Engineering Crowdsourced Stream Processing Systems

A crowdsourced stream processing system (CSP) is a system that incorporates crowdsourced tasks in the processing of a data stream. This can be seen as enabling crowdsourcing work to be applied on a sample of large-scale data at high speed, or equivalently, enabling stream processing to employ human intelligence. It also leads to a substantial expansion of the capabilities of data processing systems. Engineering a CSP system requires the combination of human and machine computation elements. From a general systems theory perspective, this means taking into account inherited as well as emerging properties from both these elements. In this paper, we position CSP systems within a broader taxonomy, outline a series of design principles and evaluation metrics, present an extensible framework for their design, and describe several design patterns. We showcase the capabilities of CSP systems by performing a case study that applies our proposed framework to the design and analysis of a real system (AIDR) that classifies social media messages during time-critical crisis events. Results show that compared to a pure stream processing system, AIDR can achieve a higher data classification accuracy, while compared to a pure crowdsourcing solution, the system makes better use of human workers by requiring much less manual work effort

PadChest: A large chest x-ray image dataset with multi-label annotated reports

We present a labeled large-scale, high resolution chest x-ray dataset for the automated exploration of medical images along with their associated reports. This dataset includes more than 160,000 images obtained from 67,000 patients that were interpreted and reported by radiologists at Hospital San Juan Hospital (Spain) from 2009 to 2017, covering six different position views and additional information on image acquisition and patient demography. The reports were labeled with 174 different radiographic findings, 19 differential diagnoses and 104 anatomic locations organized as a hierarchical taxonomy and mapped onto standard Unified Medical Language System (UMLS) terminology. Of these reports, 27% were manually annotated by trained physicians and the remaining set was labeled using a supervised method based on a recurrent neural network with attention mechanisms. The labels generated were then validated in an independent test set achieving a 0.93 Micro-F1 score. To the best of our knowledge, this is one of the largest public chest x-ray database suitable for training supervised models concerning radiographs, and the first to contain radiographic reports in Spanish. The PadChest dataset can be downloaded from http://bimcv.cipf.es/bimcv-projects/padchest/

CAD-model-based vision for space applications

A pose acquisition system operating in space must be able to perform well in a variety of different applications including automated guidance and inspections tasks with many different, but known objects. Since the space station is being designed with automation in mind, there will be CAD models of all the objects, including the station itself. The construction of vision models and procedures directly from the CAD models is the goal of this project. The system that is being designed and implementing must convert CAD models to vision models, predict visible features from a given view point from the vision models, construct view classes representing views of the objects, and use the view class model thus derived to rapidly determine the pose of the object from single images and/or stereo pairs

Indian Organised Apparel Retail Sector and DSS (Decision Support Systems)

Indian apparel retail sector poses interesting challenges to a manager as it is evolving and closely linked to fashions. Appealing mainly to youth, the sector has typical information requirements to manage its operations. DSS (Decision Support Systems) provide timely and accurate information & it can be viewed as an integrated entity providing management with the tools and information to assist their decision making. The study exploratory in nature, adopts a case study approach to understand practices of organized retailers in apparel sector regarding applications of various DSS tools. Conceptual overview of DSS is undertaken by reviewing the literature. The study describes practices and usage of DSS in operational decisions in apparel sector and managerial issues in design and implementation of DSS. A multi brand local chain and multi brand national chain of apparel was chosen for the study. Varied tools were found to be used by them. It was also found that for sales forecasting and visual merchandising decisions, prior experience rather than any DSS tool was used. The benefits realized were; “help as diagnostic tool”, “accuracy of records and in billing”, “smooth operations”. The implementation issues highlighted by the store managers were; more initial teething problems rather than resistance on the part of employees of the store, need for investment of time & money in training, due to rapid technological advancements, time to time updation in DSS tools is required . Majority of operational decisions like inventory management, CRM, campaign management were handled by ERP (Enterprise Resource Planning) or POS (Point of Sale). Prioritization as well as quantification of benefits was not attempted. The issues of coordination, integration with other systems in case of ERP usage, training were highlighted. Future outlook of DSS seems bright as apparel retailers are keen to invest in technology.