Analyzing the Flow of Information from Initial Publishing to Wikipedia

This thesis covers my efforts at researching the factors that lead to a research paper being cited by Wikipedia. Wikipedia is one of the most popular websites on the internet for quickly learning about a specific topic. It achieved this by being able to back up its claims with cited sources, many of which are research papers. I wanted to see exactly how those papers were found by Wikipedia’s editors when they write the articles. To do this, I gathered thousands of computer science research papers from arXiv.org, as well as a selection of papers that were cited by Wikipedia, so that I could examine those papers and see what made them visible and attractive to the Wikipedia editors. After I gathered the information on how and when these papers are cited, I ran a series of tests on them to learn as much as I could about what causes a paper to be cited by Wikipedia. I discovered that papers that are cited by Wikipedia tend to be more popular than papers which are not cited by Wikipedia even before they are cited but getting cited by Wikipedia can result in a boost in popularity. Wikipedia editors also tend to choose papers that either showcase a creation of the author(s) or give a general overview on a topic. I also discovered one paper that was likely added to Wikipedia by the author in an attempt at increased visibility

Web citations in patents: Evidence of technological impact?

This is an accepted manuscript of an article published by Wiley Blackwell in Journal of the Association for Information Science and Technology on 17/07/2017, available online: https://doi.org/10.1002/asi.23821 The accepted version of the publication may differ from the final published version.Patents sometimes cite web pages either as general background to the problem being addressed or to identify prior publications that will limit the scope of the patent granted. Counts of the number of patents citing an organisation’s website may therefore provide an indicator of its technological capacity or relevance. This article introduces methods to extract URL citations from patents and evaluates the usefulness of counts of patent web citations as a technology indicator. An analysis of patents citing 200 US universities or 177 UK universities found computer science and engineering departments to be frequently cited, as well as research-related web pages, such as Wikipedia, YouTube or Internet Archive. Overall, however, patent URL citations seem to be frequent enough to be useful for ranking major US and the top few UK universities if popular hosted subdomains are filtered out, but the hit count estimates on the first search engine results page should not be relied upon for accuracy

Using Citation Data for Purchase Decisions: Analysing Citing Patterns and Journal Holdings at the Royal Institute of Technology

This paper describes how citation data can be used for identifying gaps in journal holdings and in that way form a foundation for acquisitions. Citation data was matched against e-journal holdings using Web of Science™ and export files from a central knowledge base. Data for three years (2010-2012) was used, in total from 6 246 publications containing 130 090 references to 5 216 journals. Furthermore, impact factors from Journal Citation Reports™ were added as well as information about publisher and if the journal was open access or not. The journals were also enriched with subject headings. The latter information was drawn from the database Ulrich’s web™. The output was divided according to which of the nine different schools of the institution the first author was affiliated to, each school being subject specific. (i.e.: Architecture, Biotechnology, Chemistry, Computer Science, Engineering) Analysis of citations to journals held or not held by the library formed an excellent foundation for future demand driven purchase decisions. Also, conclusions could be drawn about citing patterns to high impact journals, how open access journals were cited and which publishers were most highly cited. A specific analysis was performed within the life sciences as new research groups were demanding e-resources within subject areas not traditionally associated with a technological university. The data could confirm the accuracy of recent major investments as well as give support for future purchases. The data also shows how journal holdings in general match the different subject areas of the institution and in that way can provide a valuable basis for future budget discussions with the faculty

The diffusion and influence of theoretical models of information behaviour : the case of Savolainen's ELIS model

To ascertain the diffusion and influence of Savolainen's ELIS model and its use as a theoretical and/or methodological basis for research. A context citation analysis was made of the work where this researcher published his model. Analysis covered the year of publication, the type of work and the subject-matter of the citing documents concerned. In-context citations were analysed for their frequency in each citing text, style, location and content cited. The ELIS model received 18.5 cites/year. 20.2% of them corresponded to papers published in journals in other areas, mainly computer science. The average of cites per paper was 1.8; 64.5% of the citing works cited them only once. 60% of the cites were considered essential. Only 13.7% of these cites appear in theory or methods. 37% of the citing documents contained no concept relating to the model. The method used focuses on the most direct context of a cite (sentence or paragraph), but isolates it from the general context (full document, other documents by the author or their social capital). It has however allowed this research issue to be dealt with under laboratory conditions, and revealed nuances hidden by the absolute number of cites. It has become evident that the dissemination and influence of the ELIS model is less that what the total number of cites indicates, and that it has scarcely been incorporated into research design. Despite its popularity, it is not being validated and/or refuted by way of empirical data

