93,292 research outputs found
Workflow Behavior Auditing for Mission Centric Collaboration
Successful mission-centric collaboration depends on situational awareness in an increasingly complex mission environment. To support timely and reliable high level mission decisions, auditing tools need real-time data for effective assessment and optimization of mission behaviors. In the context of a battle rhythm, mission health can be measured from workflow generated activities. Though battle rhythm collaboration is dynamic and global, a potential enabling technology for workflow behavior auditing exists in process mining.
However, process mining is not adequate to provide mission situational awareness in the battle rhythm environment since event logs may contain dynamic mission states, noise and timestamp inaccuracy. Therefore, we address a few key near-term issues. In sequences of activities parsed from network traffic streams, we identify mission state changes in the workflow shift detection algorithm. In segments of unstructured event logs that contain both noise and relevant workflow data, we extract and rank workflow instances for the process analyst. When confronted with timestamp inaccuracy in event logs from semi automated, distributed workflows, we develop the flower chain network and discovery algorithm to improve behavioral conformance. For long term adoption of process mining in mission centric collaboration, we develop and demonstrate an experimental framework for logging uncertainty testing. We show that it is highly feasible to employ process mining techniques in environments with dynamic mission states and logging uncertainty.
Future workflow behavior auditing technology will benefit from continued algorithmic development, new data sources and system prototypes to propel next generation mission situational awareness, giving commanders new tools to assess and optimize workflows, computer systems and missions in the battle space environment
21st Century Simulation: Exploiting High Performance Computing and Data Analysis
This paper identifies, defines, and analyzes the limitations imposed on Modeling and Simulation by outmoded
paradigms in computer utilization and data analysis. The authors then discuss two emerging capabilities to
overcome these limitations: High Performance Parallel Computing and Advanced Data Analysis. First, parallel
computing, in supercomputers and Linux clusters, has proven effective by providing users an advantage in
computing power. This has been characterized as a ten-year lead over the use of single-processor computers.
Second, advanced data analysis techniques are both necessitated and enabled by this leap in computing power.
JFCOM's JESPP project is one of the few simulation initiatives to effectively embrace these concepts. The
challenges facing the defense analyst today have grown to include the need to consider operations among non-combatant
populations, to focus on impacts to civilian infrastructure, to differentiate combatants from non-combatants,
and to understand non-linear, asymmetric warfare. These requirements stretch both current
computational techniques and data analysis methodologies. In this paper, documented examples and potential
solutions will be advanced. The authors discuss the paths to successful implementation based on their experience.
Reviewed technologies include parallel computing, cluster computing, grid computing, data logging, OpsResearch,
database advances, data mining, evolutionary computing, genetic algorithms, and Monte Carlo sensitivity analyses.
The modeling and simulation community has significant potential to provide more opportunities for training and
analysis. Simulations must include increasingly sophisticated environments, better emulations of foes, and more
realistic civilian populations. Overcoming the implementation challenges will produce dramatically better insights,
for trainees and analysts. High Performance Parallel Computing and Advanced Data Analysis promise increased
understanding of future vulnerabilities to help avoid unneeded mission failures and unacceptable personnel losses.
The authors set forth road maps for rapid prototyping and adoption of advanced capabilities. They discuss the
beneficial impact of embracing these technologies, as well as risk mitigation required to ensure success
Appalachian Coalfield Delegation Position Paper on Sustainable Energy
Appalachian grassroots groups(with support provided by the DataCenter) release a scathing report on the impact of coal mining to the United Nations Commission on Sustainable Development. The Delegation created an historic moment with its powerful stories and diverse outreach. Alliances were forged and the civil society discourse on energy, particularly what is sustainable energy and who gets to define it, has been challenged. Their answer---"it comes from the people!" As most government officials continue to ignore the atrocities of mountain top removal, coal sludge impoundments, and underground injections of sludge, it is up to the people of the Appalachian coal fields to let the world know the harsh realities of an economy built on seemingly cheap electricity
Remote sensing in mineral exploration from LANDSAT imagery
There are no author-identified significant results in this report
Text Data Mining from the Author's Perspective: Whose Text, Whose Mining, and to Whose Benefit?
Given the many technical, social, and policy shifts in access to scholarly
content since the early days of text data mining, it is time to expand the
conversation about text data mining from concerns of the researcher wishing to
mine data to include concerns of researcher-authors about how their data are
mined, by whom, for what purposes, and to whose benefits.Comment: Forum Statement: Data Mining with Limited Access Text: National
Forum. April 5-6, 2018. https://publish.illinois.edu/limitedaccess-tdm
On Reinforcement Learning for Full-length Game of StarCraft
StarCraft II poses a grand challenge for reinforcement learning. The main
difficulties of it include huge state and action space and a long-time horizon.
In this paper, we investigate a hierarchical reinforcement learning approach
for StarCraft II. The hierarchy involves two levels of abstraction. One is the
macro-action automatically extracted from expert's trajectories, which reduces
the action space in an order of magnitude yet remains effective. The other is a
two-layer hierarchical architecture which is modular and easy to scale,
enabling a curriculum transferring from simpler tasks to more complex tasks.
The reinforcement training algorithm for this architecture is also
investigated. On a 64x64 map and using restrictive units, we achieve a winning
rate of more than 99\% against the difficulty level-1 built-in AI. Through the
curriculum transfer learning algorithm and a mixture of combat model, we can
achieve over 93\% winning rate of Protoss against the most difficult
non-cheating built-in AI (level-7) of Terran, training within two days using a
single machine with only 48 CPU cores and 8 K40 GPUs. It also shows strong
generalization performance, when tested against never seen opponents including
cheating levels built-in AI and all levels of Zerg and Protoss built-in AI. We
hope this study could shed some light on the future research of large-scale
reinforcement learning.Comment: Appeared in AAAI 201
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