Markup Languages, Systems and Middleware

Social Networks II

Smart Cities

Recommender Systems I

Recommender Systems II

Social Networks I

Wearable Technologies: a Roadmap to the Future

Video annotation is an activity that aims to supplement this type of multimedia object with additional content or information about its context, nature, content, quality and other aspects. These annotations are the basis for building a variety of multimedia applications for various purposes ranging from entertainment to security. There are automatic methods for video annotation. However, these methods require specific conditions and features that are not even found in actual application scenarios. Manual annotation is a strategy that uses the intelligence and workforce of people in the annotation process and is an alternative to cases where automatic methods cannot be applied. However, manual video annotation can be a costly process because as the content to be annotated increases, so does the workload for annotating. Crowdsourcing appears as a viable solution strategy in this context because it relies on outsourcing the tasks to a multitude of workers, who perform specific parts of the work in a distributed way. However, as the complexity of required media annoyances increases, it becomes necessary to employ skilled labor, or willing to perform larger, more complicated, and more time-consuming tasks. This makes it challenging to use crowdsourcing, as experts demand higher pay, and recruiting tends to be a difficult activity. In order to overcome this problem, strategies based on the decomposition of the main problem into a set of simpler subtasks suitable for crowdsourcing processes have emerged. These smaller tasks are organized in a workflow so that the execution process can be formalized and controlled. In the literature, there are some different proposals for the construction of this type of workflow, but each of them presents limitations that still have to be overcome. In this sense, this thesis aims to present a new framework that allows the use of crowdsourcing to create applications that require complex video annotation tasks. The developed framework considers the whole process from the definition of the problem and the decomposition of the tasks, until the construction, execution, and management of the workflow. This framework, called CrowdWaterfall, contemplates the strengths of current proposals, incorporating new concepts, techniques, and resources to overcome some of its limitations.

Integrating Crowdsourcing and Human Computation forComplex Video Annotation Tasks

A major concern when building Recommender Systems (RSs) for recruitment domains is ensuring actual matching opportunities for all candidates and job vacancies on the system as fast as possible. Indeed, long periods waiting for matchings imply losses of business opportunities, causing financial damages for both candidates and companies. We refer to these scenarios where candidates or job vacancies suffer from the absence of matching opportunities as the Matching Scarcity Problem (MaSP). First, we formalize the MaSP itself. Second, we proposed and evaluated strategies to identify automatically candidates and job vacancies suffering from MaSP. Finally, we proposed five heuristic strategies to mitigate MaSP. Our strategies consist of introducing changes in curricula and job descriptions to approximate candidates suffering from MaSP to jobs semantically related to them, and vice versa. The best strategy was able to reduce up to 50% the number of both CVs and jobs suffering from MaSP before applying it in our sample.

Mitigating Matching Scarcity in Recruitment Recommendation Domains

Media Content Analysis II

Media Content Analysis I

Crowdsourcing offers a new form of work that makes it easy for people to work from home on their own computers. However, for some people with disabilities, such as visual impairment, problems are encountered in completing tasks. In this study, a questionnaire was used to investigate the working conditions of and problems encountered by visually impaired people on Japanese and US crowdsourcing platforms. The problems overcome by visually impaired people, difference between the visually impaired crowd workers from different countries, and possible solutions are provided herein. This research can be extended to more crowdsourcing platforms and languages in the future.

A Survey of Visually Impaired Workers in Japanese and US Crowdsourcing Platforms

keynote

technical paper

Underline digital video library