Nausett IV ACM-15 - History

Nausett IV ACM-15 - History


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Nausett IV
(ACM-15: dp. 910, 1. 189, b. 37', dr. 12', s. 12 k.; cpl. 135 GL ACM-I I )

Originally built as a mine planter for the U.S. Army, auxiliary mine layer ACM-15 was transferred to the custody of the Navy in March 1951. Never commissioned, ACM-IS was first berthed at Charleston as a unit of the Atlantic Reserve Fleet. Later moved to Green Cove Springs, Florida, she remained in reserve until struck from the Navy List, 1 July 1960. During that time she was redesignated MMA-lo, 7 February 1955, and named Nausett, 1 May 1955. After being struck, Nausett was stripped and sold, 17 May 1961, to Caribbean Enterprises, Ine., Miami, Florida.


Stroustrup was born in Aarhus, Denmark. His family was working class, and he went to the local schools. [7]

He attended Aarhus University 1969–1975 and graduated with a master's degree in mathematics and computer science. His interests focused on microprogramming and machine architecture. He learned the fundamentals of object-oriented programming from its inventor, Kristen Nygaard, who frequently visited Aarhus.

In 1979, he received a PhD in computer science from the University of Cambridge, [8] where he was supervised by David Wheeler. [1] [9] His thesis concerned communication in distributed computer systems. [10]

In 1979, Stroustrup began his career as a member of technical staff in the Computer Science Research Center of Bell Labs in Murray Hill, New Jersey, USA. There, he began his work on C++ and programming techniques. Stroustrup was the head of AT&T Bell Labs' Large-scale Programming Research department, from its creation until late 2002. In 1993, he was made a Bell Labs fellow and in 1996, an AT&T Fellow.

From 2002 to 2014, Stroustrup was the College of Engineering Chair in Computer Science Professor at Texas A&M University. [11] [12] From 2011, he was made a University Distinguished Professor.

As of January 2014, Stroustrup is a Technical Fellow and Managing Director in the technology division of Morgan Stanley in New York City and a Visiting Professor in Computer Science at Columbia University. [13]

Stroustrup is best known for his work on C++. In 1979, he began developing C++ (initially called "C with Classes"). In his own words, he "invented C++, wrote its early definitions, and produced its first implementation [. ] chose and formulated the design criteria for C++, designed all its major facilities, and was responsible for the processing of extension proposals in the C++ standards committee." C++ was made generally available in 1985. For non-commercial use, the source code of the compiler and the foundation libraries was the cost of shipping (US$75) this was before Internet access was common. Stroustrup also published a textbook for the language in 1985, The C++ Programming Language. [14]

The key language-technical areas of contribution of C++ are:

  • A static type system with equal support for built-in types and user-defined types (that requires control of the construction, destruction, copying, and movement of objects and operator overloading).
  • Value and reference semantics.
  • Systematic and general resource management (RAII): constructors, destructor, and exceptions relying on them.
  • Support for efficient object-oriented programming: based on the Simula model with statically checked interfaces, multiple inheritance, and efficient implementation based on virtual function tables.
  • Support for flexible and efficient generic programming: templates with specialization and concepts.
  • Support for compile-time programming: template metaprogramming and compile-time evaluated functions ("constexpr functions").
  • Direct use of machine and operating system resources.
  • Concurrency support through libraries (where necessary, implemented using intrinsics).

Stroustrup documented his principles guiding the design of C++ and the evolution of the language in his 1994 book, The Design and Evolution of C++, [15] and three papers for ACM's History of Programming Languages conferences. [16] [17] [18]

Stroustrup was a founding member of the C++ standards committee (from 1989, it was an ANSI committee and from 1991 an ISO committee) and has remained an active member ever since. For 24 years he chaired the subgroup chartered to handle proposals for language extensions (Evolution Working Group).

Awards and honors Edit

  • 2018: The Charles Stark Draper Prize from The US National Academy of Engineering for conceptualizing and developing the C++ programming language.
  • 2018: The Computer Pioneer Award from The IEEE Computer Society for bringing object-oriented programming and generic programming to the mainstream with his design and implementation of the C++ programming language.
  • 2017: The Faraday Medal from the IET (Institute of Engineering Technology) for significant contributions to the history of computing, in particular pioneering the C++ programming language.
  • 2010: The University of Aarhus's Rigmor og Carl Holst-Knudsens Videnskabspris.
  • 2005: The William Procter Prize for Scientific Achievement from Sigma Xi (the scientific research society) as the first computer scientist ever.
  • 1993: The ACM Grace Murray Hopper award for his early work laying the foundations for the C++ programming language. Based on those foundations and Dr. Stroustrup's continuing efforts, C++ has become one of the most influential programming languages in the history of computing.
  • Member of the National Academy of Engineering in 2004. of the Association for Computing Machinery (ACM) in 1994.
  • Fellow of the Institute of Electrical and Electronics Engineers (IEEE) in 1994.
  • Fellow of the Computer History Museum for his invention of the C++ programming language in 2015.
  • Honorary Fellow of Churchill College, Cambridge in 2017.

Honorary doctorates and professorships

  • He was awarded an honorary doctorate from the University Carlos III, Spain 2019. [20]
  • Stroustrup has been a noble doctor at ITMO University since 2013. [21]
  • Honorary Professor in Object Oriented Programming Languages, Department of Computer Science, University of Aarhus. 2010.

