This paper describes a model that we submitted to W-NUT 2016 Shared task #1: Geolocation Prediction in Twitter. Our model classifies a tweet or a user to a city using a simple neural networks structure with fully-connected layers and average pooling processes. From the findings of previous geolocation prediction approaches, we integrated various user metadata along with message texts and traine...

Text-based geolocation classifiers often operate with a grid-based view of the world. Predicting document location of origin based on text content on a geodesic grid is computationally attractive since many standard methods for supervised document classification carry over unchanged to geolocation in the form of predicting a most probable grid cell for a document. However, the grid-based approa...

In this paper we use UWB measurements for bandwidths up to 3GHz to present a framework for statistical modeling of the indoor radio channel propagation characteristics that are pertinent to precise indoor geolocation using time-of-arrival (TOA) estimations. Accuracy of indoor geolocation systems relies on the strength and TOA of the direct path (DP) in the channel profile. Based on UWB measurem...

We explain a newly found vulnerability that allows circumvention of commonly used delay-based geolocation techniques that use ping or traceroute to sample delays. Attacks may leverage the echo request/reply type of the ICMP protocol. ICMP’s echo request/reply protocol does not specify a mechanism to measure the delays between network nodes. Consequently, different implementations exist on diffe...

Inferring the Internet Point of Presence (PoP) level maps is gaining interest due to its importance to many areas, e.g., for tracking and studying properties of the Internet. In this chapter we survey research towards the generation of PoP level maps. The chapter introduces different approaches to automatically classify IP addresses to PoPs and discusses their strengths and weaknesses. Special ...

We propose a simple yet effective textbased user geolocation model based on a neural network with one hidden layer, which achieves state of the art performance over three Twitter benchmark geolocation datasets, in addition to producing word and phrase embeddings in the hidden layer that we show to be useful for detecting dialectal terms. As part of our analysis of dialectal terms, we release DA...

We present a data-driven approach for Twitter geolocation and regional classification. Our method is based on sparse coding and dictionary learning, an unsupervised method popular in computer vision and pattern recognition. Through a series of optimization steps that integrate information from both feature and raw spaces, and enhancements such as PCA whitening, feature augmentation, and voting-...

Detecting wireless network intruders is challenging since logical addressing information may be spoofed and the attacker may be located anywhere within radio range. Accurate indoor geolocation provides a method by which the physical location of rogue wireless devices may be pinpointed whilst providing an additional option for location-based access control. Existing methods for geolocation using...

The development of future geolocation systems requires a fundamental understanding of the importance of various system parameters, such as the number of sensors, the SNR, bandwidth, and channel conditions. We consider the bounds on time-based geolocation accuracy when all sensors experience non-line-of-sight (NLOS) conditions. While location accuracy generally improves with additional bandwidth...

Geolocation, the process of identifying the precise location in the world where a photo or video was taken, is central to many types of investigative work, from debunking fake news posted on social media to locating terrorist training camps. Professional geolocation is often a manual, timeconsuming process that involves searching large areas of satellite imagery for potential matches. In this p...