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Selected Topics in Signal Processing, IEEE Journal of

Issue 4 • Date Aug. 2010

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Displaying Results 1 - 13 of 13
  • Table of contents

    Page(s): C1
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  • IEEE Journal of Selected Topics in Signal Processing publication information

    Page(s): C2
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  • Introduction to the Special Issue on Signal and Information Processing for Social Networks

    Page(s): 673 - 676
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  • Measuring the Collective Potential of Populations From Dynamic Social Interaction Data

    Page(s): 677 - 686
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (652 KB) |  | HTML iconHTML  

    In any society, is the way in which individuals interact, intentionally or unintentionally, designed to maximize global benefit, or does it result in a fundamentally non-egalitarian stratification of society, where a small number of individuals inevitably dominate? Our ability to observe and record interactions between individuals in real populations has improved dramatically with modern technological improvements, but it is still a difficult task to use this data to model cooperation and collaboration between individuals, and its global effect on the entire population. To shed light on these questions, we model an individual's value in society as an epistatic mathematical function of a set of binary choices, and the collective potential of a population as the expected value of an individual over time. Individuals try to selfishly improve their societal value by adopting the choices of their neighbors, constrained by the actual observed interaction topology and order. As a result, we are also able to investigate how far natural populations are from an optimal regime of functioning. We show that interaction topology has a large impact on collective potential, but the relative order of specific interactions seems to have a negligible effect. View full abstract»

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  • Impact of Social Network Structure on Multimedia Fingerprinting Misbehavior Detection and Identification

    Page(s): 687 - 703
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    Users in video-sharing social networks actively interact with each other, and it is of critical importance to model user behavior and analyze the impact of human factors on video sharing systems. In video-sharing social networks, users have access to extra resources from their peers, and they also contribute their own resources to help others. Each user wants to maximize his/her own payoff, and they negotiate with each other to achieve fairness and address this conflict. However, some selfish users may cheat to their peers and manipulate the system to maximize their own payoffs, and cheat prevention is a critical requirement in many social networks to stimulate user cooperation. It is of ample importance to design monitoring mechanisms to detect and identify misbehaving users, and to design cheat-proof cooperation stimulation strategies. Using video fingerprinting as an example, this paper analyzes the complex dynamics among colluders during multiuser collusion, and explores possible monitoring mechanisms to detect and identify misbehaving colluders in multiuser collusion. We consider two types of colluder networks: one has a centralized structure with a trusted ringleader, and the other is a distributed peer-structured network. We investigate the impact of network structures on misbehavior detection and identification, propose different selfish colluder identification schemes for different colluder networks, and analyze their performance. We show that the proposed schemes can accurately identify selfish colluders without falsely accusing others even under attacks. We also evaluate their robustness against framing attacks and quantify the maximum number of framing colluders that they can resist. View full abstract»

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  • A Game Theoretic Analysis of Incentives in Content Production and Sharing Over Peer-to-Peer Networks

    Page(s): 704 - 717
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (852 KB) |  | HTML iconHTML  

    Peer-to-peer (P2P) networks can be easily deployed to distribute user-generated content at a low cost, but the free-rider problem hinders the efficient utilization of P2P networks. Using game theory, we investigate incentive schemes to overcome the free-rider problem in content production and sharing. We build a basic model and obtain two benchmark outcomes: 1) the non-cooperative outcome without any incentive scheme and 2) the cooperative outcome. We then propose and examine three incentive schemes based on pricing, reciprocation, and intervention. We also study a brute-force scheme that enforces full sharing of produced content. We find that 1) cooperative peers share all produced content while non-cooperative peers do not share at all without an incentive scheme; 2) by utilizing the P2P network efficiently, the cooperative outcome achieves higher social welfare than the non-cooperative outcome does; 3) a cooperative outcome can be achieved among non-cooperative peers by introducing an incentive scheme based on pricing, reciprocation, or intervention; and 4) enforced full sharing has ambiguous welfare effects on peers. In addition to describing the solutions of different formulations, we discuss enforcement and informational requirements to implement each solution, aiming to offer a guideline for protocol design for P2P networks. View full abstract»

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  • Distance-Dependent Kronecker Graphs for Modeling Social Networks

    Page(s): 718 - 731
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (876 KB) |  | HTML iconHTML  

