A Model for Managing Information Flow on the World Wide Web

Metadata merged with duplicate record (http://hdl.handle.net/10026.1/330) on 20.12.2016 by CS (TIS).This is a digitised version of a thesis that was deposited in the University Library. If you are the author please contact PEARL Admin ([email protected]) to discuss options.This thesis considers the nature of information management on the World Wide Web. The web has evolved into a global information system that is completely unregulated, permitting anyone to publish whatever information they wish. However, this information is almost entirely unmanaged, which, together with the enormous number of users who access it, places enormous strain on the web's architecture. This has led to the exposure of inherent flaws, which reduce its effectiveness as an information system. The thesis presents a thorough analysis of the state of this architecture, and identifies three flaws that could render the web unusable: link rot; a shrinking namespace; and the inevitable increase of noise in the system. A critical examination of existing solutions to these flaws is provided, together with a discussion on why the solutions have not been deployed or adopted. The thesis determines that they have failed to take into account the nature of the information flow between information provider and consumer, or the open philosophy of the web. The overall aim of the research has therefore been to design a new solution to these flaws in the web, based on a greater understanding of the nature of the information that flows upon it. The realization of this objective has included the development of a new model for managing information flow on the web, which is used to develop a solution to the flaws. The solution comprises three new additions to the web's architecture: a temporal referencing scheme; an Oracle Server Network for more effective web browsing; and a Resource Locator Service, which provides automatic transparent resource migration. The thesis describes their design and operation, and presents the concept of the Request Router, which provides a new way of integrating such distributed systems into the web's existing architecture without breaking it. The design of the Resource Locator Service, including the development of new protocols for resource migration, is covered in great detail, and a prototype system that has been developed to prove the effectiveness of the design is presented. The design is further validated by comprehensive performance measurements of the prototype, which show that it will scale to manage a web whose size is orders of magnitude greater than it is today

Understanding people through the aggregation of their digital footprints

Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 160-172).Every day, millions of people encounter strangers online. We read their medical advice, buy their products, and ask them out on dates. Yet our views of them are very limited; we see individual communication acts rather than the person(s) as a whole. This thesis contends that socially-focused machine learning and visualization of archived digital footprints can improve the capacity of social media to help form impressions of online strangers. Four original designs are presented that each examine the social fabric of a different existing online world. The designs address unique perspectives on the problem of and opportunities offered by online impression formation. The first work, Is Britney Spears Span?, examines a way of prototyping strangers on first contact by modeling their past behaviors across a social network. Landscape of Words identifies cultural and topical trends in large online publics. Personas is a data portrait that characterizes individuals by collating heterogenous textual artifacts. The final design, Defuse, navigates and visualizes virtual crowds using metrics grounded in sociology. A reflection on these experimental endeavors is also presented, including a formalization of the problem and considerations for future research. A meta-critique by a panel of domain experts completes the discussion.by Aaron Robert Zinman.Ph.D

People-search : searching for people sharing similar interests from the web

On the Web, there are limited ways of finding people sharing similar interests or background with a given person. The current methods, such as using regular search engines, are either ineffective or time consuming. In this work, a new approach for searching people sharing similar interests from the Web, called People-Search, is presented. Given a person, to find similar people from the Web, there are two major research issues: person representation and matching persons. In this study, a person representation method which uses a person\u27s website to represent this person\u27s interest and background is proposed. The design of matching process takes person representation into consideration to allow the same representation to be used when composing the query, which is also a personal website. Based on this person representation method, the main proposed algorithm integrates textual content and hyperlink information of all the pages belonging to a personal website to represent a person and match persons. Other algorithms, based on different combinations of content, inlink, and outlink information of an entire personal website or only the main page, are also explored and compared to the main proposed algorithm. Two kinds of evaluations were conducted. In the automatic evaluation, precision, recall, F and Kruskal-Goodman F measures were used to compare these algorithms. In the human evaluation, the effectiveness of the main proposed algorithm and two other important ones were evaluated by human subjects. Results from both evaluations show that the People-Search algorithm integrating content and link information of all pages belonging to a personal website outperformed all other algorithms in finding similar people from the Web

Semantic technologies: from niche to the mainstream of Web 3? A comprehensive framework for web Information modelling and semantic annotation

Context: Web information technologies developed and applied in the last decade have considerably changed the way web applications operate and have revolutionised information management and knowledge discovery. Social technologies, user-generated classification schemes and formal semantics have a far-reaching sphere of influence. They promote collective intelligence, support interoperability, enhance sustainability and instigate innovation. Contribution: The research carried out and consequent publications follow the various paradigms of semantic technologies, assess each approach, evaluate its efficiency, identify the challenges involved and propose a comprehensive framework for web information modelling and semantic annotation, which is the thesis’ original contribution to knowledge. The proposed framework assists web information modelling, facilitates semantic annotation and information retrieval, enables system interoperability and enhances information quality. Implications: Semantic technologies coupled with social media and end-user involvement can instigate innovative influence with wide organisational implications that can benefit a considerable range of industries. The scalable and sustainable business models of social computing and the collective intelligence of organisational social media can be resourcefully paired with internal research and knowledge from interoperable information repositories, back-end databases and legacy systems. Semantified information assets can free human resources so that they can be used to better serve business development, support innovation and increase productivity

