Not all Humans, Radical Criticism of the Anthropocene Narrative

Earth scientists have declared that we are living in “the Anthropocene,” but radical critics object to the implicit attribution of responsibility for climate disruption to all of humanity. They are right to object. Yet, in effort to implicate their preferred villains, their revised narratives often paint an overly narrow picture. Sharing the impulse of radical critics to tell a more precise and political story about how we arrived where we are today, this paper wagers that collective action is more effectively mobilized when we identify multiple agencies and diverse historical processes as sites in need of urgent intervention

Low-rank Similarity Measure for Role Model Extraction

Computing meaningful clusters of nodes is crucial to analyze large networks. In this paper, we present a pairwise node similarity measure that allows to extract roles, i.e. group of nodes sharing similar flow patterns within a network. We propose a low rank iterative scheme to approximate the similarity measure for very large networks. Finally, we show that our low rank similarity score successfully extracts the different roles in random graphs and that its performances are similar to the pairwise similarity measure.Comment: 7 pages, 2 columns, 4 figures, conference paper for MTNS201

Applications of a Graph Theoretic Based Clustering Framework in Computer Vision and Pattern Recognition

Recently, several clustering algorithms have been used to solve variety of problems from different discipline. This dissertation aims to address different challenging tasks in computer vision and pattern recognition by casting the problems as a clustering problem. We proposed novel approaches to solve multi-target tracking, visual geo-localization and outlier detection problems using a unified underlining clustering framework, i.e., dominant set clustering and its extensions, and presented a superior result over several state-of-the-art approaches.Comment: doctoral dissertatio

Extracting inter-arrival time based behaviour from honeypot traffic using cliques

The Leurre.com project is a worldwide network of honeypot environments that collect traces of malicious Internet traffic every day. Clustering techniques have been utilized to categorize and classify honeypot activities based on several traffic features. While such clusters of traffic provide useful information about different activities that are happening in the Internet, a new correlation approach is needed to automate the discovery of refined types of activities that share common features. This paper proposes the use of packet inter-arrival time (IAT) as a main feature in grouping clusters that exhibit commonalities in their IAT distributions. Our approach utilizes the cliquing algorithm for the automatic discovery of cliques of clusters. We demonstrate the usefulness of our methodology by providing several examples of IAT cliques and a discussion of the types of activity they represent. We also give some insight into the causes of these activities. In addition, we address the limitation of our approach, through the manual extraction of what we term supercliques, and discuss ideas for further improvement