A tree-based kernel for graphs with continuous attributes

The availability of graph data with node attributes that can be either discrete or real-valued is constantly increasing. While existing kernel methods are effective techniques for dealing with graphs having discrete node labels, their adaptation to non-discrete or continuous node attributes has been limited, mainly for computational issues. Recently, a few kernels especially tailored for this domain, and that trade predictive performance for computational efficiency, have been proposed. In this paper, we propose a graph kernel for complex and continuous nodes' attributes, whose features are tree structures extracted from specific graph visits. The kernel manages to keep the same complexity of state-of-the-art kernels while implicitly using a larger feature space. We further present an approximated variant of the kernel which reduces its complexity significantly. Experimental results obtained on six real-world datasets show that the kernel is the best performing one on most of them. Moreover, in most cases the approximated version reaches comparable performances to current state-of-the-art kernels in terms of classification accuracy while greatly shortening the running times.Comment: This work has been submitted to the IEEE Transactions on Neural Networks and Learning Systems for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

An Empirical Study on Budget-Aware Online Kernel Algorithms for Streams of Graphs

Kernel methods are considered an effective technique for on-line learning. Many approaches have been developed for compactly representing the dual solution of a kernel method when the problem imposes memory constraints. However, in literature no work is specifically tailored to streams of graphs. Motivated by the fact that the size of the feature space representation of many state-of-the-art graph kernels is relatively small and thus it is explicitly computable, we study whether executing kernel algorithms in the feature space can be more effective than the classical dual approach. We study three different algorithms and various strategies for managing the budget. Efficiency and efficacy of the proposed approaches are experimentally assessed on relatively large graph streams exhibiting concept drift. It turns out that, when strict memory budget constraints have to be enforced, working in feature space, given the current state of the art on graph kernels, is more than a viable alternative to dual approaches, both in terms of speed and classification performance.Comment: Author's version of the manuscript, to appear in Neurocomputing (ELSEVIER

Strategies to Manage Censorship Issues and Controversies in Museums

Museums are poised to educate, engage and entertain patrons, as well as challenge and influence society more now than at any other time throughout the modem history of the museum industry. With thousands of museums throughout the United States attracting hundreds of millions of visitors annually, controversial exhibits and issues of censorship continue to challenge museum industry leaders. Concern surrounding this subject exists in all artistic and cultural endeavors. Topics range from race and religion to war and sexuality, and have occurred in history, science and art museums. This study looks at the best management options available to museum directors confronted by controversy and affected by censorship issues. While each situation differs based on topic, locale and sociopolitical climate, I provide a guide that any organization may use as a starting point to address concerns. I also consider how museums may wish to deal with their audience, including the general public, and the media, as well as special interest groups, and community leaders and political appointees with a variety of agendas. Other areas of analysis I address in this study encompass managing reactions from a variety of sources, including outrage that may result during incidents involving controversial exhibits. Previous studies have analyzed and broken down some of the more recent exhibits that sparked controversy. Some have sought to uncover which exhibition topics ought to be presented, while others have developed recommendations for executing controversial exhibits and how to handle responses to an exhibit once the controversy occurs. I focus on current ways museums are managing such challenges and ideas for devising a plan for better management of these issues in the future, while focusing on innovative trends in the industry. I begin researching this subject by asking two broad questions: 1) What are the attributes necessary to endure a censorship controversy and maintain a viable exhibition? 2) Is it necessary that an exhibition add to the population’s better understanding of a topic, its social and cultural knowledge or the museum’s legacy? I use three organizations and four controversial exhibits, providing case studies of the museum’s actions in response to the public, media and special interest groups’ reactions to the exhibits. The objective of my thesis is to develop recommendations to serve as a template or prototype for managing censorship relating to controversial museum exhibitions. My focus is on how the museum initially handles a given controversial situation. I study whether preplanning for and using a proactive approach to the incident would make a difference. I also seek to answer how museums might manage issues more effectively in the future. Additionally, I look at the basic business aspects of managing such incidences and issues relevant to the administrative leadership perspective. I conduct a literature search and perform a heuristic, comparative case study. I summarize the data collected and compare and contrast the different organization’s programs. The emphasis is on helping organizations steer exhibit topics towards a positive, informed learning opportunity, without yielding to censorship demands or resorting to self-censorship. I show how a museum might benefit from better communication when planning exhibits with controversial subject matter. I note the difficulty in balancing the needs of the museum, its public, and its board members when dealing with divisive subject matter. Although one museum in particular did a better overall job of managing their particular crisis, no organization had all the answers. This organization had a basic approach that could be seen as a general standard with achievable outcomes. In compiling the museums’ responses to confronting censorship and concerns arising from controversial exhibits, I highlight the benefit of using an objective approach towards addressing unforeseen issues as they arise. I develop conclusions and recommendations, taking the best actions from each organization, compare and contrast the different organization’s programs, draw comparisons to each, and illustrate the similarities and differences. This helps me formulate the best possible approach to achieving a proactive, dynamic and enduring plan. Ultimately, I develop a universal action plan and procedure in the Plan, Do, Check, Act format. As a bonus I include detailed instruction on managing outrage if/when it occurs

Team QCRI-MIT at SemEval-2019 Task 4: Propaganda Analysis Meets Hyperpartisan News Detection

In this paper, we describe our submission to SemEval-2019 Task 4 on Hyperpartisan News Detection. Our system relies on a variety of engineered features originally used to detect propaganda. This is based on the assumption that biased messages are propagandistic in the sense that they promote a particular political cause or viewpoint. We trained a logistic regression model with features ranging from simple bag-of-words to vocabulary richness and text readability features. Our system achieved 72.9% accuracy on the test data that is annotated manually and 60.8% on the test data that is annotated with distant supervision. Additional experiments showed that significant performance improvements can be achieved with better feature pre-processing.Comment: Hyperpartisanship, propaganda, news media, fake news, SemEval-201