Inspired by empirical studies of networked systems such as the Internet, social networks, and biological networks, researchers have in recent years developed a variety of techniques and models to help us understand or predict the behavior of these systems. Here we review developments in this field, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.

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The Structure and Function of Complex Networks

Recommender systems apply knowledge discovery techniques to the problem of making personalized recommendations for information, products or services during a live interaction. These systems, especially the k-nearest neighbor collaborative ltering based ones, are achieving widespread success on the Web. The tremendous growth in the amount of available information and the number of visitors to Web sites in recent years poses some key challenges for recommender systems. These are: producing high quality recommendations, performing many recommendations per second for millions of users and items and achieving high coverage in the face of data sparsity. In traditional collaborative ltering systems the amount of work increases with the number of participants in the system. New recommender system technologies are needed that can quickly produce high quality recommendations, even for very large-scale problems. To address these issues we have explored item-based collaborative ltering techniques. Item-based techniques rst analyze the user-item matrix to identify relationships between di erent items, and then use these relationships to indirectly compute recommendations for users. In this paper we analyze di erent item-based recommendation generation algorithms. We look into di erent techniques for computing item-item similarities (e.g., item-item correlation vs. cosine similarities between item vectors) and di erent techniques for obtaining recommendations from them (e.g., weighted sum vs. regression model). Finally, we experimentally evaluate our results and compare them to the basic k-nearest neighbor approach. Our experiments suggest that item-based algorithms provide dramatically better performance than user-based algorithms, while at the same time providing better quality than the best available userbased algorithms.

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Item-based collaborative filtering recommendation algorithms

PROBLEM TO BE SOLVED: To solve the problem, wherein it is impossible for an electronic content information provider to provide commercially secure and effective method, for a configurable general-purpose electronic commercial transaction/distribution control system. SOLUTION: In this system, having at least one protected processing environment for safely controlling at least one portion of decoding of digital information, a secure content distribution method comprises a process for encapsulating digital information in one or more digital containers; a process for encrypting at least a portion of digital information; a process for associating at least partially secure control information for managing interactions with encrypted digital information and/or digital container; a process for delivering one or more digital containers to a digital information user; and a process for using a protected processing environment, for safely controlling at least a portion of the decoding of the digital information. COPYRIGHT: (C)2006,JPO&NCIPI

https://apps.dtic.mil/dtic/tr/fulltext/u2/a423264.pdf

Systems and Methods for Secure Transaction Management and Electronic Rights Protection

Numerous systems have been designed which use virtualization to subdivide the ample resources of a modern computer. Some require specialized hardware, or cannot support commodity operating systems. Some target 100% binary compatibility at the expense of performance. Others sacrifice security or functionality for speed. Few offer resource isolation or performance guarantees; most provide only best-effort provisioning, risking denial of service.This paper presents Xen, an x86 virtual machine monitor which allows multiple commodity operating systems to share conventional hardware in a safe and resource managed fashion, but without sacrificing either performance or functionality. This is achieved by providing an idealized virtual machine abstraction to which operating systems such as Linux, BSD and Windows XP, can be ported with minimal effort.Our design is targeted at hosting up to 100 virtual machine instances simultaneously on a modern server. The virtualization approach taken by Xen is extremely efficient: we allow operating systems such as Linux and Windows XP to be hosted simultaneously for a negligible performance overhead --- at most a few percent compared with the unvirtualized case. We considerably outperform competing commercial and freely available solutions in a range of microbenchmarks and system-wide tests.

/pdf/xen-and-the-art-of-virtualization-vt3kaikb6y.pdf

Xen and the art of virtualization

http://www.bmcommunity.sitew.com/fs/Root/8jig8-businessmodelsbusinessstrategy.pdf

Business Models, Business Strategy and Innovation

Systems, methods, and apparatus including computer program products are provided for determining advertisement effectiveness. In some implementations a method is provided. The method includes identifying a plurality of user events, where each user event is a tracked online activity or action and identifying information associated with each identified user event, the information including at least one of a time and a location of each user event. The method also includes associating each user event with an advertisement including relating each advertisement with times and locations of associated user events and determining advertisement effectiveness using the information from the identified plurality of user events.

Determining Advertising Effectiveness

This chapter contains sections titled: 1 Introduction, 2 Aggregation Changes Demand, 3 A Model for Aggregation and Disaggregation, 4 Site Licensing and Subscriptions, 5 Conclusion, Notes, References

Aggregation and Disaggregation of Information Goods: Implications for Bundling, Site Licensing, and Micropayment Systems

I examine a simple model of rent seeking behavior in order to determine the correct way to measure welfare loss due to rent seeking. I conduct this analysis using a general equilibrium version of the standard partial equilibrium consumers' surplus cost-benefit setup. I conclude that the ordinary tools of cost-benefit analysis, such as consumers' and producers' surplus, are up to the task of measuring the deadweight loss due to rent seeking, as long as they are applied in the proper general equilibrium context.

Measuring the deadweight costs of dup and rent seeking activities

Data from do-not-call registries and other sources show discernible patterns in the demographics of consumers who signed up for do-not-call lists. Such patterns might also be useful in analyzing the prospects for a do-not-spam registry

/pdf/the-demographics-of-the-do-not-call-list-security-of-data-42afxgbkq3.pdf

The demographics of the do-not-call list [security of data]

I consider the private provision of public goods when agents are able to make sequential contributions rather than simultaneous contributions. I show that this tends to exacerbate the free-rider problem in the sense that the amount of the public good provided under sequential contribution is always less than under simultaneous contribution.

Hal R. Varian

Papers

Determining Advertising Effectiveness

Aggregation and Disaggregation of Information Goods: Implications for Bundling, Site Licensing, and Micropayment Systems

Measuring the deadweight costs of dup and rent seeking activities

The demographics of the do-not-call list [security of data]

Sequential provision of public goods