Publications

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Wargames are a common tool for investigating complex conflict scenarios and have a long history of informing military and strategic study. Historically, these games have often been one offs, may not rigorously collect data, and have been built primarily for exploration rather than developing data-driven analytical conclusions. Experimental wargaming, a new wargaming approach that employs the basic principles of experimental design to facilitate an objective basis for exploring fundamental research questions around human behavior (such as understanding conflict escalation), is a potential tool that can be used in combination with existing wargaming approaches. The Project on Nuclear Gaming, a consortium involving the University of California, Berkeley, Sandia National Laboratories, and Lawrence Livermore National Laboratory, developed an experimental wargame, SIGNAL, to explore questions surrounding conflict escalation and strategic stabil-ity in the nuclear context. To date, the SIGNAL experimental wargame has been played hundreds of times by thousands of players from around the world, creating the largest data-base of wargame data for academic purposes known to the authors. This paper discusses the design of SIGNAL, focusing on how the principles of experimental design influenced this design.

We describe the opportunities and challenges we faced when developing SIGNAL, an experimental wargame that was deployed as a distributed wargame.

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Cross Reality (XR) immersive environments offer challenges and opportunities in designing for cognitive aspects (e.g. learning, memory, attention, etc.) of information design and interactions. Information design is a multidisciplinary endeavor involving data science, communication science, cognitive science, media, and technology. In the present paper the holodeck metaphor is extended to illustrate how information design practices and some of the qualities of this imaginary computationally augmented environment (a.k.a. the holodeck) may be achieved in XR environments to support information-rich storytelling and real life, face-to-face, and virtual collaborative interactions. The Simulation Experience Design Framework & Method is introduced to organize challenges and opportunities in the design of information for XR. The notion of carefully blending both real and virtual spaces to achieve total immersion is discussed as the reader moves through the elements of the cyclical framework. A solution space leveraging cognitive science, information design, and transmedia learning highlights key challenges facing contemporary XR designers. Challenges include but are not limited to interleaving information, technology, and media into the human storytelling process, and supporting narratives in a way that is memorable, robust, and extendable.

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People use social media resources like Twitter, Facebook, forums etc. to share and discuss various activities or topics. By aggregating topic trends across many individuals using these services, we seek to construct a richer profile of a person’s activities and interests as well as provide a broader context of those activities. This profile may then be used in a variety of ways to understand groups as a collection of interests and affinities and an individual’s participation in those groups. Our approach considers that much of these data will be unstructured, free-form text. By analyzing free-form text directly, we may be able to gain an implicit grouping of individuals with shared interests based on shared conversation, and not on explicit social software linking them. In this paper, we discuss a proof-of-concept application called Grandmaster built to pull short sections of text, a person’s comments or Twitter posts, together by analysis and visualization to allow a gestalt understanding of the full collection of all individuals: how groups are similar and how they differ, based on their text inputs.

People use social media resources like Twitter, Facebook, forums etc. to share and discuss various activities or topics. By aggregating topic trends across many individuals using these services, we seek to construct a richer profile of a person’s activities and interests as well as provide a broader context of those activities. This profile may then be used in a variety of ways to understand groups as a collection of interests and affinities and an individual’s participation in those groups. Our approach considers that much of these data will be unstructured, free-form text. By analyzing free-form text directly, we may be able to gain an implicit grouping of individuals with shared interests based on shared conversation, and not on explicit social software linking them. In this paper, we discuss a proof-of-concept application called Grandmaster built to pull short sections of text, a person’s comments or Twitter posts, together by analysis and visualization to allow a gestalt understanding of the full collection of all individuals: how groups are similar and how they differ, based on their text inputs.

The Advanced Technology Development and Mitigation (ATDM) program at Sandia National Laboratories is a new effort to build next-generation simulation codes that will map well to upcoming exascale computing platforms. Rather than follow traditional single- program, multiple data (SPMD) programming techniques, ATDM is developing applications in an asynchronous many task (AMT) form that describes work as a graph of tasks that have data dependencies. The data management team is focused on developing a data warehouse for ATDM that will enable tasks to store and exchange data objects efficiently. This report summarizes the data management teams efforts during FY15, and documents: (1) an initial API and implementation for the data warehouses key/value store, (2) API requirements for use with ATDMs runtime, (3) initial requirements for storing ATDM-specific data, and (4) the current organization of software components that will be used by the data warehouse.

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A hypothetical scenario is utilized to explore privacy and security considerations for intelligent systems, such as a Personal Assistant for Learning (PAL). Two categories of potential concerns are addressed: factors facilitated by user models, and factors facilitated by systems. Among the strategies presented for risk mitigation is a call for ongoing, iterative dialog among privacy, security, and personalization researchers during all stages of development, testing, and deployment.

Memory failures in future extreme scale applications are a significant concern in the high-performance computing community and have attracted much research attention. We contend in this paper that using application checkpoint data to detect memory failures has potential benefits and is preferable to examining application memory. To support this contention, we describe the application of machine learning techniques to evaluate the veracity of checkpoint data. Our preliminary results indicate that supervised decision tree machine learning approaches can effectively detect corruption in restart files, suggesting that future extreme-scale applications and systems may benefit from incorporating such approaches in order to cope with memory failues.

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