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Verifying baselines for crisis event information classification on Twitter
Social media are rich information sources during and in the aftermath of crisis events such as earthquakes and terrorist attacks. Despite myriad challenges, with the right tools, significant insight can be gained which can assist emergency responders and related applications. However, most extant approaches are incomparable, using bespoke definitions, models, datasets and even evaluation metrics. Furthermore, it is rare that code, trained models, or exhaustive parametrisation details are made openly available. Thus, even confirmation of self-reported performance is problematic; authoritatively determining the state of the art (SOTA) is essentially impossible. Consequently, to begin addressing such endemic ambiguity, this paper seeks to make 3 contributions: 1) the replication and results confirmation of a leading (and generalisable) technique; 2) testing straightforward modifications of the technique likely to improve performance; and 3) the extension of the technique to a novel and complimentary type of crisis-relevant information to demonstrate itâs generalisability
A Tunnel-aware Language for Network Packet Filtering
AbstractâWhile in computer networks the number of possible protocol encapsulations is growing day after day, network administrators face ever increasing difficulties in selecting accurately the traffic they need to inspect. This is mainly caused by the limited number of encapsulations supported by currently available tools and the difficulty to exactly specify which packets have to be analyzed, especially in presence of tunneled traffic. This paper presents a novel packet processing language that, besides Boolean filtering predicates, introduces special constructs for handling the more complex situations of tunneled and stacked encapsulations, giving the user a finer control over the semantics of a filtering expression. Even though this language is principally focused on packet filters, it is designed to support other advanced packet processing mechanisms such as traffic classification and field extraction. I
When Things Matter: A Data-Centric View of the Internet of Things
With the recent advances in radio-frequency identification (RFID), low-cost
wireless sensor devices, and Web technologies, the Internet of Things (IoT)
approach has gained momentum in connecting everyday objects to the Internet and
facilitating machine-to-human and machine-to-machine communication with the
physical world. While IoT offers the capability to connect and integrate both
digital and physical entities, enabling a whole new class of applications and
services, several significant challenges need to be addressed before these
applications and services can be fully realized. A fundamental challenge
centers around managing IoT data, typically produced in dynamic and volatile
environments, which is not only extremely large in scale and volume, but also
noisy, and continuous. This article surveys the main techniques and
state-of-the-art research efforts in IoT from data-centric perspectives,
including data stream processing, data storage models, complex event
processing, and searching in IoT. Open research issues for IoT data management
are also discussed
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