Publications Edit

Stroustrup has written or co-written a number of publications, [22] [23] including the books:

  • A Tour of C++ (1st and 2nd edition) [24]
  • Programming: Principles and Practice Using C++[25]
  • The C++ Programming Language (1st, 2nd, 3rd, and 4th edition) [26]
  • The Design and Evolution of C++[27]
  • The Annotated C++ Reference Manual. [28]

In all, these books have been translated into 21 languages. [29]

More than 100 academic articles, including:

  • B. Stroustrup: Thriving in a crowded and changing world: C++ 2006-2020. ACM/SIGPLAN History of Programming Languages conference, HOPL-IV. London. June 2020.
  • B. Stroustrup: Evolving a language in and for the real world: C++ 1991–2006. ACM HOPL-III. June 2007.
  • B Stroustrup: What should we teach software developers? Why? CACM. January 2010.
  • Gabriel Dos Reis and Bjarne Stroustrup: A Principled, Complete, and Efficient Representation of C++. Journal of Mathematics in Computer Science Volume 5, Issue 3 (2011), Page 335–356 doi:10.1007/s11786-011-0094-1. Special issue on Polynomial System Solving, System and Control, and Software Science.
  • Gabriel Dos Reis and Bjarne Stroustrup: General Constant Expressions for System Programming Languages. SAC-2010. The 25th ACM Symposium on Applied Computing. March 2010.
  • Y. Solodkyy, G. Dos Reis, and B. Stroustrup: Open and Efficient Type Switch for C++. Proc. OOPSLA'12.
  • Peter Pirkelbauer, Yuriy Solodkyy, Bjarne Stroustrup: Design and Evaluation of C++ Open Multi-Methods. In Science of Computer Programming (2009). Elsevier Journal. June 2009. doi:10.1016/j.scico.2009.06.002.
  • Gabriel Dos Reis and Bjarne Stroustrup: Specifying C++ Concepts. POPL06. January 2006.
  • B. Stroustrup: Exception Safety: Concepts and Techniques. In Springer Verlag Lecture Notes in Computer Science, LNCS-2022. ISSN 0302-9743. ISBN3-540-41952-7. April 2001.
  • B Stroustrup: Generalizing Overloading for C++2000. Overload, Issue 25. 1 April 1998.
  • B. Stroustrup: Why C++ isn't just an Object-Oriented Programming Language. Addendum to OOPSLA'95 Proceedings. OOPS Messenger, vol 6 no 4, pp 1–13. October 1995.
  • B. Stroustrup: A History of C++: 1979–1991. Proc ACM History of Programming Languages conference (HOPL-2). ACM Sigplan Notices. Vol 28 No 3, pp 271–298. March 1993. Also, History of Programming languages (editors T.J.Begin and R.G.Gibson) Addison-Wesley, 1996.
  • B. Stroustrup: What is Object-Oriented Programming? (1991 revised version). Proc. 1st European Software Festival. February 1991.
  • B. Stroustrup: Data Abstraction in C. Bell Labs Technical Journal. vol 63. no 8 (Part 2), pp 1701–1732. October 1984.
  • B. Stroustrup: Classes: An Abstract Data Type Facility for the C Language. Sigplan Notices, January 1982.

More than a hundred technical reports for the C++ standards committee (WG21) [30]


Quantitative diagnosis of apical cardiomyopathy using contrast echocardiography

Background: The echocardiographic diagnosis of apical hypertrophic cardiomyopathy (ACM) has been limited by the frequent inability to visualize the apical endocardium. We hypothesized that the use of contrast agents in patients with suspected ACM, but nondiagnostic echocardiographic studies, would allow quantitative diagnosis.

Methods: Contrast enhancement was performed in 26 patients with nondiagnostic transthoracic echocardiograms (TTEs) for the diagnosis of ACM 6 patients with suspected ACM based on unexplained symmetric precordial T-wave inversions and increased apical tracer uptake on single-photon emission computed tomography (SPECT) scans, 10 patients with normal electrocardiogram (ECG) readings and no history of hypertension (healthy group), and 10 patients with hypertension and ECG criteria for left ventricular hypertrophy (LVH group). Images were obtained with Optison (Mallinckrodt Medical IV, 1.0 mL) using harmonic imaging and low mechanical index. Posterior (PW) and septal wall (SW) thicknesses were measured at end-diastole in the parasternal long-axis view. Apical wall thickness (A) was measured from the contrast-enhanced apical endocardium to the visceral epicardial surface in the apical 4-chamber view. A/PW and A/SW ratios were calculated for each group. Asymmetric apical hypertrophy was defined as an A/PW ratio greater than 1.5.

Results: Contrast-enhanced apical thickness was greater than 2.0 cm in all patients in the suspected ACM group but less than 1.2 cm in all patients in the LVH and healthy groups. In all 6 patients in the suspected ACM group, A/PW and A/SW ratios were greater than 1.5. No patient in the healthy or LVH groups had thickness ratios greater than 0.85.

Conclusion: Contrast echocardiography allows quantitative diagnosis of ACM in patients with suggestive ECG and SPECT but nondiagnostic TTEs. This study suggests that contrast echocardiography should be performed before using more expensive or invasive diagnostic testing for this condition.


Appleton family [ edit | edit source ]

    , publisher , writer , 1st Lady of the United States, wife of President Franklin Pierce , 2nd President of Bowdoin College
  • Hon.Chief Justices of the Maine Supreme Judicial Court , Union colonel in the American Civil War, was made brevet brigadier general for his bravery in the United States Volunteers , studied at Phillips Andover Academy, graduated from Harvard in 1813, appointed secretary of the legation in the Netherlands (1817-1819), secretary of the legation at Rio de Janeiro, Brazil, nominated to serve as charge d'affaires there (1820-1821), this was not confirmed by the United States, charge d'affaires in Stockholm, Sweden (1826-1830), appointed special representative to the Kingdom of Naples in 1825 , Member of the U.S. House of Representatives from Massachusett's 1st district (4 Mar 1831-3 Mar 1833) and 9 Jun 1842-28 Sep 1842), member of the American Academy of Science and Art and of the Massachusetts Historical Society
  • Samuel Appleton (1766-1853), merchant prince, at the time of his death, his net worth was almost at $1,000,000 , writer, artist and patron of the fine arts , Member of the U.S. House of Representatives from Massachusetts, trader, shipowner and banker , publisher, joined his father's publishing business D. Appleton & Company in 1838, became senior member of the company in 1848 and it's representative in London, England in 1853 , educated at Hopkinson's School for Boys, graduated from Harvard n 1896, a key player in the preservation of historic homes in New England