4th. International Conference on Advanced Research Methods and Analytics (CARMA 2022)

Research methods in economics and social sciences are evolving with the increasing availability of Internet and Big Data sources of information. As these sources, methods, and applications become more interdisciplinary, the 4th International Conference on Advanced Research Methods and Analytics (CARMA) is a forum for researchers and practitioners to exchange ideas and advances on how emerging research methods and sources are applied to different fields of social sciences as well as to discuss current and future challenges. Due to the covid pandemic, CARMA 2022 is planned as a virtual and face-to-face conference, simultaneouslyDoménech I De Soria, J.; Vicente Cuervo, MR. (2022). 4th. International Conference on Advanced Research Methods and Analytics (CARMA 2022). Editorial Universitat Politècnica de València. https://doi.org/10.4995/CARMA2022.2022.1595

Crowd-supervised training of spoken language systems

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 155-166).Spoken language systems are often deployed with static speech recognizers. Only rarely are parameters in the underlying language, lexical, or acoustic models updated on-the-fly. In the few instances where parameters are learned in an online fashion, developers traditionally resort to unsupervised training techniques, which are known to be inferior to their supervised counterparts. These realities make the development of spoken language interfaces a difficult and somewhat ad-hoc engineering task, since models for each new domain must be built from scratch or adapted from a previous domain. This thesis explores an alternative approach that makes use of human computation to provide crowd-supervised training for spoken language systems. We explore human-in-the-loop algorithms that leverage the collective intelligence of crowds of non-expert individuals to provide valuable training data at a very low cost for actively deployed spoken language systems. We also show that in some domains the crowd can be incentivized to provide training data for free, as a byproduct of interacting with the system itself. Through the automation of crowdsourcing tasks, we construct and demonstrate organic spoken language systems that grow and improve without the aid of an expert. Techniques that rely on collecting data remotely from non-expert users, however, are subject to the problem of noise. This noise can sometimes be heard in audio collected from poor microphones or muddled acoustic environments. Alternatively, noise can take the form of corrupt data from a worker trying to game the system - for example, a paid worker tasked with transcribing audio may leave transcripts blank in hopes of receiving a speedy payment. We develop strategies to mitigate the effects of noise in crowd-collected data and analyze their efficacy. This research spans a number of different application domains of widely-deployed spoken language interfaces, but maintains the common thread of improving the speech recognizer's underlying models with crowd-supervised training algorithms. We experiment with three central components of a speech recognizer: the language model, the lexicon, and the acoustic model. For each component, we demonstrate the utility of a crowd-supervised training framework. For the language model and lexicon, we explicitly show that this framework can be used hands-free, in two organic spoken language systems.by Ian C. McGraw.Ph.D

Architecture and implementation of online communities

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.Includes bibliographical references.by Philip Greenspun.Ph.D

Efficient Passive Clustering and Gateways selection MANETs

Passive clustering does not employ control packets to collect topological information in ad hoc networks. In our proposal, we avoid making frequent changes in cluster architecture due to repeated election and re-election of cluster heads and gateways. Our primary objective has been to make Passive Clustering more practical by employing optimal number of gateways and reduce the number of rebroadcast packets

From subject to device, history as myth in action : the evolution of event from mythic processes as revealed in Waterfront Dispute fiction

This analysis of selected New Zealand works defends the evolving function of history as fiction-material. It is intended to establish that purpose and treatment alter, as time further separates the writing and the event. The general change is one of development from subject to device properties. In tracing history's evolving role and treatment in fiction, analysis identifies history's eventual source - shown, in fiction, to be mythic and subjectively conceptual

Informational Privacy and Self-Disclosure Online: A Critical Mixed-Methods Approach to Social Media

This thesis investigates the multifaceted processes that have contributed to normalising identifiable self-disclosure in online environments and how perceptions of informational privacy and self-disclosure behavioural patterns have evolved in the relatively brief history of online communication. Its investigative mixed-methods approach critically examines a wide and diverse variety of primary and secondary sources and material to bring together aspects of the social dynamics that have contributed to the generalised identifiable self-disclosure. This research also utilises the results of the exploratory statistical as well as qualitative analysis of an extensive online survey completed by UCL students as a snapshot in time. This is combined with arguments developed from an analysis of existing published sources and looks ahead to possible future developments. This study examines the time when people online proved to be more trusting, and how users of the Internet responded to the development of the growing societal need to share personal information online. It addresses issues of privacy ethics and how they evolved over time to allow a persistent association of online self-disclosure to real-life identity that had not been seen before the emergence of social network sites. The resistance to identifiable self-disclosure before the widespread use of social network sites was relatively resolved by a combination of elements and circumstances. Some of these result from the demographics of young users, users' attitudes to deception, ideology and trust-building processes. Social and psychological factors, such as gaining social capital, peer pressure and the overall rewarding and seductive nature of social media, have led users to waive significant parts of their privacy in order to receive the perceived benefits. The sociohistorical context allows this research to relate evolving phenomena like the privacy paradox, lateral surveillance and self-censorship to the revamped ethics of online privacy and self-disclosure