A Finite State Automaton Representation And Simulation Of A Data/Frame Model Of Sensemaking

This thesis presents the application of a finite state automaton (FSA) to analytic modeling of Data/Frame Model (DFM) of sensemaking. A FSA is chosen for the DFM simulation because of its inherent characteristics to mimic changes in system behaviors and transitional states akin to the dynamic information changes in dynamic and unstructured emergencies. It also has the ability to capture feedback and loops, transitions, and spatio-temporal events based on iterative processes of an individual or a group of sensemakers. The thesis has exploited the human-driven DFM constructs for analytical modeling using Laboratory Virtual Instrumentation Engineering Workbench (LabVIEW) software system. Sensemaking times, problem stage time (PST), and nodeto-node (NTN) transition times serve as the major performance factors. The results obtained show differences in sensemaking times based on problem complexity and information uncertainty. An analysis of variance (ANOVA) statistical analysis, for three developed fictitious scenarios with different complexities and Hurricane Katrina, was conducted to investigate sensemaking performance. The results show that sensemaking performance was significant with an F (3,177) of 16.78 and probability value less than 0.05, indicating an overall effect of sensemaking information flow on sensemaking. Tukey’s Studentized Range Test shows the significant statistical differences between the complexities of Hurricane Katrina (HK) and medium complexity scenario (MC), HK and low complexity scenario (LC), high complexity scenario (HC) and LC, and MC and LC

Modeling Interdependent and Periodic Real-World Action Sequences

Mobile health applications, including those that track activities such as exercise, sleep, and diet, are becoming widely used. Accurately predicting human actions is essential for targeted recommendations that could improve our health and for personalization of these applications. However, making such predictions is extremely difficult due to the complexities of human behavior, which consists of a large number of potential actions that vary over time, depend on each other, and are periodic. Previous work has not jointly modeled these dynamics and has largely focused on item consumption patterns instead of broader types of behaviors such as eating, commuting or exercising. In this work, we develop a novel statistical model for Time-varying, Interdependent, and Periodic Action Sequences. Our approach is based on personalized, multivariate temporal point processes that model time-varying action propensities through a mixture of Gaussian intensities. Our model captures short-term and long-term periodic interdependencies between actions through Hawkes process-based self-excitations. We evaluate our approach on two activity logging datasets comprising 12 million actions taken by 20 thousand users over 17 months. We demonstrate that our approach allows us to make successful predictions of future user actions and their timing. Specifically, our model improves predictions of actions, and their timing, over existing methods across multiple datasets by up to 156%, and up to 37%, respectively. Performance improvements are particularly large for relatively rare and periodic actions such as walking and biking, improving over baselines by up to 256%. This demonstrates that explicit modeling of dependencies and periodicities in real-world behavior enables successful predictions of future actions, with implications for modeling human behavior, app personalization, and targeting of health interventions.Comment: Accepted at WWW 201

A MODEL FOR EVALUATING THE PERFORMANCE OF OPERATIONAL LEVEL INFORMATION HANDLING ACTIVITIES

This paper describes a modeling framework that will allow a manager to simulate and evaluate the performance of alternative designs of an operational level information handling process. Performance is measured in terms of the quality of the OutputS of the process, the total flow time through the process, and the human resource time required to produce an output. The two major design options represented in the model are capabilities of computerized information systems used, and characteristics of quality control mechanisms within the information handling process. Based on the field study, we elaborate on why the problem of designing information flows in an office is difficult and we describe some of the complexities that need to be considered in a performance evaluation model. This paper presents a summary of the methodology for representing an information handling process, and an example of how the methodology can be used to address a design problem at the field site

Water balance complexities in ephemeral catchments with different land uses: Insights from monitoring and distributed hydrologic modeling

Although ephemeral catchments are widespread in arid and semiarid climates, the relationship of their water balance with climate, geology, topography, and land cover is poorly known. Here we use 4 years (2011–2014) of rainfall, streamflow, and groundwater level measurements to estimate the water balance components in two adjacent ephemeral catchments in south-eastern Australia, with one catchment planted with young eucalypts and the other dedicated to grazing pasture. To corroborate the interpretation of the observations, the physically based hydrological model CATHY was calibrated and validated against the data in the two catchments. The estimated water balances showed that despite a significant decline in groundwater level and greater evapotranspiration in the eucalypt catchment (104–119% of rainfall) compared with the pasture catchment (95–104% of rainfall), streamflow consistently accounted for 1–4% of rainfall in both catchments for the entire study period. Streamflow in the two catchments was mostly driven by the rainfall regime, particularly rainfall frequency (i.e., the number of rain days per year), while the downslope orientation of the plantation furrows also promoted runoff. With minimum calibration, the model was able to adequately reproduce the periods of flow in both catchments in all years. Although streamflow and groundwater levels were better reproduced in the pasture than in the plantation, model-computed water balance terms confirmed the estimates from the observations in both catchments. Overall, the interplay of climate, topography, and geology seems to overshadow the effect of land use in the study catchments, indicating that the management of ephemeral catchments remains highly challenging

Interactively Learning Social Media Representations Improves News Source Factuality Detection

The rise of social media has enabled the widespread propagation of fake news, text that is published with an intent to spread misinformation and sway beliefs. Rapidly detecting fake news, especially as new events arise, is important to prevent misinformation. While prior works have tackled this problem using supervised learning systems, automatedly modeling the complexities of the social media landscape that enables the spread of fake news is challenging. On the contrary, having humans fact check all news is not scalable. Thus, in this paper, we propose to approach this problem interactively, where humans can interact to help an automated system learn a better social media representation quality. On real world events, our experiments show performance improvements in detecting factuality of news sources, even after few human interactions.Comment: Accepted at Findings of IJCNLP-AACL 202

Modeling reality

Although powerful computers have allowed complex physical and manmade hardware systems to be modeled successfully, we have encountered persistent problems with the reliability of computer models for systems involving human learning, human action, and human organizations. This is not a misfortune; unlike physical and manmade systems, human systems do not operate under a fixed set of laws. The rules governing the actions allowable in the system can be changed without warning at any moment, and can evolve over time. That the governing laws are inherently unpredictable raises serious questions about the reliability of models when applied to human situations. In these domains, computers are better used, not for prediction and planning, but for aiding humans. Examples are systems that help humans speculate about possible futures, offer advice about possible actions in a domain, systems that gather information from the networks, and systems that track and support work flows in organizations