Dynamics of Gender Bias in Computing

In May 1948, women were strikingly prominent in ACM. Founded just months earlier as the "Eastern Association for Computing Machinery," the new professional society boldly aimed to "advance the science, development, construction, and application of the new machinery for computing, reasoning, and other handling of information." 36 No fewer than 27 women were ACM members, and many were leaders in the emerging field. a Among them were the pioneer programmers Jean Bartik, Ruth Lichterman, and Frances Snyder of ENIAC fame the incomparable Grace Murray Hopper who soon energized programming languages Florence Koons from the National Bureau of Standards and U.S. Census Bureau and noted mathematician-programmer Ida Rhodes. 26 During the war, Gertrude Blanch had organized a massive human computing effort (a mode of computation made visible in the 2016 film Hidden Figures 47 ) and, for her later service to the US Air Force, became "one of the most well-known computer scientists and certainly the most visible woman in the field." 24,25 Mina Rees, a mathematics Ph.D. like Hopper and Blanch, notably funded mathematics and computing through the Office of Naval Research (1946&ndash1953), later serving as the first female president of the American Association for the Advancement of Science. In 1949, Rees was among the 33 women (including at least seven ACM women) who participated in an international conference at Harvard University, chairing a heavyweight session on "Recent Developments in Computing Machinery." 29

Key Insights

Their prominence has led to the widespread but inaccurate impression that women were numerically dominant in early computer programming. As one account puts it, "at its origins, computer programming was a largely feminized occupation." 18,19 This view, resting on suggestive but fragmentary data, has become prominent in popular culture, scholarship, and mass media, including the Wall Street Journal and National Public Radio and the widely acclaimed 2015 documentary "Code: Debugging the Gender Gap" by Robin Hauser Reynolds. 14,41 The film popularized the conjecture by some scholars that "women made up 30% to 50% of all programmers" in the 1950s or 1960s and that male programmers subsequently pushed them out. Porter writes, "By the 1960s, women made up 30% to 50% of all programmers, according to [historian] Ensmenger" (specifically citing the Robin Hauser film). 46

A recent article 45 in Communications of the ACM approvingly cites one such source positing a binary switch from a female-dominated field to a male-dominanted one. There, Mundy clearly states the linear view: "after World War II, software programming was considered rote and unglamorous, somewhat secretarial&mdashand therefore suitable for women. The glittering future, it was thought, lay in hardware. But once software revealed its potential&mdashand profitability&mdashthe guys flooded in and coding became a male realm." 43 It seems widely accepted that men actively remade computer programming from a female- into a male-dominated field during the 1960s or 1970s just as computer science was professionalizing itself through expansion of research, professional societies, and higher education. This accepted view posits that computing was born female and then made masculine, with a simple linear dynamic leading straight to today's male-dominated profession. One implication of this conjecture is that gender bias was an inherent part of (male-driven) professionalization in computing. In varied forms, "many computer programmers embraced masculinity as a powerful resource for establishing their professional identity and authority," in Ensmenger's formulation. 19

The 'Linear Model' Is Too Simple

In the absence of systematic data on gender in the computing workforce, prior to the 1970 U.S. Census, such a linear model once seemed plausible. 38 It was furthermore supported by fragmentary and sometimes cherry-picked evidence and buttressed by theoretical claims about the nature of professionalization. b But it is too simple. To start, we need systematic, longitudinal data. For deeper insight on women in computing during these years, this article presents a new dataset with more than 50,000 individuals tabulated by their first (given) names, an indicator of ascribed gender (if not gender identity). The results may be surprising. In 1948, the 27 named ACM women, alongside 330 named ACM men, constituted 7.6% of its membership. Similarly, women were 8.6% and 7.6% of ACM members in 1949 and 1952 and women constituted 7.6% and 5.3% of ACM conference attendees in 1950 and 1952. Women were 5.7% of the 1949 Harvard conference. A retrospective celebration 50 suggests women were 12.7% of the Univac pioneers from 1951 (see Figure 1). c This data does not support the common conjecture that women numerically dominated early computing.


Figure 1. Women's participation in conferences/ACM members (1948&ndash1953).

The "pipeline" model is a related linear view, now widely criticized. In Berryman's influential 1983 Rockefeller Foundation report, 5 the pipeline metaphor helped identify the different reasons for underrepresentation in the quantitative sciences of African Americans, Hispanics, and American Indians, with structural "losses from the educational pipeline" beginning in high school as well as personal "field choices" (for example, college major) shaping patterns of underrepresentation. For computer science, Camp expanded on Berryman's findings for women that losses were concentrated in a latter stage (from bachelor's to doctoral degrees). 12 In computing, the pipeline model posited a one-way decline of women, from the 1980s, noting that the proportion of women "fell" at each career "stage" from undergraduate student through graduate school and on to full professor. Moshe Vardi recently voiced concern about "puncturing the recruiting pipeline." 51

A recent critique asks: "What's wrong with the pipeline? Everything. The pipeline assumes a passive flow of women (and men) from one stage to the next culminating in a scientific career. Women's underrepresentation in science results then from their leakage from the pipeline." 9 Such a linear model inadequately acknowledges women's diverse career paths and non-academic career stages, better conceptualized as non-linear "pathways." Fox and Kline caution that "women may linger as tenured associate professors without attaining full rank" and so not fully participate in academic decision-making and professional leadership, even while nominally still within the pipeline in their view the "pathways" model is a better guide to the "dynamic &hellip features and forces" of institutional settings, procedures, policies, and cultures in which women faculty members do not always experience orderly, expected, sequential or unidirectional progression through career ranks. 20 Clearly, much more needs to happen than merely "keeping women in the pipeline." 9,52

To evaluate the 'making programming masculine' thesis and scrutinize the linear-pipeline view, the Charles Babbage Institute analyzed membership and attendee lists of six computer-user groups with available archival records. 41,53 Two of the largest user groups were formed in 1955. SHARE (for IBM computers) and USE (Sperry-Rand Univacs) provided a means for diverse companies, financial institutions, federal agencies and laboratories, and international entities to share algorithms and program code, to identify and address practical problems, to develop novel technical and organizational solutions&mdashand, not least, to give sharp feedback to manufacturers. Both groups compiled attendee lists for their twice-yearly meetings, and many of these list first names.

