17,315 research outputs found
Investigative Simulation: Towards Utilizing Graph Pattern Matching for Investigative Search
This paper proposes the use of graph pattern matching for investigative graph
search, which is the process of searching for and prioritizing persons of
interest who may exhibit part or all of a pattern of suspicious behaviors or
connections. While there are a variety of applications, our principal
motivation is to aid law enforcement in the detection of homegrown violent
extremists. We introduce investigative simulation, which consists of several
necessary extensions to the existing dual simulation graph pattern matching
scheme in order to make it appropriate for intelligence analysts and law
enforcement officials. Specifically, we impose a categorical label structure on
nodes consistent with the nature of indicators in investigations, as well as
prune or complete search results to ensure sensibility and usefulness of
partial matches to analysts. Lastly, we introduce a natural top-k ranking
scheme that can help analysts prioritize investigative efforts. We demonstrate
performance of investigative simulation on a real-world large dataset.Comment: 8 pages, 6 figures. Paper to appear in the Fosint-SI 2016 conference
proceedings in conjunction with the 2016 IEEE/ACM International Conference on
Advances in Social Networks Analysis and Mining ASONAM 201
ICE: Enabling Non-Experts to Build Models Interactively for Large-Scale Lopsided Problems
Quick interaction between a human teacher and a learning machine presents
numerous benefits and challenges when working with web-scale data. The human
teacher guides the machine towards accomplishing the task of interest. The
learning machine leverages big data to find examples that maximize the training
value of its interaction with the teacher. When the teacher is restricted to
labeling examples selected by the machine, this problem is an instance of
active learning. When the teacher can provide additional information to the
machine (e.g., suggestions on what examples or predictive features should be
used) as the learning task progresses, then the problem becomes one of
interactive learning.
To accommodate the two-way communication channel needed for efficient
interactive learning, the teacher and the machine need an environment that
supports an interaction language. The machine can access, process, and
summarize more examples than the teacher can see in a lifetime. Based on the
machine's output, the teacher can revise the definition of the task or make it
more precise. Both the teacher and the machine continuously learn and benefit
from the interaction.
We have built a platform to (1) produce valuable and deployable models and
(2) support research on both the machine learning and user interface challenges
of the interactive learning problem. The platform relies on a dedicated,
low-latency, distributed, in-memory architecture that allows us to construct
web-scale learning machines with quick interaction speed. The purpose of this
paper is to describe this architecture and demonstrate how it supports our
research efforts. Preliminary results are presented as illustrations of the
architecture but are not the primary focus of the paper
Nutrition Labeling in the United States and the Role of Consumer Processing, Message Structure, and Moderating Conditions
It has been since 1990 that the landmark Nutritional Labeling Education Act (NLEA) was passed in the United States, and since 1969 that the first White House Conference on Food, Nutrition and Health occurred. In the time since these important events, considerable research has been conducted on how U.S. consumers process and use nutritional labeling. An up-to-date review of nutritional labeling research must address key findings on the processing and use of nutrition facts panels (NFPs), restaurant labeling, front-of-pack (FOP) symbols, health and nutrient content claims, new labeling efforts (e.g., for meat products), and claims not regulated by the U.S. Food and Drug Administration (FDA). Message structure mediates the ways in which consumers process nutritional labeling while moderating conditions affect research outcomes associated with labeling efforts.
The most recent policy issues and problems to be considered (e.g., by the FDA) include nutritional labeling as well as identifying opportunities for consumer research in helping to promote healthy lifestyles and reducing obesity in the United States and throughout the world. For example, several unanswered research questions remain regarding how the proposed changes to the NFPs—beef, poultry, and seafood labeling; restaurant chain calorie labeling; alternative FOP formats; and regulated and unregulated health and nutrient content claims—will affect consumers. Researchers have yet to examine not only these different labeling and nutrition information formats, but also how they might interact with one another and the role of key moderating conditions (e.g., one’s motivation, ability opportunity to process nutrition information) in affecting consumer processing and behavior
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