Search (2 results, page 1 of 1)

  • × author_ss:"Ding, J."
  • × theme_ss:"Internet"
  1. Yang, S.; Han, R.; Ding, J.; Song, Y.: ¬The distribution of Web citations (2012) 0.04
    0.03959743 = product of:
      0.07919486 = sum of:
        0.053759433 = weight(_text_:data in 2735) [ClassicSimilarity], result of:
          0.053759433 = score(doc=2735,freq=6.0), product of:
            0.14807065 = queryWeight, product of:
              3.1620505 = idf(docFreq=5088, maxDocs=44218)
              0.046827413 = queryNorm
            0.3630661 = fieldWeight in 2735, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.1620505 = idf(docFreq=5088, maxDocs=44218)
              0.046875 = fieldNorm(doc=2735)
        0.025435425 = product of:
          0.05087085 = sum of:
            0.05087085 = weight(_text_:processing in 2735) [ClassicSimilarity], result of:
              0.05087085 = score(doc=2735,freq=2.0), product of:
                0.18956426 = queryWeight, product of:
                  4.048147 = idf(docFreq=2097, maxDocs=44218)
                  0.046827413 = queryNorm
                0.26835677 = fieldWeight in 2735, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  4.048147 = idf(docFreq=2097, maxDocs=44218)
                  0.046875 = fieldNorm(doc=2735)
          0.5 = coord(1/2)
      0.5 = coord(2/4)
    
    Abstract
    A substantial amount of research has focused on the persistence or availability of Web citations. The present study analyzes Web citation distributions. Web citations are defined as the mentions of the URLs of Web pages (Web resources) as references in academic papers. The present paper primarily focuses on the analysis of the URLs of Web citations and uses three sets of data, namely, Set 1 from the Humanities and Social Science Index in China (CSSCI, 1998-2009), Set 2 from the publications of two international computer science societies, Communications of the ACM and IEEE Computer (1995-1999), and Set 3 from the medical science database, MEDLINE, of the National Library of Medicine (1994-2006). Web citation distributions are investigated based on Web site types, Web page types, URL frequencies, URL depths, URL lengths, and year of article publication. Results show significant differences in the Web citation distributions among the three data sets. However, when the URLs of Web citations with the same hostnames are aggregated, the distributions in the three data sets are consistent with the power law (the Lotka function).
    Source
    Information processing and management. 48(2012) no.4, S.779-790
  2. Ding, J.: Can data die? : why one of the Internet's oldest images lives on wirhout its subjects's consent (2021) 0.03
    0.025747139 = product of:
      0.051494278 = sum of:
        0.040896185 = weight(_text_:data in 423) [ClassicSimilarity], result of:
          0.040896185 = score(doc=423,freq=20.0), product of:
            0.14807065 = queryWeight, product of:
              3.1620505 = idf(docFreq=5088, maxDocs=44218)
              0.046827413 = queryNorm
            0.27619374 = fieldWeight in 423, product of:
              4.472136 = tf(freq=20.0), with freq of:
                20.0 = termFreq=20.0
              3.1620505 = idf(docFreq=5088, maxDocs=44218)
              0.01953125 = fieldNorm(doc=423)
        0.010598094 = product of:
          0.021196188 = sum of:
            0.021196188 = weight(_text_:processing in 423) [ClassicSimilarity], result of:
              0.021196188 = score(doc=423,freq=2.0), product of:
                0.18956426 = queryWeight, product of:
                  4.048147 = idf(docFreq=2097, maxDocs=44218)
                  0.046827413 = queryNorm
                0.111815326 = fieldWeight in 423, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  4.048147 = idf(docFreq=2097, maxDocs=44218)
                  0.01953125 = fieldNorm(doc=423)
          0.5 = coord(1/2)
      0.5 = coord(2/4)
    
    Abstract
    Lena Forsén, the real human behind the Lenna image, was first published in Playboy in 1972. Soon after, USC engineers searching for a suitable test image for their image processing research sought inspiration from the magazine. They deemed Lenna the right fit and scanned the image into digital, RGB existence. From here, the story of the image follows the story of the internet. Lenna was one of the first inhabitants of ARPANet, the internet's predecessor, and then the world wide web. While the image's reach was limited to a few research papers in the '70s and '80s, in 1991, Lenna was featured on the cover of an engineering journal alongside another popular test image, Peppers. This caught the attention of Playboy, which threatened a copyright infringement lawsuit. Engineers who had grown attached to Lenna fought back. Ultimately, they prevailed, and as a Playboy VP reflected on the drama: "We decided we should exploit this because it is a phenomenon." The Playboy controversy canonized Lenna in engineering folklore and prompted an explosion of conversation about the image. Image hits on the internet rose to a peak number in 1995.
    Content
    "Having known Lenna for almost a decade, I have struggled to understand what the story of the image means for what tech culture is and what it is becoming. To me, the crux of the Lenna story is how little power we have over our data and how it is used and abused. This threat seems disproportionately higher for women who are often overrepresented in internet content, but underrepresented in internet company leadership and decision making. Given this reality, engineering and product decisions will continue to consciously (and unconsciously) exclude our needs and concerns. While social norms are changing towards non-consensual data collection and data exploitation, digital norms seem to be moving in the opposite direction. Advancements in machine learning algorithms and data storage capabilities are only making data misuse easier. Whether the outcome is revenge porn or targeted ads, surveillance or discriminatory AI, if we want a world where our data can retire when it's outlived its time, or when it's directly harming our lives, we must create the tools and policies that empower data subjects to have a say in what happens to their data. including allowing their data to die."

Authors

Types