Analyzing the Flow of Information from Initial Publishing to Wikipedia

This thesis covers my efforts at researching the factors that lead to a research paper being cited by Wikipedia. Wikipedia is one of the most popular websites on the internet for quickly learning about a specific topic. It achieved this by being able to back up its claims with cited sources, many of which are research papers. I wanted to see exactly how those papers were found by Wikipedia’s editors when they write the articles. To do this, I gathered thousands of computer science research papers from arXiv.org, as well as a selection of papers that were cited by Wikipedia, so that I could examine those papers and see what made them visible and attractive to the Wikipedia editors. After I gathered the information on how and when these papers are cited, I ran a series of tests on them to learn as much as I could about what causes a paper to be cited by Wikipedia. I discovered that papers that are cited by Wikipedia tend to be more popular than papers which are not cited by Wikipedia even before they are cited but getting cited by Wikipedia can result in a boost in popularity. Wikipedia editors also tend to choose papers that either showcase a creation of the author(s) or give a general overview on a topic. I also discovered one paper that was likely added to Wikipedia by the author in an attempt at increased visibility

A Note on Implementing OPS5 Production Systems on DADO

This brief note is written in response to a recent publication, "Implementing OPS5 Production Systems on DADO," published as a Carnegie-Mellon Department of Computer Science Technical Report by Anoop Gupta in March, 1984. Gupta's paper analyzes the performance of OPS5 Production System programs on the DADO parallel computer, a special purpose production system (PS) machine. The analysis leads Gupta to conclude that DADO is not an effective OPS5 PS machine. we have studied Gupta's analysis carefully, and conclude that his conclusions are Inaccurate, flawed by, at times, incorrect or outdated information about the DADO2 prototype, and, at other times, inexact reasoning. We have divided the following into two sections. Section 2 details specific technical errors regarding the DADO2 design cited by Gupta. Although Gupta properly cites statistics we reported in earlier papers proposing DADO, his analysis is based on an earlier design of the second prototype system presently near completion at Columbia University. However, after making the changes appropriate to be consistent with the current technical design of DADO2, Gupta's analysis is also flawed in not adequately understanding the detailed workings of several reported algorithms. Section 3 focuses on philosophical differences. We shall be careful to accurately quote Gupta to strengthen our case that his conclusion is rather weak. We conclude that DADO indeed is an effective OPS5 processor. More importantly, we believe the DADO machine will produce dramatic performance Improvements of AI computation when the sequentialities inherent in OPSS are removed. The reader should first carefully read Gupta's paper and anyone of the most recent reports detailing the DADO system and algorithms, (Stolfo and Miranker, 1984), for example

What's in a Name? The Multiple Meanings of "Chunk" and "Chunking"

© 2016 Gobet, Lloyd-Kelly and Lane. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.The term chunk, denoting a unit, and the related term chunking, denoting a mechanism to construct that unit, are familiar terms within psychology and cognitive science. The Oxford English Dictionary provides several definitions for “chunk.” First, “a thick, more or less cuboidal, lump, cut off anything,” or, colloquially, “a large or substantial amount.” The Merriam-Webster dictionary provides similar definitions. OUP's Oxford Dictionary alone gives a computer-related meaning: “a section of information or data.” It is in this context, a chunk as a section of information, that the word is used within psychology and cognitive science. In these fields, a chunk typically refers to a single unit built from several smaller elements, and chunking to the process of creating a chunk. Gobet et al. (2001, p. 236) define a chunk as “a collection of elements having strong associations with one another, but weak associations with elements within other chunks.” However, in different contexts and with different authors, these two terms are used with a variety of meanings, which are very often conflated, leading to considerable confusion. Table 1 provides a taxonomy of the main meanings of “chunk” and “chunking,” which will be used to structure this article.Peer reviewedFinal Published versio

Searching for new breakthroughs in science: How effective are computerised detection algorithms?

In this study we design, develop, implement and test an analytical framework and measurement model to detect scientific discoveries with 'breakthrough' characteristics. To do so we have developed a series of computerized search algorithms  that data mine large quantities of research publications. These algorithms facilitate early-stage detection of 'breakout' papers  that  emerge as highly cited and distinctive and are considered to be potential breakthroughs. Combining computer-aided data  mining with decision heuristics, enabled us to assess structural changes within citation patterns with the international scientific literature. In our case studies we applied a citation  impact  time  window  of 24--36 months after publication of each research paper.  In this paper, we report on our test results, in which five algorithms were applied to the entire Web of Science database. We analysed the citation impact patterns of all research articles from the period 1990--1994. We succeeded in detecting many papers with distinctive impact profiles (breakouts). A small subset of these breakouts is classified as 'breakthroughs': Nobel Prize research papers; papers occurring in Nature's Top-100 Most Cited Papers Ever; papers still (highly) cited by review papers or patents; or those frequently mentioned in today's social media. We also compare the outcomes of our algorithms with the results of a 'baseline' detection algorithm developed by Redner in 2005, which selects the world's most highly cited 'hot papers'.The detection rates of the algorithms vary, but overall, they present a powerful tool for tracing breakout papers in science. The wider applicability of these algorithms, across all science fields, has not yet been ascertained. Whether or not our early-stage breakout papers present a 'breakthrough' remains a matter of opinion, where input from subject experts is needed for verification and confirmation, but our detection approach certain helps to limit the search domain to trace and track important emerging topics in science.Merit, Expertise and Measuremen

