Nuzzer: A Large-Scale Device-Free Passive Localization System for Wireless Environments

The widespread usage of wireless local area networks and mobile devices has fostered the interest in localization systems for wireless environments. The majority of research in the context of wireless-based localization systems has focused on device-based active localization, in which a device is attached to tracked entities. Recently, device-free passive localization (DfP) has been proposed where the tracked entity is neither required to carry devices nor participate actively in the localization process. DfP systems are based on the fact that RF signals are affected by the presence of people and objects in the environment. The DfP concept enables a wide range of applications including intrusion detection and tracking, border protection, and smart buildings automation. Previous studies have focused on small areas with direct line of sight and/or controlled environments. In this paper, we present the design, implementation and analysis of Nuzzer, a large-scale device-free passive localization system for real environments. Without any additional hardware, it makes use of the already installed wireless data networks to monitor and process changes in the received signal strength (RSS) transmitted from access points at one or more monitoring points. We present probabilistic techniques for DfP localization and evaluate their performance in a typical office building, rich in multipath, with an area of 1500 square meters. Our results show that the Nuzzer system gives device-free location estimates with less than 2 meters median distance error using only two monitoring laptops and three access points. This indicates the suitability of Nuzzer to a large number of application domains.Comment: 9 page

A Fine-grained Indoor Location-based Social Network

Existing Location-based social networks (LBSNs), e.g., Foursquare, depend mainly on GPS or cellular-based localization to infer users' locations. However, GPS is unavailable indoors and cellular-based localization provides coarse-grained accuracy. This limits the accuracy of current LBSNs in indoor environments, where people spend 89% of their time. This in turn affects the user experience, in terms of the accuracy of the ranked list of venues, especially for the small screens of mobile devices; misses business opportunities; and leads to reduced venues coverage. In this paper, we present CheckInside: a system that can provide a fine-grained indoor location-based social network. CheckInside leverages the crowd-sensed data collected from users' mobile devices during the check-in operation and knowledge extracted from current LBSNs to associate a place with a logical name and a semantic fingerprint. This semantic fingerprint is used to obtain a more accurate list of nearby places as well as to automatically detect new places with similar signature. A novel algorithm for detecting fake check-ins and inferring a semantically-enriched floorplan is proposed as well as an algorithm for enhancing the system performance based on the user implicit feedback. Furthermore, CheckInside encompasses a coverage extender module to automatically predict names of new venues increasing the coverage of current LBSNs. Experimental evaluation of CheckInside in four malls over the course of six weeks with 20 participants shows that it can infer the actual user place within the top five venues 99% of the time. This is compared to 17% only in the case of current LBSNs. In addition, it increases the coverage of existing LBSNs by more than 37%.Comment: 15 pages, 18 figure

Applying deep learning to classify pornographic images and videos

It is no secret that pornographic material is now a one-click-away from everyone, including children and minors. General social media networks are striving to isolate adult images and videos from normal ones. Intelligent image analysis methods can help to automatically detect and isolate questionable images in media. Unfortunately, these methods require vast experience to design the classifier including one or more of the popular computer vision feature descriptors. We propose to build a classifier based on one of the recently flourishing deep learning techniques. Convolutional neural networks contain many layers for both automatic features extraction and classification. The benefit is an easier system to build (no need for hand-crafting features and classifiers). Additionally, our experiments show that it is even more accurate than the state of the art methods on the most recent benchmark dataset.Comment: PSIVT 2015, the final publication is available at link.springer.co

Asymptotic Freedom in a String Model of High Temperature QCD

Recently we have shown that a phase transition occurs in the leading and sub-leading approximation of the large N limit in rigid strings coupled to long range Kalb-Ramond interactions. The disordered phase is essentially the Nambu-Goto-Polyakov string theory while the ordered phase is a new theory. In this letter we compute the free energy per unit length of the interacting rigid string at finite temperature. We show that the mass of the winding states solves that of QCD strings in the limit of high temperature. We obtain a precise identification of the QCD coupling constant and those of the interacting rigid string. The relation we obtain is $Ng_{QCD}^2=({4\pi^2(D-2)\over 3})^2{1\over 3\kappa}$ where $\kappa={Dt\alpha\over\pi\mu_{c}}$ is the ratio of the extrinsic curvature coupling constant t, the Kalb-Ramond coupling constant $\alpha$, and the critical string tension $\mu_{c}$. The running beta function of $\kappa$ reproduces correctly the asymptotic behaviour of QCD.Comment: PHYZZX, 10 page

The Inverse Problem for Simple Liquid Metals a Study Case on Liquid Aluminum at Melting Point

In an attempt to test the possibility of solving the inverse problem for liquid metals i.e. obtaining the effective pair potential from the experimental structure factor, we solve the modified Hypernetted-Chain Integral equation for liquid aluminum at melting temperature to obtain the effective pair potential starting from the experimental structure factor and compare it with the potential obtained from theoretical considerations. Then we use the potential obtained by solving the inverse problem in Monte Carlo simulation to test it, and the calculated structure factor of the liquid aluminum is compared with experiment. We show that the solution of the inverse problem in such cases gives reasonable quantitative results, and reproduces the general features of the pair potential and the results for the structure factor are not far from the experimental measurements