First names, suitably analyzed and methodically tallied, indicate gender in addition, committee reports identify hundreds of attendees as "Mr." or "Mrs." or "Miss" oral histories identify others and the Social Security Administration tabulates all given U.S. birth names by ascribed gender since 1880. 32 Between 80% and 100% of user-group attendees can be gender-identified. 41 Available records also give insight into Control Data's Coop, Burroughs' CUBE, Digital's DECUS, and the best-selling Mark IV software package for IBM computers. For each user-group, a time-series shows the participation of women in professional computing and indicates the rate of growth. The user-group attendees are taken to be samples of the computing workforce. No single user-group, with the possible exception of SHARE, is anything like a representative sample.

All such historical statistics, including government-compiled ones, are formed from sources of data that vary in uniformity (for example, direct personal surveys, company personnel reports, trade literature assessments, and industry or trade-group statistics) "uniform data" for historical statistics are always created by researchers, compilers, and analysts. 2,3 This present longitudinal dataset is the largest available for assessing changes in women's participation in the computing workforce (trade journals occasionally conducted one-time salary surveys 27 )&mdashuntil data from the U.S. Census and Bureau of Labor Statistics in the 1970s. The research method introduced here might be used to create longitudinal data, now lacking or fragmentary, on women in the STEM workforce. This systematic approach convincingly supplants earlier studies' reliance on fragmentary data or anecdotal evidence drawn from scattered or non-representative observations.

Figures 2a&ndashf present new time-series data on women's participation in the U.S. computing workforce from 1955 to 1989. Each graph's x-axis gives the years from available archival records d the y-axis, the percentage of women identified by first names and the bubble area, the total analyzed population for each year. Individuals with gender-ambiguous or initials-only names are included in the bubble area (N) but are set aside for tabulation of women's participation. The data establishes varied growth across the 1960s and into the 1980s. Women's participation in SHARE grew slowly but steadily from 1955 to 1973, when, with thousands of attendees, it shifted to initials-only names. Women's participation as SHARE officer-managers similarly grew from 1968 to 1989, with a higher R 2 value supporting the upward linear-trend line. (R 2 is a standard linear regression measure of the 'goodness of fit' of computed trendlines with the underlying data: technically, R 2 is the percent of variation in dependent variable [%-women] that can be attributed to variation in independent variable [years]. All trendlines and statistics computed by Mac Numbers 4.3.1.) Notably, after women officer-managers reached 26.5% in 1989, a wider measure of women as SHARE meeting speakers was lower at 16.8% (N=491) and 19.4% (N=443) during 1991&ndash1992. Women's participation grew steadily in USE during 1955&ndash89, in CDC's Coop during 1959&ndash1964, and in Burroughs' CUBE during 1962&ndash1976, all with moderate R 2 values. Data from the Mark IV software user group 1969&ndash1981, shows strong growth (R 2 =0.94) with women's participation reaching 30%.


Figure 2. Women's participation in user groups (1955&ndash1989).

Gender Bias Is Non-Linear

Figure 3 combines the membership and user-group data across 1948&ndash1995, adding the available federal workforce statistics and US computer-science bachelor degrees from the Bureau of Labor Statistics and NSF, respectively. For clarity, this graph simplifies the time-series data through plotting the underlying trend lines. Figure 3 shows decidedly non-linear dynamics, with varied growth rates and significant declines. The trendlines indicate unmistakable growth 1960s&ndash1980s in women's participation in the computing workforce, refuting the commonly held "linear model" and any supposed masculine takeover. This user-group data tallies with salary surveys, e company-wide group photographs, f and the NSF and BLS/US Census data.


Figure 3. Women in computing 1948&ndash1995.

At least three distinct periods may be discerned. First: From 1948 through around 1960, women were a numerically small proportion of the computing community (ranging from 0 to around 10%). There is no systematic data&mdashhere or elsewhere&mdashthat women were anything like 30% to 50% of the skilled white-collar computing workforce until the 1980s. Growth was modest (see 'USE' [slope = 0.0008]). The apparent sharp growth in CDC [N=371] reflects two years 1959&ndash1960 with zero women data for 񟬎s' is not a proper time-series. Second: From the 1960s through the 1970s, women in computer-user groups grew steadily if slowly to reach roughly 12% to 20% (see 'CUBE' and 'SHARE' [slopes = 0.0031, 0.0016]). Women were entering computing during these years&mdashdespite the linear model's speculation about them leaving. (The only time-series showing any downward drift is DECUS in 1968, 1972, 1976 [N=2,116] easing from 9.5% to 8.2% women.) Subsequent data through the mid-1980s suggest accelerating growth in women's participation in computing (see 'SHARE-Mgmt' and 'Mark IV' [slopes = 0.007 and 0.022]). Women attending USE grew to reach 15% in the mid-1980s women officer-managers of SHARE grew to 26% in 1989 and women attending Mark IV conferences grew to 30% in 1980. These data are consistent with the U.S. Census reporting 22.5% women in the computing workforce (1970) and with the peak years for women's participation in the mid-1980s. 22 Third: women indeed left computing&mdashafter the peak in the mid-1980s&mdashand this is what has persisted to the present. According to the CRA Taulbee survey, women's share of computer-science bachelor's degrees fell to 11.2% (2009). The U.S. Census American Community Survey reported women constituted 27% of the computing workforce (in 2011), a precipitous drop from the mid-1980s peak of 38%. 38

There is no systematic data&mdashhere or elsewhere&mdashthat women were anything like 30%&ndash50% of the skilled white-collar computing workforce until the 1980s.

These three periods demonstrate a non-linear dynamic for gender bias in computing. Instead of one question based on conjecture&mdash"when" did women leave computing?&mdashwe now face distinct data-driven research questions. How did women establish a significant presence in the nascent high-skilled computer field in the 1950s? Men solidly dominated the fields that early computing drew on most heavily, such as engineering, g physics, and mathematics h and yet computing women took up positions of responsibility and leadership such as Frances Holberton (née Snyder), Grace Hopper, Mina Rees, and many others. Why was women's growth in the computing workforce steady although slow through the mid-1960s? What attracted so many women into computing just as it professionalized during roughly 1965&ndash1985? Computing among scientific and technical fields stood out for its expanding hospitality to women during these two decades, and we should be alert for useful lessons. And, finally, how to understand the exodus of women beginning in the late-1980s that afflicts computing through today?

