Transferring a Question-Based Dialog Framework to a Distributed Architecture

Inquiry skills are an essential tool for assessing and integrating knowledge. In facilitated face-to-face settings, inquiry skills were improved successfully by using a “question-based dialog” and its resulting visual representation. However, groups that work without a facilitator, or in which members collaborate asynchronously or in different geographical regions, such as Communities of Practice (CoP), cannot schedule face-to-face inquiry meetings. This paper summarises the unmet requirements of CoPs for a collaborative inquiry tool found by previous research on the Noracle model and proposes a distributed Web architecture as a solution. It mitigates the need for a common infrastructure, central coordination or facilitation, addresses the evolutionary nature of communities of practice and reduces the cognitive load for the individual by filtering and organising the representational artefacts with respect to the social network of the community. The implementation we envision in this paper aims at applying the concept to a much broader audience, ultimately replacing the need for local meetings

A Microservice Infrastructure for Distributed Communities of Practice

Non-formal learning in Communities of Practice (CoPs) makes up a significant portion of today’s knowledge gain. However, only little technological support is tailored specifically towards CoPs and their particular strengths and challenges. Even worse, CoPs often do not possess the resources to host or even develop a software ecosystem to support their activities. In this paper, we describe a distributed, microservice-based Web infrastructure for non-formal learning in CoPs. It mitigates the need for central infrastructures, coordination or facilitation and takes into account the constant change of these communities. As a real use case, we implement an inquiry-based learning application on-top of our infrastructure. Our evaluation results indicate the usefulness of this learning application, which shows promise for future work in the domain of community-hosted, microservice-based Web infrastructures for learning outside of formal settings

SCREEN: Learning a Flat Syntactic and Semantic Spoken Language Analysis Using Artificial Neural Networks

In this paper, we describe a so-called screening approach for learning robust processing of spontaneously spoken language. A screening approach is a flat analysis which uses shallow sequences of category representations for analyzing an utterance at various syntactic, semantic and dialog levels. Rather than using a deeply structured symbolic analysis, we use a flat connectionist analysis. This screening approach aims at supporting speech and language processing by using (1) data-driven learning and (2) robustness of connectionist networks. In order to test this approach, we have developed the SCREEN system which is based on this new robust, learned and flat analysis. In this paper, we focus on a detailed description of SCREEN's architecture, the flat syntactic and semantic analysis, the interaction with a speech recognizer, and a detailed evaluation analysis of the robustness under the influence of noisy or incomplete input. The main result of this paper is that flat representations allow more robust processing of spontaneous spoken language than deeply structured representations. In particular, we show how the fault-tolerance and learning capability of connectionist networks can support a flat analysis for providing more robust spoken-language processing within an overall hybrid symbolic/connectionist framework.Comment: 51 pages, Postscript. To be published in Journal of Artificial Intelligence Research 6(1), 199

Settlement in modern network-based payment infrastructures – description and prototype of the E-Settlement model

Payment systems are undergoing rapid and fundamental changes stimulated largely by technological progress especially distributed network technology and real-time processing. Internet and e-commerce will have a major impact on payment systems in the future. User demands and competition will speed up developments. Payment systems will move from conventions that were originally paper-based to truly network-based solutions. This paper presents a solution – E-Settlement – for improving interbank settlement systems. It is based on a decentralised approach to be fully integrated with the banks’ payment systems. The basic idea is that central bank money, the settlement cover, is transferred as an encrypted digital stamp as part of the interbank payment message. The future payment systems would in this model operate close to the Internet/e-mail concept by sending payment messages directly from the sending bank’s account/payment server to the system of the receiving bank with immediate final interbank settlement without intervening centralised processing. Payment systems would become more efficient and faster and the overall structure would be come straightforward. The E-Settlement and network-based system concept could be applied with major benefits for correspondent banking, ACH and RTGS processing environments. In order to assess this novel idea the Bank of Finland built a prototype of the E-Settlement model. It consist of a group of emulated banks sending payments to each other via a TCP/IP network under the control of a central bank as the liquidity provider and an administration site monitoring the system security. This paper contains an introduction to network-based payment systems and E-Settlement, the specifications of the E-Settlement model and the description, results and experiences of the actual E-Settlement prototype.network-based payment systems; settlement systems; interbank settlement; payment system integration

User Intent Prediction in Information-seeking Conversations

Conversational assistants are being progressively adopted by the general population. However, they are not capable of handling complicated information-seeking tasks that involve multiple turns of information exchange. Due to the limited communication bandwidth in conversational search, it is important for conversational assistants to accurately detect and predict user intent in information-seeking conversations. In this paper, we investigate two aspects of user intent prediction in an information-seeking setting. First, we extract features based on the content, structural, and sentiment characteristics of a given utterance, and use classic machine learning methods to perform user intent prediction. We then conduct an in-depth feature importance analysis to identify key features in this prediction task. We find that structural features contribute most to the prediction performance. Given this finding, we construct neural classifiers to incorporate context information and achieve better performance without feature engineering. Our findings can provide insights into the important factors and effective methods of user intent prediction in information-seeking conversations.Comment: Accepted to CHIIR 201

Why not one big database? : principles for data ownership

"This paper supercedes an earlier version, WP#2518-93 published in December 1992 under the title "Ownership principles for distributed database design."Includes bibliographical references (p. 36-39).Supported by the MIT Industrial Performance Center. Supported by the Advanced Research Projects Agency. F30602-93-C-0160Marshall Van Alstyne, Erik Brynjolfsson, Stuart E. Madnick