5918485
A framework for dynamic Byzantine storage
We present a framework for transforming several quorum-based protocols so that they can dynamically adapt their failure threshold and server count, allowing them to be reconfigured in anticipation of possible failures or to replace servers as desired. We demonstrate this transformation on the dissemination quorum protocol. The resulting system provides confirmable wait-free atomic semantics while tolerating Byzantine failures from the clients or servers. The system can grow without bound to tolerate as many failures as desired. Finally, the protocol is optimal and fast: only the minimal number of servers - 3f + 1 - is needed to tolerate any f failures and, in the common case, reads require only one message round-trip.
53305414
Fast Byzantine Consensus
We present the first protocol that reaches asynchronous Byzantine consensus in two communication steps in the common case. We prove that our protocol is optimal in terms of both number of communication steps and number of processes for two-step consensus. The protocol can be used to build a replicated state machine that requires only three communication steps per request in the common case. Further, we show a parameterized version of the protocol that is safe despite f Byzantine failures and, in the common case, guarantees two-step execution despite some number t of failures (t les f). We show that this parameterized two-step consensus protocol is also optimal in terms of both number of communication steps and number of processes
5491635
Small byzantine quorum systems
In this paper we present two protocols for asynchronous Byzantine quorum systems (BQS) built on top of reliable channels-one for self-verifying data and the other for any data. Our protocols tolerate f Byzantine failures with f fewer servers than existing solutions by eliminating nonessential work in the write protocol and by using read and write quorums of different sizes. Since engineering a reliable network layer on an unreliable network is difficult, two other possibilities must be explored. The first is to strengthen the model by allowing synchronous networks that use time-outs to identify failed links or machines. We consider running synchronous and asynchronous Byzantine quorum protocols over synchronous networks and conclude that, surprisingly, "self-timing" asynchronous Byzantine protocols may offer significant advantages for many synchronous networks when network time-outs are long. We show how to extend an existing Byzantine quorum protocol to eliminate its dependency on reliable networking and to handle message loss and retransmission explicitly.
5705354
BAR fault tolerance for cooperative services
This paper describes a general approach to constructing cooperative services that span multiple administrative domains. In such environments, protocols must tolerate both Byzantine behaviors when broken, misconfigured, or malicious nodes arbitrarily deviate from their specification and rational behaviors when selfish nodes deviate from their specification to increase their local benefit. The paper makes three contributions: (1) It introduces the BAR (Byzantine, Altruistic, Rational) model as a foundation for reasoning about cooperative services; (2) It proposes a general three-level architecture to reduce the complexity of building services under the BAR model; and (3) It describes an implementation of BAR-B the first cooperative backup service to tolerate both Byzantine users and an unbounded number of rational users. At the core of BAR-B is an asynchronous replicated state machine that provides the customary safety and liveness guarantees despite nodes exhibiting both Byzantine and rational behaviors. Our prototype provides acceptable performance for our application: our BAR-tolerant state machine executes 15 requests per second, and our BAR-B backup service can back up 100MB of data in under 4 minutes.
39279518
Representing UNIX Domain Metaphors
The language used to describe technical domains like UNIX is filled with metaphor. An approach to metaphor, based on theexplicit representation of knowledge about metaphors, has beendeveloped. MIDAS (Metaphor Interpretation, Denotation, andAcquisition System) is a computer program that that has been developedbased upon this approach. MIDAS can be used to representknowledge about conventional metaphors, interpret metaphoric languageby applying this knowledge, and dynamically learn new metaphors asthey are encountered during normal processing.
14471368
A Unified Framework for Constructing Multimodal Experiments and Applications
Inspired by a Wizard of Oz (WOZ) simulation experiment, we developed a working prototype of a system that enables users to interact with a map display through synergistic combinations of pen and voice. To address many of the issues raised by multimodal fusion, our implementation employed a distributed multi-agent framework to coordinate parallel competition and cooperation among processing components. Since then, the agent-based infrastructure has been enhanced with a collaboration technology, creating a framework in which multiple humans and automated agents can naturally interact within the same graphical workspace.Our current endeavour is the leveraging of this architecture to create a unified implementation framework for simultaneously developing both WOZ simulated systems and their fully-automated counterparts. Bootstrapping effects made possible by such an approach are illustrated by an experiment currently under way in our laboratory: as a naive subject draws, writes, and speaks requests to a (simulated) interactive map, a hidden Wizard responds as efficiently as possible using our best fullyautomated system, through either standard graphical interface devices or multimodal combinations of pen and voice. The input choices made by both subject and Wizard are invaluable, and the data collected from each can be applied directly to evaluating and improving the automated part of the system.
6708049
Understanding New Metaphors
Metaphors are widespread in natural language. Most natural language processing systems have failed to adequately deal with this issue. A two part approach to this problem involving a Metaphoric Lexicon and a dynamic extension system is described. The extension mechanism operates by analogically mapping previously understood mappings to account for new uses based on known regularities in the English metaphor system. A system based on this approach has been applied to the UNIX domain. Results indicate that this domain exhibits all the predicted regularities and that understanding new senses is possible using these regularities
33984452
Using System Networks to Build Rhetorical Structures
We are currently engaged in a project to study the generation of instructional texts for common consumer devices. Our initial efforts have focused on an exhaustive corpus-based analysis of instruction booklets for cordless telephones. In this paper, we present our analysis of the way the various processes used in this domain are expressed. Our emphasis here is on the relationship between the surface grammatical coding of these processes and the underlying rhetorical structure of the text. This analysis has been formalized using a systemic-functional framework, with the resulting system networks forming the basis for the Imagene text generation system.
12982947
Support Vector Learning for Semantic Argument Classification
The natural language processing community has recently experienced a growth of interest in domain independent shallow semantic parsing—the process of assigning a Who did What to Whom, When, Where, Why, How etc. structure to plain text. This process entails identifying groups of words in a sentence that represent these semantic arguments and assigning specific labels to them. It could play a key role in NLP tasks like Information Extraction, Question Answering and Summarization. We propose a machine learning algorithm for semantic role parsing, extending the work of Gildea and Jurafsky (2002), Surdeanu et al. (2003) and others. Our algorithm is based on Support Vector Machines which we show give large improvement in performance over earlier classifiers. We show performance improvements through a number of new features designed to improve generalization to unseen data, such as automatic clustering of verbs. We also report on various analytic studies examining which features are most important, comparing our classifier to other machine learning algorithms in the literature, and testing its generalization to new test set from different genre. On the task of assigning semantic labels to the PropBank (Kingsbury, Palmer, & Marcus, 2002) corpus, our final system has a precision of 84% and a recall of 75%, which are the best results currently reported for this task. Finally, we explore a completely different architecture which does not requires a deep syntactic parse. We reformulate the task as a combined chunking and classification problem, thus allowing our algorithm to be applied to new languages or genres of text for which statistical syntactic parsers may not be available.