Computational Approaches to Measuring the Similarity of Short Contexts : A Review of Applications and Methods

Measuring the similarity of short written contexts is a fundamental problem in Natural Language Processing. This article provides a unifying framework by which short context problems can be categorized both by their intended application and proposed solution. The goal is to show that various problems and methodologies that appear quite different on the surface are in fact very closely related. The axes by which these categorizations are made include the format of the contexts (headed versus headless), the way in which the contexts are to be measured (first-order versus second-order similarity), and the information used to represent the features in the contexts (micro versus macro views). The unifying thread that binds together many short context applications and methods is the fact that similarity decisions must be made between contexts that share few (if any) words in common.Comment: 23 page

Multi-GCN: Graph Convolutional Networks for Multi-View Networks, with Applications to Global Poverty

With the rapid expansion of mobile phone networks in developing countries, large-scale graph machine learning has gained sudden relevance in the study of global poverty. Recent applications range from humanitarian response and poverty estimation to urban planning and epidemic containment. Yet the vast majority of computational tools and algorithms used in these applications do not account for the multi-view nature of social networks: people are related in myriad ways, but most graph learning models treat relations as binary. In this paper, we develop a graph-based convolutional network for learning on multi-view networks. We show that this method outperforms state-of-the-art semi-supervised learning algorithms on three different prediction tasks using mobile phone datasets from three different developing countries. We also show that, while designed specifically for use in poverty research, the algorithm also outperforms existing benchmarks on a broader set of learning tasks on multi-view networks, including node labelling in citation networks

The Closer the Better: Similarity of Publication Pairs at Different Co-Citation Levels

We investigate the similarities of pairs of articles which are co-cited at the different co-citation levels of the journal, article, section, paragraph, sentence and bracket. Our results indicate that textual similarity, intellectual overlap (shared references), author overlap (shared authors), proximity in publication time all rise monotonically as the co-citation level gets lower (from journal to bracket). While the main gain in similarity happens when moving from journal to article co-citation, all level changes entail an increase in similarity, especially section to paragraph and paragraph to sentence/bracket levels. We compare results from four journals over the years 2010-2015: Cell, the European Journal of Operational Research, Physics Letters B and Research Policy, with consistent general outcomes and some interesting differences. Our findings motivate the use of granular co-citation information as defined by meaningful units of text, with implications for, among others, the elaboration of maps of science and the retrieval of scholarly literature

Video browsing interfaces and applications: a review

We present a comprehensive review of the state of the art in video browsing and retrieval systems, with special emphasis on interfaces and applications. There has been a significant increase in activity (e.g., storage, retrieval, and sharing) employing video data in the past decade, both for personal and professional use. The ever-growing amount of video content available for human consumption and the inherent characteristics of video data—which, if presented in its raw format, is rather unwieldy and costly—have become driving forces for the development of more effective solutions to present video contents and allow rich user interaction. As a result, there are many contemporary research efforts toward developing better video browsing solutions, which we summarize. We review more than 40 different video browsing and retrieval interfaces and classify them into three groups: applications that use video-player-like interaction, video retrieval applications, and browsing solutions based on video surrogates. For each category, we present a summary of existing work, highlight the technical aspects of each solution, and compare them against each other

Simulation of urban system evolution in a synergetic modelling framework. The case of Attica, Greece

Spatial analysis and evolution simulation of such complex and dynamic systems as modern urban areas could greatly benefit from the synergy of methods and techniques that constitute the core of the fields of Information Technology and Artificial Intelligence. Additionally, if during the decision making process, a consistent methodology is applied and assisted by a user-friendly interface, premium and pragmatic solution strategies can be tested and evaluated. In such a framework, this paper presents both a prototype Decision Support System and a consorting spatio-temporal methodology, for modelling urban growth. Its main focus is on the analysis of current trends, the detection of the factors that mostly affect the evolution process and the examination of user-defined hypotheses regarding future states of the problem environment. According to the approach, a neural network model is formulated for a specific time intervals and each different group of spatial units, mainly based to the degree of their contiguity and spatial interaction. At this stage, fuzzy logic provides a precise image of spatial entities, further exploited in a twofold way. First, for the analysis and interpretation of up-to-date urban evolution and second, for the formulation of a robust spatial simulation model. It should be stressed, however, that the neural network model is not solely used to define future urban images, but also to evaluate the degree of influence that each variable as a significant of problem parameter, contributes to the final result. Thus, the formulation and the analysis of alternative planning scenarios are assisted. Both the proposed methodological framework and the prototype Decision Support System are utilized during the study of Attica, Greece?s principal prefecture and the definition of a twenty-year forecast. The variables considered and projected refer to population data derived from the 1961-1991 censuses and building uses aggregated in ten different categories. The final results are visualised through thematic maps in a GIS environment. Finally, the performance of the methodology is evaluated as well as directions for further improvements and enhancements are outlined. Keywords: Computational geography, Spatial modelling, Neural network models, Fuzzy logic.