265 research outputs found
Where Did the President Visit Last Week? Detecting Celebrity Trips from News Articles
Celebrities' whereabouts are of pervasive importance. For instance, where
politicians go, how often they visit, and who they meet, come with profound
geopolitical and economic implications. Although news articles contain travel
information of celebrities, it is not possible to perform large-scale and
network-wise analysis due to the lack of automatic itinerary detection tools.
To design such tools, we have to overcome difficulties from the heterogeneity
among news articles: 1)One single article can be noisy, with irrelevant people
and locations, especially when the articles are long. 2)Though it may be
helpful if we consider multiple articles together to determine a particular
trip, the key semantics are still scattered across different articles
intertwined with various noises, making it hard to aggregate them effectively.
3)Over 20% of the articles refer to the celebrities' trips indirectly, instead
of using the exact celebrity names or location names, leading to large portions
of trips escaping regular detecting algorithms. We model text content across
articles related to each candidate location as a graph to better associate
essential information and cancel out the noises. Besides, we design a special
pooling layer based on attention mechanism and node similarity, reducing
irrelevant information from longer articles. To make up the missing information
resulted from indirect mentions, we construct knowledge sub-graphs for named
entities (person, organization, facility, etc.). Specifically, we dynamically
update embeddings of event entities like the G7 summit from news descriptions
since the properties (date and location) of the event change each time, which
is not captured by the pre-trained event representations. The proposed CeleTrip
jointly trains these modules, which outperforms all baseline models and
achieves 82.53% in the F1 metric.Comment: Accepted to ICWSM 2024, 12 page
An Efficient Patch Dissemination Strategy for Mobile Networks
Mobile phones and personal digital assistants are becoming increasingly important in our daily life since they enable us to access a large variety of ubiquitous services. Mobile networks, formed by the connection of mobile devices following some relationships among mobile users, provide good platforms for mobile virus spread. Quick
and efficient security patch dissemination strategy is necessary for the update of antivirus software so that it can detect mobile virus, especially the new virus under the wireless mobile network environment with limited bandwidth which is also large scale, decentralized, dynamically evolving, and of unknown network topology. In this paper, we propose an efficient semi autonomy-oriented computing (SAOC) based patch dissemination strategy to restrain the mobile virus. In this strategy, some entities are deployed in a mobile network to search for mobile devices according to some specific rules and with the assistance of a center. Through experiments involving both real-world networks and dynamically evolving networks, we demonstrate that the proposed strategy can effectively send security patches to as many mobile devices as possible at a considerable speed and lower cost in the mobile network. It is a reasonable, effective, and secure
method to reduce the damages mobile viruses may cause
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