Feature Type Analysis in Automated Genre Classification

In this paper, we compare classifiers based on language model, image, and stylistic features for automated genre classification. The majority of previous studies in genre classification have created models based on an amalgamated representation of a document using a multitude of features. In these models, the inseparable roles of different features make it difficult to determine a means of improving the classifier when it exhibits poor performance in detecting selected genres. By independently modeling and comparing classifiers based on features belonging to three types, describing visual, stylistic, and topical properties, we demonstrate that different genres have distinctive feature strengths.

Learning About Meetings

Most people participate in meetings almost every day, multiple times a day. The study of meetings is important, but also challenging, as it requires an understanding of social signals and complex interpersonal dynamics. Our aim this work is to use a data-driven approach to the science of meetings. We provide tentative evidence that: i) it is possible to automatically detect when during the meeting a key decision is taking place, from analyzing only the local dialogue acts, ii) there are common patterns in the way social dialogue acts are interspersed throughout a meeting, iii) at the time key decisions are made, the amount of time left in the meeting can be predicted from the amount of time that has passed, iv) it is often possible to predict whether a proposal during a meeting will be accepted or rejected based entirely on the language (the set of persuasive words) used by the speaker

How analysts think: think-steps as a tool for structuring sensemaking in criminal intelligence analysis

Sensemaking has been described as a process involving information structuring. However, there are few detailed accounts of how this manifests in practice, particularly in relation to the creation and use of external representations such as data visualisations, and how such structuring aids sensemaking. To explore these questions in depth, we present an interview study of police crime analysts from which a model of their analysis process is developed. We describe the model focusing on the notion of 'think-steps', which for the analysts acted as a primary structuring concept. We describe how 'think-steps' propagate throughout the analysis process captured in the model. For the analysts, 'think-steps' are extensible templates that decompose a case into elements, provide a way of storing and visually structuring data, support generation of requests for information, focus research, simulate a case, and shape reporting. We reflect on the implications that our findings might have for design, including the possibility of a repertoire of evolving, sharable and reusable templates for sensemaking within a community of practice

ASPIE: A Framework for Active Sensing and Processing of Complex Events in the Internet of Manufacturing Things

Rapid perception and processing of critical monitoring events are essential to ensure healthy operation of Internet of Manufacturing Things (IoMT)-based manufacturing processes. In this paper, we proposed a framework (active sensing and processing architecture (ASPIE)) for active sensing and processing of critical events in IoMT-based manufacturing based on the characteristics of IoMT architecture as well as its perception model. A relation model of complex events in manufacturing processes, together with related operators and unified XML-based semantic definitions, are developed to effectively process the complex event big data. A template based processing method for complex events is further introduced to conduct complex event matching using the Apriori frequent item mining algorithm. To evaluate the proposed models and methods, we developed a software platform based on ASPIE for a local chili sauce manufacturing company, which demonstrated the feasibility and effectiveness of the proposed methods for active perception and processing of complex events in IoMT-based manufacturing

Explicit diversification of event aspects for temporal summarization

During major events, such as emergencies and disasters, a large volume of information is reported on newswire and social media platforms. Temporal summarization (TS) approaches are used to automatically produce concise overviews of such events by extracting text snippets from related articles over time. Current TS approaches rely on a combination of event relevance and textual novelty for snippet selection. However, for events that span multiple days, textual novelty is often a poor criterion for selecting snippets, since many snippets are textually unique but are semantically redundant or non-informative. In this article, we propose a framework for the diversification of snippets using explicit event aspects, building on recent works in search result diversification. In particular, we first propose two techniques to identify explicit aspects that a user might want to see covered in a summary for different types of event. We then extend a state-of-the-art explicit diversification framework to maximize the coverage of these aspects when selecting summary snippets for unseen events. Through experimentation over the TREC TS 2013, 2014, and 2015 datasets, we show that explicit diversification for temporal summarization significantly outperforms classical novelty-based diversification, as the use of explicit event aspects reduces the amount of redundant and off-topic snippets returned, while also increasing summary timeliness

Evaluating the Integrated Measurement and Evaluation System IMES: A Success Story

This case study serves to illustrate an integrated and practical methodology for evaluating advanced information database systems. The goal of the integration is to create a top-down evaluation process that reduces user and data requirements to a standard evaluation structure. In this framework, the evaluation of the Integrated Measurement and Evaluation System IMES was implemented by the Energy Policy Unit of the National Technical University of Athens. Evaluation team members successfully followed the proposed evaluation methodology