RCNF: Real-time Collaborative Network Forensic Scheme for Evidence Analysis

Network forensic techniques help in tracking different types of cyber attack by monitoring and inspecting network traffic. However, with the high speed and large sizes of current networks, and the sophisticated philosophy of attackers, in particular mimicking normal behaviour and/or erasing traces to avoid detection, investigating such crimes demands intelligent network forensic techniques. This paper suggests a real-time collaborative network Forensic scheme (RCNF) that can monitor and investigate cyber intrusions. The scheme includes three components of capturing and storing network data, selecting important network features using chi-square method and investigating abnormal events using a new technique called correntropy-variation. We provide a case study using the UNSW-NB15 dataset for evaluating the scheme, showing its high performance in terms of accuracy and false alarm rate compared with three recent state-of-the-art mechanisms

Crescendo: An Infrastructure-free Ubiquitous Cellular Network-based Localization System

A ubiquitous outdoor localization system that is easy to deploy and works equally well for all mobile devices is highly-desirable. The GPS, despite its high accuracy, cannot be reliably used for this purpose since it is not available on low-end phones nor in areas with low satellite coverage. The application of classical fingerprinting approaches, on the other hand, is prohibited by excessive maintenance and deployment costs. In this paper, we propose Crescendo, a cellular network-based outdoor localization system that does not require calibration or infrastructure support. Crescendo builds on techniques borrowed from computational geometry to estimate the user's location. Specifically, given the network cells heard by the mobile device it leverages the Voronoi diagram of the network sites to provide an initial ambiguity area and incrementally reduces this area by leveraging pairwise site comparisons and visible cell information. Evaluation of Crescendo in both an urban and a rural area using real data shows median accuracies of 152m and 224m, respectively. This is an improvement over classical techniques by at least 18% and 15%, respectively

Phase Transition and Absence Of Ghosts in Rigid QED

Ordinary QED formulated in the Feynman's space-time picture is equivalent to a one dimensional field theory. In the large N limit there is no phase transition in such a theory. In this letter, we show a phase transition does exist in a generalization of QED characterized by the addition of the curvature of the world line (rigidity) to the Feynman's space-time action. The large distance scale of the disordered phase essentially coincides with ordinary QED, while the ordered phase is strongly coupled. Although rigid QED exhibits the typical pathologies of higher derivative theories at the classical level, we show that both phases of the quantum theory are free of ghosts and tachyons. Quantum fluctuations prevent taking the naive classical limit and inherting the problems of the classical theory.Comment: 9 pages, 1 figure, phyzzx, to appear in Phys. Lett. B235 (1994), CINCI3-DEC-9

Spot: An accurate and efficient multi-entity device-free WLAN localization system

Device-free (DF) localization in WLANs has been introduced as a value-added service that allows tracking indoor entities that do not carry any devices. Previous work in DF WLAN localization focused on the tracking of a single entity due to the intractability of the multi-entity tracking problem whose complexity grows exponentially with the number of humans being tracked. In this paper, we introduce Spot as an accurate and efficient system for multi-entity DF detection and tracking. Spot is based on a probabilistic energy minimization framework that combines a conditional random field with a Markov model to capture the temporal and spatial relations between the entities' poses. A novel cross-calibration technique is introduced to reduce the calibration overhead of multiple entities to linear, regardless of the number of humans being tracked. This also helps in increasing the system accuracy. We design the energy minimization function with the goal of being efficiently solved in mind. We show that the designed function can be mapped to a binary graph-cut problem whose solution has a linear complexity on average and a third order polynomial in the worst case. We further employ clustering on the estimated location candidates to reduce outliers and obtain more accurate tracking. Experimental evaluation in two typical testbeds, with a side-by-side comparison with the state-of-the-art, shows that Spot can achieve a multi-entity tracking accuracy of less than 1.1m. This corresponds to at least 36% enhancement in median distance error over the state-of-the-art DF localization systems, which can only track a single entity. In addition, Spot can estimate the number of entities correctly to within one difference error. This highlights that Spot achieves its goals of having an accurate and efficient software-only DF tracking solution of multiple entities in indoor environments.Comment: 14 pages, 24 figure

Dejavu: An Accurate Energy-Efficient Outdoor Localization System

We present Dejavu, a system that uses standard cell-phone sensors to provide accurate and energy-efficient outdoor localization suitable for car navigation. Our analysis shows that different road landmarks have a unique signature on cell-phone sensors; For example, going inside tunnels, moving over bumps, going up a bridge, and even potholes all affect the inertial sensors on the phone in a unique pattern. Dejavu employs a dead-reckoning localization approach and leverages these road landmarks, among other automatically discovered abundant virtual landmarks, to reset the accumulated error and achieve accurate localization. To maintain a low energy profile, Dejavu uses only energy-efficient sensors or sensors that are already running for other purposes. We present the design of Dejavu and how it leverages crowd-sourcing to automatically learn virtual landmarks and their locations. Our evaluation results from implementation on different android devices in both city and highway driving show that Dejavu can localize cell phones to within 8.4m median error in city roads and 16.6m on highways. Moreover, compared to GPS and other state-of-the-art systems, Dejavu can extend the battery lifetime by 347%, achieving even better localization results than GPS in the more challenging in-city driving conditions