    This paper focuses on a generalization of stochastic Kronecker graphs, introducing a Kronecker-like operator and defining a family of generator matrices H dependent on distances between nodes in a specified graph embedding. We prove that any lattice-based network model with sufficiently small distance-dependent connection probability will have a Poisson degree distribution and provide a general framework to prove searchability for such a network. Using this framework, we focus on a specific example of an expanding hypercube and discuss the similarities and differences of such a model with recently proposed network models based on a hidden metric space. We also prove that a greedy forwarding algorithm can find very short paths of length O((log log n)2) on the hypercube with n nodes, demonstrating that distance-dependent Kronecker graphs can generate searchable network models. View full abstract»

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  • Context-Adaptive Information Flow Allocation and Media Delivery in Online Social Networks

    Page(s): 732 - 745
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1718 KB) |  | HTML iconHTML  

    This paper investigates context-driven flow allocation and media delivery in online social networks. We exploit information on contacts and content preferences found in social networking applications to provide efficient network services and operation at the underlying transport layer. We formulate a linear programming framework that maximizes the information flow–cost ratio of the transport network serving the nodes in the social graph. For practical deployments, we also design a distributed version of the optimization framework that provides similar performance to its centralized counterpart, with lower complexity. In addition, we devise a tracker-based system for efficient content discovery in peer-to-peer (P2P) systems based on social network information. Finally, we design a context-aware packet scheduling technique that maximizes the utility of media delivery among the members of the social network. We provide a comprehensive investigation of the performance of our optimization strategies through both simulations and analysis. We demonstrate their significant advantages over several performance factors relative to conventional solutions that do not employ social network information in their operation. View full abstract»

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  • Probabilistic Mining of Socio-Geographic Routines From Mobile Phone Data

    Page(s): 746 - 755
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    There is relatively little work on the investigation of large-scale human data in terms of multimodality for human activity discovery. In this paper, we suggest that human interaction data, or human proximity, obtained by mobile phone Bluetooth sensor data, can be integrated with human location data, obtained by mobile cell tower connections, to mine meaningful details about human activities from large and noisy datasets. We propose a model, called bag of multimodal behavior, that integrates the modeling of variations of location over multiple time-scales, and the modeling of interaction types from proximity. Our representation is simple yet robust to characterize real-life human behavior sensed from mobile phones, which are devices capable of capturing large-scale data known to be noisy and incomplete. We use an unsupervised approach, based on probabilistic topic models, to discover latent human activities in terms of the joint interaction and location behaviors of 97 individuals over the course of approximately a 10-month period using data from MIT's Reality Mining project. Some of the human activities discovered with our multimodal data representation include “going out from 7 pm-midnight alone” and “working from 11 am-5 pm with 3-5 other people,” further finding that this activity dominantly occurs on specific days of the week. Our methodology also finds dominant work patterns occurring on other days of the week. We further demonstrate the feasibility of the topic modeling framework for human routine discovery by predicting missing multimodal phone data at specific times of the day. View full abstract»

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  • The Use of Mobile Devices in Aiding Dietary Assessment and Evaluation

    Page(s): 756 - 766
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1208 KB) |  | HTML iconHTML  

    There is a growing concern about chronic diseases and other health problems related to diet including obesity and cancer. The need to accurately measure diet (what foods a person consumes) becomes imperative. Dietary intake provides valuable insights for mounting intervention programs for prevention of chronic diseases. Measuring accurate dietary intake is considered to be an open research problem in the nutrition and health fields. In this paper, we describe a novel mobile telephone food record that will provide an accurate account of daily food and nutrient intake. Our approach includes the use of image analysis tools for identification and quantification of food that is consumed at a meal. Images obtained before and after foods are eaten are used to estimate the amount and type of food consumed. The mobile device provides a unique vehicle for collecting dietary information that reduces the burden on respondents that are obtained using more classical approaches for dietary assessment. We describe our approach to image analysis that includes the segmentation of food items, features used to identify foods, a method for automatic portion estimation, and our overall system architecture for collecting the food intake information. View full abstract»

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  • IEEE Journal of Selected Topics in Signal Processing Information for authors

    Page(s): 767 - 768
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  • IEEE Signal Processing Society Information

    Page(s): C3
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  • Blank page [back cover]

    Page(s): C4
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Aims & Scope

The Journal of Selected Topics in Signal Processing (J-STSP) solicits special issues on topics that cover the entire scope of the IEEE Signal Processing Society including the theory and application of filtering, coding, transmitting, estimating, detecting, analyzing, recognizing, synthesizing, recording, and reproducing signals by digital or analog devices or techniques.

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
Fernando Pereira
Instituto Superior Técnico