Basics of qualitative research: Grounded theory procedures and techniques

A fundamental problem that confronts peer-to-peer applications is to efficiently locate the node that stores a particular data item. This paper presents Chord, a distributed lookup protocol that addresses this problem. Chord provides support for just one operation: given a key, it maps the key onto a node. Data location can be easily implemented on top of Chord by associating a key with each data item, and storing the key/data item pair at the node to which the key maps. Chord adapts efficiently as nodes join and leave the system, and can answer queries even if the system is continuously changing. Results from theoretical analysis, simulations, and experiments show that Chord is scalable, with communication cost and the state maintained by each node scaling logarithmically with the number of Chord nodes.

Chord: A scalable peer-to-peer lookup service for internet applications

The network structure of a hyperlinked environment can be a rich source of information about the content of the environment, provided we have effective means for understanding it. We develop a set of algorithmic tools for extracting information from the link structures of such environments, and report on experiments that demonstrate their effectiveness in a variety of context on the World Wide Web. The central issue we address within our framework is the distillation of broad search topics, through the discovery of “authorative” information sources on such topics. We propose and test an algorithmic formulation of the notion of authority, based on the relationship between a set of relevant authoritative pages and the set of “hub pages” that join them together in the link structure. Our formulation has connections to the eigenvectors of certain matrices associated with the link graph; these connections in turn motivate additional heuristrics for link-based analysis.

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Authoritative sources in a hyperlinked environment

Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package--PMTK (probabilistic modeling toolkit)--that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

Machine Learning : A Probabilistic Perspective

This paper presents the design and evaluation of Pastry, a scalable, distributed object location and routing substrate for wide-area peer-to-peer ap- plications. Pastry performs application-level routing and object location in a po- tentially very large overlay network of nodes connected via the Internet. It can be used to support a variety of peer-to-peer applications, including global data storage, data sharing, group communication and naming. Each node in the Pastry network has a unique identifier (nodeId). When presented with a message and a key, a Pastry node efficiently routes the message to the node with a nodeId that is numerically closest to the key, among all currently live Pastry nodes. Each Pastry node keeps track of its immediate neighbors in the nodeId space, and notifies applications of new node arrivals, node failures and recoveries. Pastry takes into account network locality; it seeks to minimize the distance messages travel, according to a to scalar proximity metric like the number of IP routing hops. Pastry is completely decentralized, scalable, and self-organizing; it automatically adapts to the arrival, departure and failure of nodes. Experimental results obtained with a prototype implementation on an emulated network of up to 100,000 nodes confirm Pastry's scalability and efficiency, its ability to self-organize and adapt to node failures, and its good network locality properties.

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Pastry: Scalable, Decentralized Object Location, and Routing for Large-Scale Peer-to-Peer Systems

Online forums are an integral part of modern day courses, but motivating students to participate in educationally beneficial discussions can be challenging. Our proposed solution is to initialize (or “seed”) a new course forum with comments from past instances of the same course that are intended to trigger discussion that is beneficial to learning. In this work, we develop methods for selecting high-quality seeds and evaluate their impact over one course instance of a 186-student biology class. We designed a scale for measuring the “seeding suitability” score of a given thread (an opening comment and its ensuing discussion). We then constructed a supervised machine learning (ML) model for predicting the seeding suitability score of a given thread. This model was evaluated in two ways: first, by comparing its performance to the expert opinion of the course instructors on test/holdout data; and second, by embedding it in a live course, where it was actively used to facilitate seeding by the course instructors. For each reading assignment in the course, we presented a ranked list of seeding recommendations to the course instructors, who could review the list and filter out seeds with inconsistent or malformed content. We then ran a randomized controlled study, in which one group of students was shown seeds that were recommended by the ML model, and another group was shown seeds that were recommended by an alternative model that ranked seeds purely by the length of discussion that was generated in previous course instances. We found that the group of students that received posts from either seeding model generated more discussion than a control group in the course that did not get seeded posts. Furthermore, students who received seeds selected by the ML-based model showed higher levels of engagement, as well as greater learning gains, than those who received seeds ranked by length of discussion.

Seeding Course Forums using the Teacher-in-the-Loop

Browsing the Internet today is for the most part a private ex-perience, with the exception of a few websites. We explore the possibilities and design considerations around a system to make browsing the web a more social activity. In order to address this, we present a system called Eyebrowse that allows users to selectively share their web browsing activ-ity publicly and with friends, using a whitelist at the domain level. This opens up the capabilities to conduct discussions both in real-time and asynchronously around webpages that are designated by the user as a “public space”, much like public spaces in real life. Eyebrowse also allows users to find interesting content recommendations, collect personal, web-scale, and friend browsing analytics, and maintain a public persona around their browsing data.

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Eyebrowse: Selective and Public Web Activity Sharing

To find interesting, personally relevant web content, we often rely on friends and colleagues to pass links along as they encounter them. In this paper, we study and augment link-sharing via e-mail, the most popular means of sharing web content today. Armed with survey data indicating that active sharers of novel web content are often those that actively seek it out, we present FeedMe, a plug-in for Google Reader that makes directed sharing of content a more salient part of the user experience. Our survey research indicates that sharing is moderated by concern about relevancy to the recipient, a desire to send only novel content to the recipient, and the effort required to share. FeedMe allays these concerns by recommending friends who may be interested in seeing the content, providing information on what the recipient has seen and how many emails they have received recently, and giving recipients the opportunity to provide lightweight feedback when they appreciate shared content. FeedMe introduces a novel design space for mixed-initiative social recommenders: friends who know the user voluntarily vet the material on the user’s behalf. We present a two week field experiment (N=60) demonstrating that FeedMe’s recommendations and social awareness features made it easier and more enjoyable to share content that recipients appreciated and would not have found otherwise. Author

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Understanding and Supporting Directed Content Sharing on the Web

The development of the RDF[2] standard highlights the fact that a great deal of useful information is in the form of semistructured data—objects connected by relations fitting no rigorous schema. To make use of this information it is important to be able to search and browse semistructured data repositories. In this paper, we present a framework, system, and user interface supporting navigation in such repositories. The system is general purpose, involving no hard-coded assumptions about the structure of the repository. The focus is on providing users with helpful “next steps” leading them to the information they are seeking.

Assisted Browsing for Semistructured Data

This paper presents the design, implementation, analysis, and experimental evaluation of speak-up , a defense against application-level distributed denial-of-service (DDoS), in which attackers crip-ple a server by sending legitimate-looking requests that consume computational resources ( e.g. , CPU cycles, disk). With speak-up, a victimized server encourages all clients, resources permitting, to automatically send higher volumes of traf(cid:2)c . We suppose that attackers are already using most of their upload bandwidth so cannot react to the encouragement. Good clients, however, have spare upload bandwidth and will react to the encouragement with drastically higher volumes of traf(cid:2)c. The intended outcome of this traf(cid:2)c in(cid:3)ation is that the good clients crowd out the bad ones, thereby captur-ing a much larger fraction of the server’s resources than before. We experiment under various conditions and (cid:2)nd that speak-up causes the server to spend resources on a group of clients in rough proportion to their aggregate upload bandwidth. This result makes the defense viable and effective for a class of real attacks.

David R. Karger

Papers

Seeding Course Forums using the Teacher-in-the-Loop

Eyebrowse: Selective and Public Web Activity Sharing

Understanding and Supporting Directed Content Sharing on the Web

Assisted Browsing for Semistructured Data

MIT Open Access Articles DDoS defense by offense