Research for the Future

Further research is necessary to address these new questions, but it's clear the worrisome sea-change in computing during the late 1980s and 1990s accompanied dramatic cultural shifts. These include the rise of personal computing, gendered avatars in computer gaming, and the media's lionization of male "nerds." The nerd image, which had been previously ambiguous, flexible, and rhetorically situated distant from power, "gets rehabilitated and partially incorporated into hegemonic masculinity" beginning in the 1980s. 34 (Hegemonic masculinity can be defined as the "configuration of gender practice [that] guarantees [or is taken to guarantee] the dominant position of men and the subordination of women." 17 ) Popular media such as "Revenge of the Nerds" (1984) and "Triumph of the Nerds" (1996) sharpened the nerd image as a computing male. And nerds became allied with power. Wired magazine offered up Nicholas Negroponte, Stewart Brand, George Gilder, and John Perry Barlow in the 1990s. "Wired is about the most powerful people on the planet today&mdashthe Digital Generation," stated its co-founder. Bill Gates graced its cover five times in 15 years (and later gained a sixth with Mark Zuckerberg). 37,55,56 Today, many researchers target computing's gender-slanted culture, ingrained stereotypes, and associated public images as promising sites for positive intervention. 15,16,21,31,33

The labor-intensive research method reported here might be automated by linking meeting and membership records with the SSA dataset. 32 As a pilot, I analyzed the 1949 ACM roster (N=435) in two ways. First, I did manual spreadsheet tallies of listed individuals as woman's, man's, initials-only, or gender-ambiguous name as usual, I resolved gender-unclear names though contextual-archival linking or the SSA dataset. Second, I drew on the SSA dataset (year-of-birth = 1925) to directly compute the gender probabilities of each name. All but three "male" names (n=160) had 95% or greater probability of being male. Noel (91%), Francis (90%), and Jan (45%) were the exceptions in this instance, it was Jan Rajchman, the noted RCA Laboratory engineer and IAS computer designer. Only one "female" name (n=27) had less than a 99% probability of being female. Jean is a woman's name in the U.S. (97.5%) but a Francophone male name the 'Jeans' from, for example, Hydro-Québec attending these meetings indicate the need for contextual knowledge to correctly infer gender. In addition, 15 first names did not appear in the SSA dataset and were set aside. A weighed sum of the "male" and "female" name probabilities directly computed with one minor adjustment (resolving eight "initials-only" ACM members who were well-known men, namely JH Boekhoff, JG Brainerd, H Campaign, JJ Eachus, RW Hamming, CC Hurd, CC Gotlieb, and MV Wilkes) predicted that 8.52% of that year's ACM members were women, close to the manually tabulated 8.55% women. APIs exist for inferring gender from first names, 42,49 and some may deal with temporal changes in ascribed gender for such names as "Robin" or "Leslie" or even international names beyond the U.S.-based SSA dataset. 48

Women's advances in the computing profession from the 1960s through the 1980s deserve special scrutiny today in these years, computing was attractive to literally thousands of women programmers, systems analysts, database specialists, and middle managers.

Women's advances in the computing profession from the 1960s through the 1980s deserve special scrutiny today in these years, computing was attractive to literally thousands of women programmers, systems analysts, database specialists, and middle managers. It is a mistaken notion that computing was somehow "made masculine" during these years when, in fact, women were flooding into the profession&mdashattending professional meetings, participating in computer-user groups, and earning an increasing share of computer-science bachelor's degrees. The "making programming masculine" thesis has unwittingly obscured the very years when women found computing to be an exciting field where their technical talents could be actively exercised and professionally rewarded. 1,10,28,40,57 Recent retirements of top women executives at IBM, HP, and Xerox underscore the peak years of the 1980s when these women launched computing careers and when the field was nearly 40% women. i

More detailed gender-analysis of membership lists and conference attendees of ACM's numerous SIGs could shed light on which branches of computer science evinced greater or lesser openness to women's participation. Some branches of computer security had especially noteworthy women's leadership. For example, pioneering intrusion-detection research was led by Dorothy Denning, Teresa Lunt, Debra Anderson, Rebecca Bace, and others. 40,58 HCI has focused research attention on gender. 6,11,54 Recent findings suggest gender bias may be endemic in the content of machine learning, as expressed in the meme "Man is to Computer Programmer as Woman is to Homemaker." 4,8,35 Data beyond user groups is desirable. ACM members likely possess SIG records that could advance our understanding of the dynamics of gender bias in computing. ACM's History Committee recently launched a SIG-focused archiving initiative. 39 A large-scale data-gathering effort could empirically analyze what computing did right during the 1960s-1980s&mdashfocusing on specific SIGs and subfields&mdashas well as what went wrong during the 1990s and beyond. If the preliminary research reported here is extended, perhaps the hard problem of gender bias in computing can be made tractable.

Acknowledgments. This research was supported by Alfred P. Sloan Foundation grant G-B2014-07.

Full data for all the figures in this article is available at https://tjmisa.com/papers/2021-06_CACM-data.zip.

References

1. Abbate, J. The pleasure paradox: Bridging the gap between popular images of computing and women's historical experiences. Gender Codes. T. Misa, (Ed), 211&ndash227.

2. Anderson, M. The history of women and the history of statistics. J. Women's History 4, 1 (1992), 14&ndash36 https://muse.jhu.edu/article/363006.

3. Anderson, M. The Census, audiences, and publics. Social Science History 32, 1 (2008), 1&ndash18 https://muse.jhu.edu/article/232094.

4. Babaeianjelodar, M., Lorenz, S., Gordon, J., Matthews, J., and Freitag, E. Quantifying gender bias in different corpora. In WWW ཐ: Companion Proceedings of the Web Conf. (Apr. 2020), 752&ndash759 https://dl.acm.org/doi/10.1145/3366424.3383559

5. Berryman, S.E. Who will do science? Minority and female attainment of science and mathematics degrees: Trends and causes. Rockefeller Foundation, New York, NY, 1983.

