Local Causal States and Discrete Coherent Structures

Coherent structures form spontaneously in nonlinear spatiotemporal systems and are found at all spatial scales in natural phenomena from laboratory hydrodynamic flows and chemical reactions to ocean, atmosphere, and planetary climate dynamics. Phenomenologically, they appear as key components that organize the macroscopic behaviors in such systems. Despite a century of effort, they have eluded rigorous analysis and empirical prediction, with progress being made only recently. As a step in this, we present a formal theory of coherent structures in fully-discrete dynamical field theories. It builds on the notion of structure introduced by computational mechanics, generalizing it to a local spatiotemporal setting. The analysis' main tool employs the \localstates, which are used to uncover a system's hidden spatiotemporal symmetries and which identify coherent structures as spatially-localized deviations from those symmetries. The approach is behavior-driven in the sense that it does not rely on directly analyzing spatiotemporal equations of motion, rather it considers only the spatiotemporal fields a system generates. As such, it offers an unsupervised approach to discover and describe coherent structures. We illustrate the approach by analyzing coherent structures generated by elementary cellular automata, comparing the results with an earlier, dynamic-invariant-set approach that decomposes fields into domains, particles, and particle interactions.Comment: 27 pages, 10 figures; http://csc.ucdavis.edu/~cmg/compmech/pubs/dcs.ht

Urban detection, delimitation and morphology: comparative analysis of selective "megacities"

Postprint (published version

Discriminating different classes of biological networks by analyzing the graphs spectra distribution

The brain's structural and functional systems, protein-protein interaction, and gene networks are examples of biological systems that share some features of complex networks, such as highly connected nodes, modularity, and small-world topology. Recent studies indicate that some pathologies present topological network alterations relative to norms seen in the general population. Therefore, methods to discriminate the processes that generate the different classes of networks (e.g., normal and disease) might be crucial for the diagnosis, prognosis, and treatment of the disease. It is known that several topological properties of a network (graph) can be described by the distribution of the spectrum of its adjacency matrix. Moreover, large networks generated by the same random process have the same spectrum distribution, allowing us to use it as a "fingerprint". Based on this relationship, we introduce and propose the entropy of a graph spectrum to measure the "uncertainty" of a random graph and the Kullback-Leibler and Jensen-Shannon divergences between graph spectra to compare networks. We also introduce general methods for model selection and network model parameter estimation, as well as a statistical procedure to test the nullity of divergence between two classes of complex networks. Finally, we demonstrate the usefulness of the proposed methods by applying them on (1) protein-protein interaction networks of different species and (2) on networks derived from children diagnosed with Attention Deficit Hyperactivity Disorder (ADHD) and typically developing children. We conclude that scale-free networks best describe all the protein-protein interactions. Also, we show that our proposed measures succeeded in the identification of topological changes in the network while other commonly used measures (number of edges, clustering coefficient, average path length) failed

Enhancing security incident response follow-up efforts with lightweight agile retrospectives

Security incidents detected by organizations are escalating in both scale and complexity. As a result, security incident response has become a critical mechanism for organizations in an effort to minimize the damage from security incidents. The final phase within many security incident response approaches is the feedback/follow-up phase. It is within this phase that an organization is expected to use information collected during an investigation in order to learn from an incident, improve its security incident response process and positively impact the wider security environment. However, recent research and security incident reports argue that organizations find it difficult to learn from incidents. A contributing factor to this learning deficiency is that industry focused security incident response approaches, typically, provide very little practical information about tools or techniques that can be used to extract lessons learned from an investigation. As a result, organizations focus on improving technical security controls and not examining or reassessing the effectiveness or efficiency of internal policies and procedures. An additional hindrance, to encouraging improvement assessments, is the absence of tools and/or techniques that organizations can implement to evaluate the impact of implemented enhancements in the wider organization. Hence, this research investigates the integration of lightweight agile retrospectives and meta-retrospectives, in a security incident response process, to enhance feedback and/or follow-up efforts. The research contribution of this paper is twofold. First, it presents an approach based on lightweight retrospectives as a means of enhancing security incident response follow-up efforts. Second, it presents an empirical evaluation of this lightweight approach in a Fortune 500 Financial organization's security incident response team

Aligning Community Colleges to Their Local Labor Markets

Examines ways to better align community college curricula with employer needs, including analyzing online job ads to gather data on occupation and skill demands; examples of use of labor market information; and the potential and limitations of such data

Construction IT in 2030: a scenario planning approach

Summary: This paper presents a scenario planning effort carried out in order to identify the possible futures that construction industry and construction IT might face. The paper provides a review of previous research in the area and introduces the scenario planning approach. It then describes the adopted research methodology. The driving forces of change and main trends, issues and factors determined by focusing on factors related to society, technology, environment, economy and politics are discussed. Four future scenarios developed for the year 2030 are described. These scenarios start from the global view and present the images of the future world. They then focus on the construction industry and the ICT implications. Finally, the preferred scenario determined by the participants of a prospective workshop is presented

Comparing and Combining Sentiment Analysis Methods

Several messages express opinions about events, products, and services, political views or even their author's emotional state and mood. Sentiment analysis has been used in several applications including analysis of the repercussions of events in social networks, analysis of opinions about products and services, and simply to better understand aspects of social communication in Online Social Networks (OSNs). There are multiple methods for measuring sentiments, including lexical-based approaches and supervised machine learning methods. Despite the wide use and popularity of some methods, it is unclear which method is better for identifying the polarity (i.e., positive or negative) of a message as the current literature does not provide a method of comparison among existing methods. Such a comparison is crucial for understanding the potential limitations, advantages, and disadvantages of popular methods in analyzing the content of OSNs messages. Our study aims at filling this gap by presenting comparisons of eight popular sentiment analysis methods in terms of coverage (i.e., the fraction of messages whose sentiment is identified) and agreement (i.e., the fraction of identified sentiments that are in tune with ground truth). We develop a new method that combines existing approaches, providing the best coverage results and competitive agreement. We also present a free Web service called iFeel, which provides an open API for accessing and comparing results across different sentiment methods for a given text.Comment: Proceedings of the first ACM conference on Online social networks (2013) 27-3

Emotions in context: examining pervasive affective sensing systems, applications, and analyses

Pervasive sensing has opened up new opportunities for measuring our feelings and understanding our behavior by monitoring our affective states while mobile. This review paper surveys pervasive affect sensing by examining and considering three major elements of affective pervasive systems, namely; “sensing”, “analysis”, and “application”. Sensing investigates the different sensing modalities that are used in existing real-time affective applications, Analysis explores different approaches to emotion recognition and visualization based on different types of collected data, and Application investigates different leading areas of affective applications. For each of the three aspects, the paper includes an extensive survey of the literature and finally outlines some of challenges and future research opportunities of affective sensing in the context of pervasive computing