This study looks into unsupervised and supervised methods for classifying and clustering volcano-seismic time series data. Due to the resource intensive labelling process required to understand volcano-seismic signals it is important to explore unsupervised analysis techniques in this domain. This study offers a comparison between the performance of supervised and unsupervised methods and offers insight into their potential for frame by frame classification of volcano-seismic signals.

This work presents a methodology to generate a mobility-pattern database for urban traffic monitoring through distributed acoustic sensing. Registers from the continuous monitoring of a live experimental testbed provide three canonical types of events (buses, cars and pedestrians). Their separability for automatic detection and classification is inspected through PCA analysis of their approximate entropy and Hjorth parameters. An automatic detection and labeling procedure is presented and used to generate a database of labeled files accessible and usable for future machine learning approaches.

The Syria conflict has been described as the most socially mediated in history, with online social media playing a particularly important role. At the same time, the ever-changing landscape of the conflict leads to difficulties in applying analytical approaches taken by other studies of online political activism. Therefore, in this paper, we use an approach that does not require strong prior assumptions or the proposal of an advance hypothesis to analyze Twitter and YouTube activity of a range of protagonists to the conflict, in an attempt to reveal additional insights into the relationships between them. By means of a network representation that combines multiple data views, we uncover communities of accounts falling into four categories that broadly reflect the situation on the ground in Syria. A detailed analysis of selected communities within the anti-regime categories is provided, focusing on their central actors, preferred online platforms, and activity surrounding “real world” events. Our findings indicate that social media activity in Syria is considerably more convoluted than reported in many other studies of online political activism, suggesting that alternative analytical approaches can play an important role in this type of scenario.

In this paper, we describe a News Story Gisting system that generates a 10-word short summary of a news story. This system uses a machine learning technique to combine linguistic, statistical and positional information in order to generate an appropriate summary. We also present the results of an automatic evaluation of this system with respect to the performance of other baseline summarisers using the new ROUGE evaluation metric.

Describes research into the use of lexical chains to build effective topic tracking systems. Lexical chaining is a method of grouping lexically related terms into so called lexical chains, using simple natural language processing techniques. Topic tracking involves tracking a given news event in a stream of news stories i.e. finding all subsequent storks in the news stream that discuss the given event. It has grown out of the Topic Detection and Tracking (TDT) initiative sponsored by DARPA. The paper describes the results of a topic tracking system, LexTrack, based on lexical chaining and compares it to a tracking system designed using traditional IR techniques.

In this paper we describe a type of data fusion involving the combination of evidence derived from multiple document representations. Our aim is to investigate if a composite representation can improve the online detection of novel events in a stream of broadcast news stories. This classification process otherwise known as first story detection FSD (or in the Topic Detection and Tracking pilot study as online new event detection [1]), is one of three main classification tasks defined by the TDT initiative. Our composite document representation consists of a semantic representation (based on the lexical chains derived from a text) and a syntactic representation (using proper nouns). Using the TDT1 evaluation methodology, we evaluate a number of document representation combinations using these document classifiers.

No abstract available.