70 research outputs found
Improving performance, privacy and relevance of location-based services for mobile users
No full textLocation-based services are becoming increasingly popular due to the ubiquity of smartphones and the emergence of vehicular computing applications. Mobile clients have traditionally been consumers of location-based services, but various social applications have recently demonstrated that mobile clients can also be producers of location-based data. Our thesis is that the quality of the location-based services critically depends on the performance of the service, the privacy assurances offered to the clients, and the quality of the data provided by the service. In this dissertation, we propose three contributions addressing key aspects of these challenges: network performance, location privacy for the mobile clients, and relevance of data provided by the service. With respect to the wireless network performance for location-based services, we present Context-Aware Rate Selection (CARS), a rate adaptation algorithm that makes use of knowledge of speed and distance between communicating nodes to choose the optimum transmission rate. Our experimental evaluation in real outdoor vehicular environments shows that CARS adapts to changing link conditions at high vehicular speeds significantly faster than existing rate-adaptation algorithms. With respect to the client location privacy, we present SybilQuery, a fully decentralized and autonomous k-anonymity-based scheme that generates synthetic queries that resemble a real client query. Our experiments on real mobility traces of approximately 500 cabs in the San Francisco Bay area show that SybilQuery can efficiently generate synthetic queries and that these queries are indistinguishable from real queries. Finally, with respect to improving the relevance of location-based data, we present SocialTelescope, a novel location-based service that leverages user interactions in mobile social networks to infer people's preference for places. Our results evaluating the coverage and relevance of our system in comparison to current state-of-the-art approaches show that our approach returns results that are at least as relevant as those returned by current approaches, at a substantially lower cost. The main conclusion of this dissertation is that location-based services can become truly ubiquitous services by providing mobile clients with good network performance, privacy guarantees, as well as relevant results.Ph. D.Includes bibliographical referencesby Pravin Shanka
Data-Driven Approach for Modeling the Mixed Traffic Conditions Using Supervised Machine Learning
No full textThe article describes modeling vehicular movements using supervised machine learning algorithms with trajectory data from heterogeneous non-lane-based traffic conditions. The trajectory data on the mid-block road section of around 540 m is used in the study. Supervised machine learning algorithms are employed to model the vehicular positions. A set of parameters were identified for modeling the longitudinal and lateral positions. With the set of parameters, the algorithm’s potentiality for mimicking vehicular positions is evaluated. It was identified that supervised machine learning algorithms would model the vehicles’ positions with accuracy in the range of 20–60 mean absolute percentage error. The k-NN algorithm was marginally edging past all algorithms and acted as a promising candidate for modeling vehicular positions.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Transport and Plannin
Author Correction: The isocyanide SN2 reaction
No full textIn this article the author name Katarzyna Kurpiewska was incorrectly written as Katarzyna Kurpiewsk. The original article has been corrected.</p
Privately Querying Location-based Services with SybilQuery
No full textTo usefully query a location-based service, a mobile device must typically present its own location in its query to the server. This may not be acceptable to clients that wish to protect the privacy of their location. Much prior work has addressed the problem of protecting client privacy in such location-based queries using k-anonymity. In such schemes, the location-based server is unable to distinguish a querying client from a group of k clients. However, prior work on k-anonymity-based schemes has typically required the use of an anonymizer— a proxy that intercepts and modifies client queries so as to achieve k-anonymity. The centralized nature of anonymizers makes them a single point of failure. Moreover, in the presence of mobile clients, anonymizers must be implemented so as to avoid correlation attacks, where an adversary compromises client location using that client’s queries from multiple locations. Alternatives that eliminate the anonymizer either rely on the participation of k other peers, thus making the system reliant on these peers, or are based upon computationally-expensive cryptographic protocols that present scalability problems. This paper presents the design and implementation of SybilQuery, a fully decentralized and autonomous k-anonymity-based scheme to privately query location-based services. SybilQuery is a clientside tool that generates k − 1 Sybil queries for each query by the client. The location-based server is presented with a set of k queries and is unable to distinguish between the client’s query and the Sybil queries, thereby achieving k-anonymity. We tested our implementation of SybilQuery on real mobility traces of approximately 500 cabs in the San Francisco Bay area. Our experiments show that SybilQuery can efficiently generate Sybil queries and that these queries are indistinguishable from real queries.Technical report DCS-TR-65
Emergence of hierarchy in directed online social networks
No full textSocial hierarchy and stratification among humans is a well studied concept in sociology. The popularity of online social networks presents an opportunity to study social hierarchy for different types of people, and at different scales. We conjecture that people form connections in social network based on their perceived social hierarchy; as a result, the edge directions in directed social networks can be leveraged to infer hierarchy. In this paper, we define a measure of hierarchy in a directed social network, and present an efficient algorithm to compute this measure. We validate our measure using ground truths including Wikipedia notability score. We use this measure to study hierarchy in several directed online social networks including Twitter, Delicious, Flickr, and curated lists of several types of people based on different occupations, and different organizations. Our experiments how hierarchy emerges as the networks grows on different online social networks. We show that the degree of stratification in a network is bounded and does not increase after the graph has become large.Technical report DCS-TR-67
