to extract every image of a face from select issues of Time magazine over the time period of interest. Mechanical Turk will also be used to label the following characteristics of each face: photo/drawing, gaze direction, context (ad/feature/cover), color/monochrome, ethnicity, age, gender, presence of a smile, and image quality. My work focused on the developing the labeling system and exploring the consistency of labelers. Through processing multiple issues from the corpus, I found potentially problematic issues that had not yet been considered such as the presence of masked individuals, images with an overwhelming number of miniature faces, and photos of young children with ambiguous gender and ethnicity. The results of my exploration led to the creation of new categories (e.g. adult versus child) and the establishment of specific criteria to be provided to potential raters to reduce ambiguity in the task. These criteria were included in an instruction set for MTurkers that I created. In addition, I developed an R-script that used Cohen\u27s kappa to analyze interrater reliability across all categories to view the consistency of labelers. The data used was of 3 university students; who individually processed images from the same issue and labeled the faces according to the established protocol. The overall average Cohen’s kappa was .740. The Cohen’s kappa was above .723 for all variables except image quality. Kappa for image quality was .362.  Lastly, I aided in the development of internal checks within the Mechanical Turk system to identify instances of labelers potentially manipulating the system for financial gain

Crowdsourcing Image Extraction and Annotation: Software Development and Case Study

We describe the development of web-based software that facilitates large-scale, crowdsourced image extraction and annotation within image-heavy corpora that are of interest to the digital humanities. An application of this software is then detailed and evaluated through a case study where it was deployed within Amazon Mechanical Turk to extract and annotate faces from the archives of Time magazine. Annotation labels included categories such as age, gender, and race that were subsequently used to train machine learning models. The systemization of our crowdsourced data collection and worker quality verification procedures are detailed within this case study. We outline a data verification methodology that used validation images and required only two annotations per image to produce high-fidelity data that has comparable results to methods using five annotations per image. Finally, we provide instructions for customizing our software to meet the needs for other studies, with the goal of offering this resource to researchers undertaking the analysis of objects within other image-heavy archives

Improvement in Delivery Times Using Lean Manufacturing Tools in a SME the Beverage Sector in Peru

The non-alcoholic beverage industry, such as bottled water, is one of the largest industries in which the process is carried out at the lowest cost, but with the highest quality in the final product. This sector has a significant impact on the world economy, and consumption per person is constantly growing. This research focuses on the improvement of delivery times through Lean Manufacturing tools. The model makes use of tools such as 5S' to create and maintain a more efficient and productive space, improve overall equipment efficiency through Total Productive Maintenance, and optimize material and operator movements by eliminating unnecessary ones using Standard Work, from that were positive indicators for management. For the validation of our proposal, an integrating model of the pilot plans was carried out in order to corroborate the efficiency of the proposed tools using the Arena software. By validating the proposed model, it was possible to reduce the rate of products delivered out of time by 37.82%, increase the OEE of the machine by 16% and reduce cycle times