The Turing Guide

This volume celebrates the various facets of Alan Turing (1912–1954), the British mathematician and computing pioneer, widely considered as the father of computer science. It is aimed at the general reader, with additional notes and references for those who wish to explore the life and work of Turing more deeply. The book is divided into eight parts, covering different aspects of Turing’s life and work. Part I presents various biographical aspects of Turing, some from a personal point of view. Part II presents Turing’s universal machine (now known as a Turing machine), which provides a theoretical framework for reasoning about computation. His 1936 paper on this subject is widely seen as providing the starting point for the field of theoretical computer science. Part III presents Turing’s working on codebreaking during World War II. While the War was a disastrous interlude for many, for Turing it provided a nationally important outlet for his creative genius. It is not an overstatement to say that without Turing, the War would probably have lasted longer, and may even have been lost by the Allies. The sensitive nature of Turning’s wartime work meant that much of this has been revealed only relatively recently. Part IV presents Turing’s post-War work on computing, both at the National Physical Laboratory and at the University of Manchester. He made contributions to both hardware design, through the ACE computer at the NPL, and software, especially at Manchester. Part V covers Turing’s contribution to machine intelligence (now known as Artificial Intelligence or AI). Although Turing did not coin the term, he can be considered a founder of this field which is still active today, authoring a seminal paper in 1950. Part VI covers morphogenesis, Turing’s last major scientific contribution, on the generation of seemingly random patterns in biology and on the mathematics behind such patterns. Interest in this area has increased rapidly in recent times in the field of bioinformatics, with Turing’s 1952 paper on this subject being frequently cited. Part VII presents some of Turing’s mathematical influences and achievements. Turing was remarkably free of external influences, with few co-authors – Max Newman was an exception and acted as a mathematical mentor in both Cambridge and Manchester. Part VIII considers Turing in a wider context, including his influence and legacy to science and in the public consciousness. Reflecting Turing’s wide influence, the book includes contributions by authors from a wide variety of backgrounds. Contemporaries provide reminiscences, while there are perspectives by philosophers, mathematicians, computer scientists, historians of science, and museum curators. Some of the contributors gave presentations at Turing Centenary meetings in 2012 in Bletchley Park, King’s College Cambridge, and Oxford University, and several of the chapters in this volume are based on those presentations – some through transcription of the original talks, especially for Turing’s contemporaries, now aged in their 90s. Sadly, some contributors died before the publication of this book, hence its dedication to them. For those interested in personal recollections, Chapters 2, 3, 11, 12, 16, 17, and 36 will be of interest. For philosophical aspects of Turing’s work, see Chapters 6, 7, 26–31, and 41. Mathematical perspectives can be found in Chapters 35 and 37–39. Historical perspectives can be found in Chapters 4, 8, 9, 10, 13–15, 18, 19, 21–25, 34, and 40. With respect to Turing’s body of work, the treatment in Parts II–VI is broadly chronological. We have attempted to be comprehensive with respect to all the important aspects of Turing’s achievements, and the book can be read cover to cover, or the chapters can be tackled individually if desired. There are cross-references between chapters where appropriate, and some chapters will inevitably overlap. We hope that you enjoy this volume as part of your library and that you will dip into it whenever you wish to enter the multifaceted world of Alan Turing

Redefining Creativity in the Era of AI? Perspectives of Computer Scientists and New Media Artists

Artificial intelligence (AI) has breached creativity research. The advancements of creative AI systems dispute the common definitions of creativity that have traditionally focused on five elements: actor, process, outcome, domain, and space. Moreover, creative workers, such as scientists and artists, increasingly use AI in their creative processes, and the concept of co-creativity has emerged to describe blended human–AI creativity. These issues evoke the question of whether creativity requires redefinition in the era of AI. Currently, co-creativity is mostly studied within the framework of computer science in pre-organized laboratory settings. This study contributes from a human scientific perspective with 52 interviews of Finland-based computer scientists and new media artists who use AI in their work. The results suggest scientists and artists use similar elements to define creativity. However, the role of AI differs between the scientific and artistic creative processes. Scientists need AI to produce accurate and trustworthy outcomes, whereas artists use AI to explore and play. Unlike the scientists, some artists also considered their work with AI co-creative. We suggest that co-creativity can explain the contemporary creative processes in the era of AI and should be the focal point of future creativity research.© 2022 The Author(s). Published with license by Taylor & Francis Group, LLC. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.fi=vertaisarvioitu|en=peerReviewed