SBone: Personal Device Sharing Using Social Networks
No full textPeople own a number of personal devices whose state and resources might be of interest to others. Owners of these devices may be willing to selectively share them with their family, friends, and colleagues, but there exists no easy and secure mechanism to do so. In this paper, we propose SBone, an architecture that allows personal devices to share their resources and state with each other, seamlessly and securely, using a social network for authentication, naming, discovery and access control. The sharing mechanism is derived from the inter-personal relationships that exists among owners by virtue of their presence in online social networks. We present a case study of how SBone can be used to share internet connectivity resource between devices.Technical report DCS-TR-66
Machining parameters optimization during machining of Al/5 wt% alumina metal matrix composite by fiber laser
No full textA convex optimization approach for moderately large deflection problems in circular membranes and circular paths
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Previous issue date: 1996Item marked as restricted to the 'UIUC Users [automated]' Group (id=2) by Howard Ding ([email protected]) on 2011-05-07T14:43:52Z
Item is restricted indefinitely.Circular membranes and circular plates are used in several types of equipments. For the better design of these equipments an accurate computation of the stresses and the displacements in circular membranes and circular plates are very important. Since larger loads on membranes (plates) leads to stresses as nonlinear functions of displacements, little is known about the existence and the uniqueness of the solution even for the case where a circular membrane (plate) is subjected to axisymmetric transverse loads and axisymmetric boundary conditions.Membranes are a limiting case of the plates where the bending stiffness of the plate goes to zero. In this thesis, a variational formulation is obtained for the problem of moderately large deflections in circular membranes subjected to axisymmetric transverse loads and axisymmetric boundary conditions. For this variational formulation, unique stresses and displacements are shown to exist under quite general loading conditions. And, the variational formulation is cast as a constrained convex optimization problem. Furthermore, an algorithm for computing the stresses and a procedure for obtaining the displacements from the stresses is given.Unique stresses and displacements are also shown to exist for the problem of moderately large deflections in circular plates subjected to axisymmetric transverse loads and axisymmetric boundary conditions. And, an algorithm for computing the stresses and the corresponding displacements from the stresses is given.Restriction data tranferred 2014-07-01T11:19:14-05:00
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The Quest for Citations: Drivers of Article Impact
No full textWhy do some articles become building blocks for future scholars, while many others remain unnoticed? We aim to answer this question by contrasting, synthesizing and simultaneously testing three scientometric perspectives – universalism, social constructivism and presentation – on the influence of article and author characteristics on article citations. To do so, we study all articles published in a sample of five major journals in marketing from 1990 to 2002 that are central to the discipline. We count the number of citations each of these articles has received and regress this count on an extensive set of characteristics of the article (i.e. article quality, article domain, title length, the use of attention grabbers and expositional clarity), and the author (i.e. author visibility and author personal promotion). We find that the number of citations an article in the marketing discipline receives, depends upon “what one says†(quality and domain), on “who says it†(author visibility and personal promotion) and not so much on “how one says it†(title length, the use of attention grabbers, and expositional clarity). Our insights contribute to the marketing literature and are relevant to scientific stakeholders, such as the management of scientific journals and individual academic scholars, as they strive to maximize citations. They are also relevant to marketing practitioners. They inform practitioners on characteristics of the academic journals in marketing and their relevance to decisions they face. On the other hand, they also raise challenges towards making our journals accessible and relevant to marketing practitioners: (1) authors visible to academics are not necessarily visible to practitioners; (2) the readability of an article may hurt academic credibility and impact, while it may be instrumental in influencing practitioners; (3) it remains questionable whether articles that academics assess to be of high quality are also managerially relevant.Impact;Citation Analysis;Referencing;Scientometrics;Cite
RoadSpeak: Enabling Voice Chat on Roadways using Vehicular Social Networks
No full textA great number of people spend one or more hours each day driving between home and the office. These daily roadway commutes are highly predictable and regular, and provide a great opportunity to form virtual mobile communities. However, even though these commuters are already physically present in the same location, they are limited in their ability to communicate with each other. This paper presents a framework for building such communities, which we call Vehicular Social Networks (VSNs), to facilitate better communication between commuters driving on highways. As a proof of concept, we present the design of RoadSpeak, a VSN-based system which allows drivers to automatically join VSNs along popular highways and roadways, and communicate with each other by means of voice chat messages.Technical report DCS-TR-62
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