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Neighborhood effects have an important role in evacuation decision-making by a family. Owing to peer influence, neighbors evacuating can motivate a family to evacuate. Paradoxically, if a lot of neighbors evacuate, then the likelihood of an individual or family deciding to evacuate decreases, for fear of crime and looting. Such behavior cannot be captured using standard models of contagion spread on networks, e.g., threshold, independent cascade, and linear threshold models. Here, we propose a new threshold-based graph dynamical system model, 2mode-threshold, which captures this dichotomy. We study theoretically the dynamical properties of 2mode-threshold in different networks, and find significant differences from a standard threshold model. We build and characterize small world networks of Virginia Beach, VA, where nodes are geolocated families (households) in the city and edges are interactions between pairs of families. We demonstrate the utility of our behavioral model through agent-based simulations on these small world networks. We use it to understand evacuation rates in this region, and to evaluate the effects of modeling parameters on evacuation decision dynamics. Specifically, we quantify the effects of (1) network generation parameters, (2) stochasticity in the social network generation process, (3) model types (2mode-threshold vs. standard threshold models), (4) 2mode-threshold model parameters, (5) and initial conditions, on computed evacuation rates and their variability. An illustrative example result shows that the absence of looting effect can overpredict evacuation rates by as much as 50%. 

The first major goal of this project is to build a state-of-the-art information storage, retrieval, and analysis system that utilizes the latest technology and industry methods. This system is leveraged to accomplish another major goal, supporting modern search and browse capabilities for a large collection of tweets from the Twitter social media platform, web pages, and electronic theses and dissertations (ETDs). The backbone of the information system is a Docker container cluster running with Rancher and Kubernetes. Information retrieval and visualization is accomplished with containers in a pipelined fashion, whether in the cluster or on virtual machines, for Elasticsearch and Kibana, respectively. In addition to traditional searching and browsing, the system supports full-text and metadata searching. Search results include facets as a modern means of browsing among related documents. The system supports text analysis and machine learning to reveal new properties of collection data. These new properties assist in the generation of available facets. Recommendations are also presented with search results based on associations among documents and with logged user activity. The information system is co-designed by five teams of Virginia Tech graduate students, all members of the same computer science class, CS 5604. Although the project is an academic exercise, it is the practice of the teams to work and interact as though they are groups within a company developing a product. The teams on this project include three collection management groups -- Electronic Theses and Dissertations (ETD), Tweets (TWT), and Web-Pages (WP) -- as well as the Front-end (FE) group and the Integration (INT) group to help provide the overarching structure for the application. This submission focuses on the work of the Integration (INT) team, which creates and administers Docker containers for each team in addition to administering the cluster infrastructure. Each container is a customized application environment that is specific to the needs of the corresponding team. Each team will have several of these containers set up in a pipeline formation to allow scaling and extension of the current system. The INT team also contributes to a cross-team effort for exploring the use of Elasticsearch and its internally associated database. The INT team administers the integration of the Ceph data storage system into the CS Department Cloud and provides support for interactions between containers and the Ceph filesystem. During formative stages of development, the INT team also has a role in guiding team evaluations of prospective container components and workflows. The INT team is responsible for the overall project architecture and facilitating the tools and tutorials that assist the other teams in deploying containers in a development environment according to mutual specifications agreed upon with each team. The INT team maintains the status of the Kubernetes cluster, deploying new containers and pods as needed by the collection management teams as they expand their workflows. This team is responsible for utilizing a continuous integration process to update existing containers. During the development stage the INT team collaborates specifically with the collection management teams to create the pipeline for the ingestion and processing of new collection documents, crossing services between those teams as needed. The INT team develops a reasoner engine to construct workflows with information goal as input, which are then programmatically authored, scheduled, and monitored using Apache Airflow. The INT team is responsible for the flow, management, and logging of system performance data and making any adjustments necessary based on the analysis of testing results. The INT team has established a Gitlab repository for archival code related to the entire project and has provided the other groups with the documentation to deposit their code in the repository. This repository will be expanded using Gitlab CI in order to provide continuous integration and testing once it is available. Finally, the INT team will provide a production distribution that includes all embedded Docker containers and sub-embedded Git source code repositories. The INT team will archive this distribution on the Virginia Tech Docker Container Registry and deploy it on the Virginia Tech CS Cloud. The INT-2020 team owes a sincere debt of gratitude to the work of the INT-2019 team. This is a very large undertaking and the wrangling of all of the products and processes would not have been possible without their guidance in both direct and written form. We have relied heavily on the foundation they and their predecessors have provided for us. We continue their work with systematic improvements, but also want to acknowledge their efforts Ibid. Without them, our progress to date would not have been possible. 

With the demand and abundance of information increasing over the last two decades, generations of computer scientists are trying to improve the whole process of information searching, retrieval, and storage. With the diversification of the information sources, users' demand for various requirements of the data has also changed drastically both in terms of usability and performance. Due to the growth of the source material and requirements, correctly sorting, filtering, and storing has given rise to many new challenges in the field. With the help of all four other teams on this project, we are developing an information retrieval, analysis, and storage system to retrieve data from Virginia Tech's Electronic Thesis and Dissertation (ETD), Twitter, and Web Page archives. We seek to provide an appropriate data research and management tool to the users to access specific data. The system will also give certain users the authority to manage and add more data to the system. This project's deliverable will be combined with four others to produce a system usable by Virginia Tech's library system to manage, maintain, and analyze these archives. This report attempts to introduce the system components and design decisions regarding how it has been planned and implemented. Our team has developed a front end web interface that is able to search, retrieve, and manage three important content collection types: ETDs, tweets, and web pages. The interface incorporates a simple hierarchical user permission system, providing different levels of access to its users. In order to facilitate the workflow with other teams, we have containerized this system and made it available on the Virginia Tech cloud server. The system also makes use of a dynamic workflow system using a KnowledgeGraph and Apache Airflow, providing high levels of functional extensibility to the system. This allows curators and researchers to use containerised services for crawling, pre-processing, parsing, and indexing their custom corpora and collections that are available to them in the system.