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

Staging urban emergence through collective creativity: Devising an outdoor mobile augmented reality tool

The unpredictability of global geopolitical conflicts, economic trends, and impacts of climate change, coupled with an increasing urban population, necessitates a more profound commitment to resilience thinking in urban planning and design. In contrast to top-down planning and designing for sustainability, allowing for emergence to take place seems to contribute to a capacity to better deal with this complex unpredictability, by allowing incremental changes through bottom-up, self-organized adaptation made by diverse actors in the proximity of various social, economical and functional entities in the urban context.The present thesis looks into the processes of creating urban emergence from both theoretical and practical perspectives. The theoretical section of the thesis first looks into the relationship between the processes and the qualities of a compact city. The Japanese city of Tokyo is used as an example of a resilient compact city that continuously emerges through incremental micro-adaptations by individual actors guided by urban rules that ‘let it happen’ without much central control or top-down design of the individual outcomes. The thesis then connects such rule-based emergent processes and the qualities of a compact city to complex adaptive system’s (CAS) theory, emphasizing the value of incremental and individual multiple-stakeholder input. The latter part of the thesis focuses on how to create a platform that can combine the bottom-up, emergent, rule-based planning approaches, and collective creativity based on individual participation and input from the public. This section is dedicated to developing a tool for a collaborative urban design using outdoor mobile augmented reality (MAR) by research-through-design method.The thesis thus has three parts addressing the topics: 1. urban planning processes and resulting urban qualities concerning compact city – i.e., density and diversity; 2. the processes of urban emergence, which generates complexity that renders urban resilience from the urban planning theory perspective; 3. developing a tool for non-expert citizens and other stakeholders to design and visualize an urban neighborhood by simulating the rule-based urban emergence using outdoor MAR. The results include a proposal for a complementary hybrid planning approaches that might approximate the CAS in urban systems with qualities that contribute to urban resiliency. Thereafter, the results describe specifications and design criteria for a tool as a public collaborative design platform using outdoor MAR to promote public participation: Urban CoBuilder. The processes of developing and prototyping such a tool to test various urban concepts concerning identified adaptive urban planning approaches are also presented with an assessment of the MAR tool based on focus group user tests. Future studies need to better include the potential of crowdsourcing public creativity through mass participation using the collaborative design tool and actual integration of these participatory design results in urban policies

Modeling flocks with perceptual agents from a dynamicist perspective

Computational simulations of flocks and crowds have typically been processed by a set of logic or syntactic rules. In recent decades, a new generation of systems has emerged from dynamicist approaches in which the agents and the environment are treated as a pair of dynamical systems coupled informationally and mechanically. Their spontaneous interactions allow them to achieve the desired behavior. The main proposition assumes that the agent does not need a full model or to make inferences before taking actions; rather, the information necessary for any action can be derived from the environment with simple computations and very little internal state. In this paper, we present a simulation framework in which the agents are endowed with a sensing device, an oscillator network as controller and actuators to interact with the environment. The perception device is designed as an optic array emulating the principles of the animal retina, which assimilates stimuli resembling optic flow to be captured from the environment. The controller modulates informational variables to action variables in a sensory-motor flow. Our approach is based on the Kuramoto model that describes mathematically a network of coupled phase oscillators and the use of evolutionary algorithms, which is proved to be capable of synthesizing minimal synchronization strategies based on the dynamical coupling between agents and environment. We carry out a comparative analysis with classical implementations taking into account several criteria. It is concluded that we should consider replacing the metaphor of symbolic information processing by that of sensory-motor coordination in problems of multi-agent organizations

An all-densities pedestrian simulator based on a dynamic evaluation of the interpersonal distances

In this paper we deal with pedestrian modeling, aiming at simulating crowd behavior in normal and emergency scenarios, including highly congested mass events. We are specifically concerned with a new agent-based, continuous-in-space, discrete-in-time, nondifferential model, where pedestrians have finite size and are compressible to a certain extent. The model also takes into account the pushing behavior appearing at extreme high densities. The main novelty is that pedestrians are not assumed to generate any kind of "field" in the space around which determines the behavior of the crowd. Instead, the behavior of each pedestrian solely relies on its knowledge of the environment and the evaluation of interpersonal distances between it and the others. The model is able to reproduce the concave/concave fundamental diagram with a "double hump" (i.e. with a second peak) which shows up when body forces come into play. We present several numerical tests (some of them being inspired by the recent ISO 20414 standard), which show how the model can reproduce classical self-organizing patterns

Multiple-Input-Single-Output prediction models of crowd dynamics for Model Predictive Control (MPC) of crowd evacuations

Predicting crowd dynamics in real-time may allow the design of adaptive pedestrian flow control mechanisms that prioritize attendees? safety and overall experience. Single-Input-SingleOutput (SISO) AutoRegresive eXogenous (ARX) prediction models of crowd dynamics have been effectively used in Linear Model Predictive Controllers (MPC) that adaptively regulate the movement of people to avoid overcrowding. However, an open research question is whether Multiple-Input, State-space, and Nonlinear modeling approaches may improve MPC control performance through better prediction capabilities. This paper considers a simulated controlled evacuation scenario, where evacuees in a long corridor dynamically receive speed instructions to modulate congestion at the exits. We aim to investigate Multiple-Input-Single-Output (MISO) prediction models such that the inputs are the control action (speed recommendation) and pedestrian flow measurement, and the output is the local density of the pedestrian outflow. State-space and Input?output MISO models, linear and neural, are identified using a datadriven approach in which input?output datasets are generated from strategically designed microscopic evacuation simulations. Different estimation algorithms, including the subspace method, prediction error minimization, and regularized AutoRegressive eXogenous (ARX) model reduction, are evaluated and compared. Finally, to investigate the importance of measuring and modeling the pedestrian inflow, the case in which the models? structure is defined as a Single-Input-Single-Output (SISO) system has been explored, where the pedestrian inflow is considered an unmeasured input disturbance. This study has important implications for the design of more effective MPC controllers for regulating pedestrian flows. We found that the prediction error minimization algorithm performs best and that nonlinear state-space modeling does not improve prediction performance. The study suggests that modeling the inner state of the evacuation process through a state-space model positively influences predicting system dynamics. Also, modeling pedestrian inflow improves prediction performance from a predefined prediction horizon value. Overall, linear state-space models have been deemed the most suitable option in corridor-type scenariosUAH-Catedra MANED