An empirical analysis of a submarine motion model

This report describes an empirical analysis of a motion model that has been used to generate random submarine tracks for an antisubmarine warfare tactical decision aid. The model describes a submarine's motion as a series of transitions between the square cells of a grid that covers a defined operating region. A 3 x 3 transition matrix is associated with each cell of the grid which determines the submarine's transitions from a cell. The set of transition matrices define a Markov process. Despite its discrete nature, this Markov track generating process has been called a diffusion process in antisubmarine warfare tactical decision aid literature. The transition matrices are determined by tracks generated by an auxiliary stochastic process that is presumed to be of higher fidelity but more costly to implement than the Markov processPrepared for: Naval Air Development Center, Johnsville, Pennsylvaniahttp://archive.org/details/empiricalanalysi00forrO&MN, Direct FundingN

Correlating Pedestrian Flows and Search Engine Queries

An important challenge for ubiquitous computing is the development of techniques that can characterize a location vis-a-vis the richness and diversity of urban settings. In this paper we report our work on correlating urban pedestrian flows with Google search queries. Using longitudinal data we show pedestrian flows at particular locations can be correlated with the frequency of Google search terms that are semantically relevant to those locations. Our approach can identify relevant content, media, and advertisements for particular locations.Comment: 4 pages, 1 figure, 1 tabl

An approximate solution technique for the constrained search path moving target search problem

A search is conducted for a target moving in discrete time among a finite number of cells according to a known Markov process. The searcher must choose one cell in which to search in each time period. The set of cells from which he can choose is a function of the cell chosen in the previous time period. The problem is to find a searcher path, i.e., a sequence of search cells, that minimizes the probability of not detecting the target in a fixed number of time periods. The problem is formulated as a nonlinear program and solved for a local optimum by a simple implementation of the convex simplex methodNaval Postgraduate School, Monterey, CA.http://archive.org/details/approximatesolut15eaglNaval Postgraduate School, Monterey, CA.NAApproved for public release; distribution is unlimited

Eigenbehaviors: Identifying Structure in Routine

Longitudinal behavioral data generally contains a significant amount of structure. In this work, we identify the structure inherent in daily behavior with models that can accurately analyze, predict, and cluster multimodal data from individuals and communities within the social network of a population. We represent this behavioral structure by the principal components of the complete behavioral dataset, a set of characteristic vectors we have termed eigenbehaviors. In our model, an individual’s behavior over a specific day can be approximated by a weighted sum of his or her primary eigenbehaviors. When these weights are calculated halfway through a day, they can be used to predict the day’s remaining behaviors with 79% accuracy for our test subjects. Additionally, we demonstrate the potential for this dimensionality reduction technique to infer community affiliations within the subjects’ social network by clustering individuals into a “behavior space” spanned by a set of their aggregate eigenbehaviors. These behavior spaces make it possible to determine the behavioral similarity between both individuals and groups, enabling 96% classification accuracy of community affiliations within the population-level social network. Additionally, the distance between individuals in the behavior space can be used as an estimate for relational ties such as friendship, suggesting strong behavioral homophily amongst the subjects. This approach capitalizes on the large amount of rich data previously captured during the Reality Mining study from mobile phones continuously logging location, proximate phones, and communication of 100 subjects at MIT over the course of 9 months. As wearable sensors continue to generate these types of rich, longitudinal datasets, dimensionality reduction techniques such as eigenbehaviors will play an increasingly important role in behavioral research

Close relationships: A study of mobile communication records

Mobile phone communication as digital service generates ever-increasing datasets of human communication actions, which in turn allow us to investigate the structure and evolution of social interactions and their networks. These datasets can be used to study the structuring of such egocentric networks with respect to the strength of the relationships by assuming direct dependence of the communication intensity on the strength of the social tie. Recently we have discovered that there are significant differences between the first and further "best friends" from the point of view of age and gender preferences. Here we introduce a control parameter $p_{\rm max}$ based on the statistics of communication with the first and second "best friend" and use it to filter the data. We find that when $p_{\rm max}$ is decreased the identification of the "best friend" becomes less ambiguous and the earlier observed effects get stronger, thus corroborating them.Comment: 11 pages, 7 figure

Alterity & sensitivity in inter-organizational relations: contours of the tutor in marketing ethics education.