6. Beckwith, L., Burnett, M., Grigoreanu, V., Wiedenbeck, S. Gender HCI: What about the software? Computer 39, 11 (2006), 97&ndash101 https://ieeexplore.ieee.org/document/4014778.

7. Bix, A.S. From 'engineeresses' to 'girl engineers' to 'good engineers:' A history of women's U.S. engineering education. NWSA J. 16, 1, (2004), 27&ndash49 https://muse.jhu.edu/article/168384.

8. Bolukbasi, T., Chang, K. W., Zou, J., Saligrama, V., Kalai, A. Man is to computer programmer as woman is to homemaker? Debiasing Word embeddings. July 21, 2016 arXiv:1607.06520 https://arxiv.org/abs/1607.06520

9. Branch, E.H. Pathways, Potholes, and the Persistence of Women in Science: Reconsidering the Pipeline. Lexington Books, Lanham MD, 2016.

10. Buckholtz, E. Queens of code. IEEE Annals of the History of Computing 42, 2 (2020 55&ndash62.

11. Burnett, M., Peters, A., Hill, C., and Elarief, N. Finding gender-inclusiveness software issues with GenderMag: A field investigation. In Proceedings of the 2016 CHI Conf. on Human Factors in Computing Systems. ACM, New York, NY, 2586&ndash2598

12. Camp, T. The incredible shrinking pipeline. Commun. ACM 40, 10 (Oct. 1997), 103&ndash110 https://dl.acm.org/doi/10.1145/262793.262813.

13. Canning, R. Issues in programming management. EDP Analyzer 12, 4 (1974), 1&ndash14.

14. Cass, S. A Review of Code: Debugging the Gender Gap. IEEE Spectrum (June 19, 2015) http://bit.ly/3q6gUgI

15. Cheryan, S., Plaut, V.C., Handron, C., and Hudson, L. The stereotypical computer scientist: Gendered media representations as a barrier to inclusion for women. Sex Roles: A Journal of Research 69, 1&ndash2 (2013), 58&ndash71.

16. Clayton, K.L., Von Hellens, L.A. and Nielsen, S.H. Gender stereotypes prevail in ICT: A research review. In Proceedings of the SIGMIS 47 th Annual Conf. Computer Personnel Research. ACM, New York, NY, 2009, 153&ndash158 https://dl.acm.org/doi/10.1145/1542130.1542160.

17. Connell, R.W. Masculinities. University of California Press, Berkeley, CA, 1995, 77.

18. Ensmenger, N. Making programming masculine. Gender Codes. T. Misa, (Ed.), 115&ndash141.

19. Ensmenger, N. 'Beards, sandals, and other signs of rugged individualism:' Masculine culture within the computing professions. Osiris (2015), 38&ndash65 https://www.journals.uchicago.edu/doi/10.1086/682955.

20. Fox, M.F. and Kline, K. Women faculty in computing: A key case of women in science. Pathways, Potholes, and the Persistence of Women in Science. E.H. Branch, (ed), 41&ndash55.

21. Frieze, C. Diversifying the images of computer science: Undergraduate women take on the challenge. SIGCSE Bulletin 37, 1 (Feb. 2005), 397&ndash400 https://dl.acm.org/doi/10.1145/1047124.1047476.

22. Gilchrist, B. and Weber, R.E. Enumerating full-time programmers. Commun. ACM 17, 10 (Oct. 1974), 592&ndash593 https://dl.acm.org/doi/10.1145/355620.361177.

23. Green, J. and LaDuke, J. Pioneering Women in American Mathematics: The Pre-1940 Ph.D.s. American Mathematical Society, Providence, R.I, 2008.

24. Grier, D.A. Gertrude Blanch of the Mathematical Tables Project. IEEE Annals of the History of Computing 19, 4 (1997), 18&ndash27 https://ieeexplore.ieee.org/document/627896.

25. Grier, D.A. Ida Rhodes and the dreams of a human computer. IEEE Annals of the History of Computing 22, 1 (2000), 82&ndash85 https://ieeexplore.ieee.org/document/815468

26. Gurer, D.W. Women's contributions to early computing at the National Bureau of Standards. IEEE Annals of the History of Computing 18, 3 (1996), 29&ndash35 https://ieeexplore.ieee.org/document/511941.

27. Haigh, T. Masculinity and the machine man: Gender in the history of data processing. Gender Codes. T. Misa, (Ed). John Wiley, 2010, 51&ndash71.

28. Halvorson, M.J. Code Nation: Personal computing and the learn to program movement in America. ACM, New York, NY, 2020 https://dl.acm.org/doi/book/10.1145/3368274.

29. Harvard University. Second Symposium on Large-Scale Digital Calculating Machinery (Sept. 11&ndash16, 1949) https://bit.ly/2XAec6U

30. Herzig, A.H. Becoming mathematicians: Women and students of color choosing and leaving doctoral mathematics. Rev. Educational Research 74, 2 (2004), 171&ndash214 https://www.jstor.org/stable/3516055.

31. Jia, S., Lansdall-Welfare, T., and Cristianini, N. Measuring gender bias in news images. In Proceedings of the 24 th Intern. Conf. World Wide Web. ACM, New York, NY, 2015, 893&ndash898 https://dl.acm.org/doi/10.1145/2740908.2742007

32. Karimi, F., Wagner, C., Lemmerich, F., Jadidi, M., and Strohmaier, M. Inferring gender from names on the Web: A comparative evaluation of gender detection methods. In Proceedings of the 25 th Intern. Conf. Companion on World Wide Web. Intern. World Wide Web Conf. Steering Committee, Republic and Canton of Geneva, Switzerland, 2016, 53&ndash54 https://dl.acm.org/doi/10.1145/2872518.2889385.

33. Kay, M., Matuszek, C., and Munson, S.A. Unequal representation and gender stereotypes in image search results for occupations. In Proceedings of the 33 rd Annual ACM Conf. on Human Factors in Computing Systems. ACM, New York, NY, 2015, 3819&ndash3828 https://dl.acm.org/doi/10.1145/2702123.2702520.

