Today, most popular software applications are deployed in the cloud, interact with many users, and run on multiple platforms from Web browsers to mobile operating systems. While these applications confer a number of benefits to their users, building them brings many challenges: manually managing state between asynchronous user actions, creating and maintaining separate code bases for each desired client platform and gracefully scaling to handle a large number of concurrent users. Dog is a new programming language that provides a solution to these challenges and others through a unique runtime model that allows developers to model scalable cross-client applications as an imperative control-flow --- simplifying many development tasks. In this paper we describe the key features of Dog and show its utility through several applications that are difficult and time-consuming to write in existing languages, but are simple and easily written in Dog in a few lines of code.

We present Jabberwocky, a social computing stack that consists of three components: a human and machine resource management system called Dormouse, a parallel programming framework for human and machine computation called ManReduce, and a high-level programming language on top of ManReduce called Dog. Dormouse is designed to enable cross-platform programming languages for social computation, so, for example, programs written for Mechanical Turk can also run on other crowdsourcing platforms. Dormouse also enables a programmer to easily combine crowdsourcing platforms or create new ones. Further, machines and people are both first-class citizens in Dormouse, allowing for natural parallelization and control flows for a broad range of data-intensive applications. And finally and importantly, Dormouse includes notions of real identity, heterogeneity, and social structure. We show that the unique properties of Dormouse enable elegant programming models for complex and useful problems, and we propose two such frameworks. ManReduce is a framework for combining human and machine computation into an intuitive parallel data flow that goes beyond existing frameworks in several important ways, such as enabling functions on arbitrary communication graphs between human and machine clusters. And Dog is a high-level procedural language written on top of ManReduce that focuses on expressivity and reuse. We explore two applications written in Dog: bootstrapping product recommendations without purchase data, and expert labeling of medical images.

We present We Feel Fine, an emotional search engine and web-based artwork whose mission is to collect the world's emotions to help people better understand themselves and others. We Feel Fine continuously crawls blogs, microblogs, and social networking sites, extracting sentences that include the words "I feel" or "I am feeling", as well as the gender, age, and location of the people authoring those sentences. The We Feel Fine search interface allows users to search or browse over the resulting sentence-level index, asking questions such as "How did young people in Ohio feel when Obama was elected?" While most research in sentiment analysis focuses on algorithms for extraction and classification of sentiment about given topics, we focus instead on building an interface that provides an engaging means of qualitative exploration of emotional data, and a flexible data collection and serving architecture that enables an ecosystem of data analysis applications. We use our observations on the usage of We Feel Fine to suggest a class of visualizations called Experiential Data Visualization, which focus on immersive item-level interaction with data. We also discuss the implications of such visualizations for crowdsourcing qualitative research in the social sciences.

We present Aardvark, a social search engine. With Aardvark, users ask a question, either by instant message, email, web input, text message, or voice. Aardvark then routes the question to the person in the user's extended social network most likely to be able to answer that question. As compared to a traditional web search engine, where the challenge lies in finding the right document to satisfy a user's information need, the challenge in a social search engine like Aardvark lies in finding the right person to satisfy a user's information need. Further, while trust in a traditional search engine is based on authority, in a social search engine like Aardvark, trust is based on intimacy. We describe how these considerations inform the architecture, algorithms, and user interface of Aardvark, and how they are reflected in the behavior of Aardvark users.

Large-scale competitive P2P networks are threatened by the non-cooperation problem, where peers do not forward queries to potential competitors. Non-cooperation will be a growing problem in such applications as pay-per-transaction file-sharing, P2P auctions, and P2P service discovery networks, where peers are in competition with each other to provide services. Here, the authors showed how non-cooperation causes unacceptable degradation in quality of results, and present an economic protocol to address this problem. This protocol, called the RTR protocol, is based on the buying and selling of the right-to-respond (RTR) to each query in the network. Through simulations it is shown how the RTR protocol not only overcomes non-cooperation by providing proper incentives to peers, but also results in a network that is even more effective and efficient through intelligent, incentive-compatible routing of messages

We present a peer-level protocol for forming adaptive, self-organizing topologies for data-sharing P2P networks. This protocol is based on the idea that a peer should directly connect to those peers from which it is most likely to download satisfactory content. We show that the resulting topologies are more efficient than standard Gnutella topologies. Furthermore, we show that these adaptive topologies have the added benefits of increased resistance to certain types of attacks, intrinsic rewards for active peers and punishments for malicious peers and free riders.

We present a novel algorithm for the fast computation of PageRank, a hyperlink-based estimate of the ''importance'' of Web pages. The original PageRank algorithm uses the Power Method to compute successive iterates that converge to the principal eigenvector of the Markov matrix representing the Web link graph. The algorithm presented here, called Quadratic Extrapolation, accelerates the convergence of the Power Method by periodically subtracting off estimates of the nonprincipal eigenvectors from the current iterate of the Power Method. In Quadratic Extrapolation, we take advantage of the fact that the first eigenvalue of a Markov matrix is known to be 1 to compute the nonprincipal eigenvectors using successive iterates of the Power Method. Empirically, we show that using Quadratic Extrapolation speeds up PageRank computation by 25-300% on a Web graph of 80 million nodes, with minimal overhead. Our contribution is useful to the PageRank community and the numerical linear algebra community in general, as it is a fast method for determining the dominant eigenvector of a matrix that is too large for standard fast methods to be practical.

Peer-to-peer file-sharing networks are currently receiving much attention as a means of sharing and distributing information. However, as recent experience shows, the anonymous, open nature of these networks offers an almost ideal environment for the spread of self-replicating inauthentic files.We describe an algorithm to decrease the number of downloads of inauthentic files in a peer-to-peer file-sharing network that assigns each peer a unique global trust value, based on the peer's history of uploads. We present a distributed and secure method to compute global trust values, based on Power iteration. By having peers use these global trust values to choose the peers from whom they download, the network effectively identifies malicious peers and isolates them from the network.In simulations, this reputation system, called EigenTrust, has been shown to significantly decrease the number of inauthentic files on the network, even under a variety of conditions where malicious peers cooperate in an attempt to deliberately subvert the system.