Purpose & literature addressed: This paper scrutinises the way in which ethics is taught in the modern business/industrial marketing syllabus. We argue for a reappraisal of the tutor-student relationship such that we may facilitate a greater understanding of how marketing students can make sense of themselves and of ‘the other’ within industrial networks. Research method: This paper is conceptual in its approach. Drawing on literature from the history of marketing thought, educational philosophy and the work of Emmanuel Levinas, we suggest that the conceptualisation of ethics in marketing cannot be divorced from the question of pedagogy and the responsibilities of the tutor. Research findings: We suggest that the ideas of alterity and proximity offers space for a discussion of justice within the global supply chain, providing entry into the marketing discourse for those members of the industrial network not normally encountered by students in the course of teaching. Main contribution: Importantly for teachers of inter-organizational relationships, Levinas offers an opportunity to simultaneously re-imagine the relationship between the student and the tutor. In the process we are forced to confront and acknowledge the responsibility that the role of a moral mediator entails

Alterity and sensitivity: contours of the tutor in marketing ethics education.

This paper attempts to (re)plot the contours of the Tutor by scrutinising the way in which ethics is taught in the modern marketing syllabus. We open up a debate on how the Tutor role as a conduit of apparent ethical knowledge to students has somehow failed to map with sufficient sensitivity the terrain of the moral impulse in business practice. In particular, we argue for a reappraisal of the Tutor/student relationship such that we may facilitate a greater understanding of how marketing students can make sense of themselves and of ‗the other‘. Drawing on literature from educational philosophy and the work of Emmanuel Levinas, we suggest that the conceptualisation of ethics in marketing cannot be divorced from the question of pedagogy and the responsibilities of the tutor. Whilst the largely conventional model adopted for the teaching of marketing may provide students with a prescribed set of knowledge and skills, it may by the same token refuse us the moral education that seems to be necessary. The paper concludes that that recent economic problems offer an opportunity for a reappraisal of the teaching of marketing ethics. It is an opportunity to re-imagine the relationship between the student and the tutor

Semantics, sensors, and the social web: The live social semantics experiments

The Live Social Semantics is an innovative application that encourages and guides social networking between researchers at conferences and similar events. The application integrates data and technologies from the Semantic Web, online social networks, and a face-to-face contact sensing platform. It helps researchers to find like-minded and influential researchers, to identify and meet people in their community of practice, and to capture and later retrace their real-world networking activities at conferences. The application was successfully deployed at two international conferences, attracting more than 300 users in total. This paper describes this application, and discusses and evaluates the results of its two deployment

Mobile Communication Signatures of Unemployment

The mapping of populations socio-economic well-being is highly constrained by the logistics of censuses and surveys. Consequently, spatially detailed changes across scales of days, weeks, or months, or even year to year, are difficult to assess; thus the speed of which policies can be designed and evaluated is limited. However, recent studies have shown the value of mobile phone data as an enabling methodology for demographic modeling and measurement. In this work, we investigate whether indicators extracted from mobile phone usage can reveal information about the socio-economical status of microregions such as districts (i.e., average spatial resolution < 2.7km). For this we examine anonymized mobile phone metadata combined with beneficiaries records from unemployment benefit program. We find that aggregated activity, social, and mobility patterns strongly correlate with unemployment. Furthermore, we construct a simple model to produce accurate reconstruction of district level unemployment from their mobile communication patterns alone. Our results suggest that reliable and cost-effective economical indicators could be built based on passively collected and anonymized mobile phone data. With similar data being collected every day by telecommunication services across the world, survey-based methods of measuring community socioeconomic status could potentially be augmented or replaced by such passive sensing methods in the future