34. Kendall, L. Nerd nation: Images of nerds in US popular culture. Intern. J. Cultural Studies 2, 2 (1999), 260&ndash283 https://journals.sagepub.com/doi/10.1177/136787799900200206.

35. Leavy, S. Gender bias in artificial intelligence: The need for diversity and gender theory in machine learning. In Proceedings of the 1 st ACM/IEEE Intern. Workshop on Gender Equality in Software Engineering (May 28, 2018), 14&ndash16 https://dl.acm.org/doi/10.1145/3195570.3195580.

36. Longo, B. Edmund Berkeley and the Social Responsibility of Computer Professionals. ACM Morgan & Claypool, New York, NY, 2015 https://dl.acm.org/doi/book/10.1145/2787754.

37. Millar, M.S. Cracking the Gender Code. Second Story Press, Toronto, Canada, 1998, 96&ndash107.

38. Misa, T.J. Gender Codes: Why Women Are Leaving Computing. John Wiley, Hoboken, NJ, 2010.

39. Misa, T.J. Computing is history. Commun. ACM 58, 10 (Oct. 2015), 35&ndash37 https://dl.acm.org/doi/10.1145/2814845.

40. Misa, T.J. 2016. Communities of Computing: Computer Science and Society in the ACM. ACM Morgan & Claypool, 2016 https://dl.acm.org/doi/book/10.1145/2973856.

41. Misa, T.J. Gender bias in computing. Historical Studies in Computing, Information, and Society. W. Aspray, (ed). Springer Nature, Switzerland, 2019, 113&ndash133 https://link.springer.com/chapter/10.1007%2F978-3-030-18955-6_6.

42. Mueller, J. and Stumme, G. Gender Inference using Statistical Name Characteristics in Twitter. In Proceedings of the 3 rd Multidisciplinary Intern. Social Networks Conference on Social Informatics. Data Science 2016. ACM, New York, NY, https://dl.acm.org/doi/10.1145/2955129.2955182.

43. Mundy, L. Why is Silicon Valley so awful to women? The Atlantic (Apr. 2017). 60&ndash73 http://bit.ly/3sfs6cU.

44. Murray, M.A.M. Women Becoming Mathematicians: Creating a Professional Identity in Post-World War II America. MIT Press, Cambridge, MA, 2000.

45. Payton, F.C. and Berki, E. Countering the negative image of women in computing. Commun. ACM 2, 5 (May 2019), 56&ndash63.

46. Porter, J. The fascinating evolution of brogramming and the fight to get women back. Fast Company (Oct. 20, 2014) http://bit.ly/3qcyuQl.

47. Shetterly, M.L. Hidden Figures: The American dream and the untold story of the Black women mathematicians who helped win the space race. William Morrow, New York, NY, 2016.

48. Smith, B.N., Singh, M., and Torvik, V.I. A search engine approach to estimating temporal changes in gender orientation of first names. In Proceedings of the 13 th ACM/IEEE-CS Joint Conf. Digital Libraries. ACM, New York, NY, 2013, 199&ndash208 https://dl.acm.org/doi/10.1145/2467696.2467720.

49. Tran, A. Inferring gender from column of first names in R. Rev. Aug. 21, 2015 gist.github.com/andrewbtran/d3d8e04f5c86dcfa2bb0

50. Univac Pioneers Day. Pioneer Day 1981: UNIVAC I. Annals of the History of Computing 3, 4 (1981), 400&ndash407.

51. Vardi, M.Y. How We Lost the Women in Computing. Commun. ACM 61, 5 (May 2018), 9 https://dl.acm.org/doi/10.1145/3201113

52. Vitores, A. and Gil-Juárez, A. The trouble with 'women in computing': A critical examination of the deployment of research on the gender gap in computer science," J. Gender Studies 25, 6 (2016), 666&ndash680 https://www.tandfonline.com/doi/full/10.1080/09589236.2015.1087309

53. Vogel, W.F. 'The spitting image of a woman programmer': Changing portrayals of women in the American computing industry, 1958&ndash1985. IEEE Annals of the History of Computing 39, 2 (2017), 49&ndash64.

54. Vorvoreanu, M., Zhang, L., Huang, Y. H., Hilderbrand, C., Steine-Hanson, Z., and Burnett, M. From gender biases to gender-inclusive design: An empirical investigation. In Proceedings of the 2019 Conf. Human Factors in Computing Systems Paper 53 (May 2019), 1&ndash14 https://dl.acm.org/doi/10.1145/3290605.3300283

55. Waern, A., Larsson, A., and Nerén, C. Hypersexual avatars: Who wants them? In Proceedings of the 2005 ACM SIGCHI Intern. Conf. Advances in Computer Entertainment Technology. ACM, New York, NY, 2005, 238&ndash241 DOI.

56. Wired. Gates, Zuckerberg meet for Wired cover shoot. (Apr. 19, 2010) http://bit.ly/3oAjuLS/

57. Yost, J. Programming enterprise: Women entrepreneurs in software and computer services. Gender Codes. T. Misa, (ed), 229&ndash250.

58. Yost, J.R. The march of IDES: Early history of intrusion-detection expert systems. IEEE Annals of the History of Computing 38, 4 (2016), 42&ndash54 https://ieeexplore.ieee.org/document/7155454.

Author

Thomas J. Misa is Past President of the Society for the History of Technology (2021&ndash2022) and editorial board member for ACM Books (2013&ndashpresent). He directed the Charles Babbage Institute (2006&ndash2017) at the University of Minnesota and chaired the ACM History Committee (2014&ndash2016).

