Alien Registration- Eagle, Olive P. (Portland, Cumberland County)

https://digitalmaine.com/alien_docs/21405/thumbnail.jp

Alien Registration- Eagle, Olive P. (Portland, Cumberland County)

https://digitalmaine.com/alien_docs/21405/thumbnail.jp

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

High resolution nighttime cloud-cover radiometer Quarterly report XVII, 1 Oct. 1965 - 1 Jan. 1966

Electronic, optical, mechanical, and electron packaging component and system design reviews for high resolution cloud cover infrared radiomete

Handling oversampling in dynamic networks using link prediction

Oversampling is a common characteristic of data representing dynamic networks. It introduces noise into representations of dynamic networks, but there has been little work so far to compensate for it. Oversampling can affect the quality of many important algorithmic problems on dynamic networks, including link prediction. Link prediction seeks to predict edges that will be added to the network given previous snapshots. We show that not only does oversampling affect the quality of link prediction, but that we can use link prediction to recover from the effects of oversampling. We also introduce a novel generative model of noise in dynamic networks that represents oversampling. We demonstrate the results of our approach on both synthetic and real-world data.Comment: ECML/PKDD 201

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

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

“Exploring the link between managing cultural heritage and tourism industry competitiveness: a two country comparison”

Purpose of the Paper – Built cultural heritage, such as museums, are deeply linked totheir locations and have a crucial role to play in tourism developments. Yet, theliterature on museum management is focused upon museums without considering thecompetitiveness of the tourism industry where they are located. This paper will seek toaddress this lacuna, and attempt to connect or link museums management andcompetitiveness in the tourism industry.Design – Two samples (most visited U.K. and Italian museums) will be analysed togetherwith the competitiveness of their (local) tourism industry.Findings – Research findings will allow classifying most visited U.K. and Italian museums in clusters. The comparison will reveal country-specific differences andtourism industry competitiveness of regions of most visited Italian and U.K. museums.Practical Implications – The differences in tourism industry competitiveness and themuseums appeal will enable elaboration of specific strategies for museums and thetourism industry for each identifiable cluster. Originality/value – The link between museum marketing strategies and destinationcompetitiveness has been quite neglected by researchers to date. This paper is a firstattempt to address this gap, with regard to U.K. and Italian context.KEY WORDS museums | tourism destination competitiveness | museum marketingstrategies | tourism industry strategie

From Relational Data to Graphs: Inferring Significant Links using Generalized Hypergeometric Ensembles

The inference of network topologies from relational data is an important problem in data analysis. Exemplary applications include the reconstruction of social ties from data on human interactions, the inference of gene co-expression networks from DNA microarray data, or the learning of semantic relationships based on co-occurrences of words in documents. Solving these problems requires techniques to infer significant links in noisy relational data. In this short paper, we propose a new statistical modeling framework to address this challenge. It builds on generalized hypergeometric ensembles, a class of generative stochastic models that give rise to analytically tractable probability spaces of directed, multi-edge graphs. We show how this framework can be used to assess the significance of links in noisy relational data. We illustrate our method in two data sets capturing spatio-temporal proximity relations between actors in a social system. The results show that our analytical framework provides a new approach to infer significant links from relational data, with interesting perspectives for the mining of data on social systems.Comment: 10 pages, 8 figures, accepted at SocInfo201