Footnotes

a. The May 1948 ACM membership roster is in Margaret R. Fox papers (Charles Babbage Institute 45 purl.umn.edu/41420) box 2, folder 9 other ACM rosters in Frances E. Holberton papers (CBI 94 purl.umn.edu/40810) box 23.

b. Ensmenger's 1974 source for "reliable contemporary observers" 18,19 claiming 30-plus percent women programmers in fact mentions women on just two pages: a certain single IBM programming group and a conjecture on women in the "moderating role of 'mother'." 13

c. See UNIVAC Conference 1990, CBI OH 200 purl.umn.edu/104288 and "NCC 1981 Pioneer Day" http://bit.ly/3sim3UT.

d. Archival collections include the Hagley Museum and Library's USE/UNITE records Accession 1881 at findingaids.hagley.org/repositories/3/resources/915 as well as the Charles Babbage Institute's SHARE, USE, Control Data, Burroughs, DECUS, Margaret Fox, and Evan Linick (Mark IV) records at https://bit.ly/3nxsEHG

e. Business Automation in 1960 found women were less than 15% of programmers in its 1971 survey (N=600,000), women were "14% of systems analysts and 21% of computer programmers." 27

f. See photos of attendees at NMAA (1951), ACM (various), and company-wide photographs from Control Data (1962, 1966, 1982) https://bit.ly/3nzMlhR

g. Bix writes, "As late as the 1960s, women still made up less than 1 percent of students studying engineering in the United States." 7 Available data are thin or non-existent for women in specific engineering or science workforces many studies make estimates from educational data.

h. Mathematics prior to 1940 was distinctly open to women, who gained 14% of the field's Ph.D.'s. 23 But during 1945&ndash1960 the number of men gaining math Ph.D.'s roughly tripled while women experienced stasis in numbers and decline in participation (falling to 4.6&ndash9.3% of total math Ph.D.'s). 30,44

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Biographical

Lindstrom, Gary, "Elliott I. Organick: 1925-1985," Comm. ACM , Vol. 29, No. 3, Mar. 1986, p. 231.

Significant Publications

Organick, Elliott Irving, Fortran IV Primer, Addison-Wesley, New York, 1966.

Organick, Elliott Irving, Fortran Primer, Addison-Wesley, New York, 1965.

Organick, Elliott Irving, Interpreting Machines: Architecture and Programming of the B-5000 , North-Holland, Amsterdam, 1978.

Organick, Elliott Irving, Multics System: An Examination of Its Structure, MIT Press, Cambridge, Mass., 1972.

Organick, Elliott Irving, and Loren P. Meissner, Fortran IV, 2nd ed., Addison Wesley, Reading, Mass., 1974.

Organick, Elliott Irving, Alexandra 1. Forsythe, and Robert P. Plummer, Programming Language Structures, Academic Press, New York, 1978.

Original content Copyright © 1995 by the Institute of Electrical and Electronics Engineers Inc.
New content Copyright © 2013-2021 by the IEEE Computer Society and the Institute of Electrical and Electronics Engineers Inc.
All rights reserved. This material may not be reproduced or redistributed without the express written permission of the copyright holder.


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Online Privacy: Regional Differences

Protesters marching in Washington, D.C., in 2013 in opposition to governmental surveillance of telephone conversations and online activity.

Credit: Bill Clark / CQ Roll Call / Getty Images

One of the most controversial topics in our always-online, always-connected world is privacy. Even casual computer users have become aware of how much "they" know about our online activities, whether referring to the National Security Agency spying on U.S. citizens, or the constant barrage of ads related to something we once purchased.

Concerns over online privacy have brought different responses in different parts of the world. In the U.S., for example, many Web browsers let users enable a Do Not Track option that tells advertisers not to set the cookies through which those advertisers track their Web use. Compliance is voluntary, though, and many parties have declined to support it. On the other hand, European websites, since 2012, have been required by law to obtain visitors' "informed consent" before setting a cookie, which usually means there is a notice on the page saying something like "by continuing to use this site, you consent to the placing of a cookie on your computer." Why are these approaches so different?

A Short History

As the use of computers to store, cross-reference, and share data among corporations and government agencies grew through the 1960s and 1970s, so did concern about proper use and protection of personal data. The first data privacy law in the world was passed in the German region of Hesse in 1970. That same year, the U.S. implemented its Fair Credit Reporting Act, which also contained some data privacy elements. Since that time, new laws have been passed in the U.S., Europe, Japan, and elsewhere to try and keep up with technology and citizens' concerns. Research by Graham Greenleaf of the University of New South Wales published in June 2013 (http://bit.ly/ZAygX7) found 99 countries with data privacy laws and another 21 countries with relevant bills under consideration.

There remain fundamental differences in the approaches taken by the U.S., Europe, and Japan, however. One big reason for this, according to Katitza Rodriguez, international rights director of the Electronic Frontier Foundation (EFF), is that most countries around the world regard data protection and privacy as a fundamental rightthat is written into the European Constitution, and is a part of the Japanese Act Concerning Protection of Personal Information. No such universal foundation exists in the U.S., although the Obama administration is trying to change that.

These differences create a compliance challenge for international companies, especially for U.S. companies doing business in regions with tighter privacy restrictions. Several major U.S. firmsmost famously Googlehave run afoul of EU regulators because of their data collection practices. In an acknowledgment of the issue's importance and of the difficulties U.S. businesses can face, the U.S. Department of Commerce has established "Safe Harbor" frameworks with the European Commission and with Switzerland to streamline efforts to comply with those regions' privacy laws. After making certain its data protection practices adhere to the frameworks' standards, a company can self-certify its compliance, which creates an "enforceable representation" that it is following recommended practices.

Data Privacy in the U.S.

EFF's Rodriguez describes data protection in the U.S. as "sectorial." The 1996 Health Insurance Portability and Accountability Act (HIPAA), for example, applies to medical records and other health-related information, but nothing beyond that. "In Europe, they have general principles that apply to any sector," she says.

The U.S. relies more on a self-regulatory model, while Europe favors explicit laws. An example of the self-regulatory model is the Advertising Self-Regulatory Council (ASRC) administered by the Council of Better Business Bureaus. The ASRC suggests placing an icon near an ad on a Web page that would link to an explanation of what information is being collected and allow consumers to opt out however, there is no force of law behind the suggestion. Oddly, Rodriguez points out, while the formal U.S. regulatory system is much less restrictive than the European approach, the fines handed down by the U.S. Federal Trade Commissionwhich is charged with overseeing what privacy regulations there areare much harsher than similar fines assessed in Europe.

The Obama administration, in a January 2012 white paper titled Consumer Data Privacy in a Networked World: A Framework for Protecting Privacy and Promoting Innovation in the Global Digital Economy, outlined seven privacy principles and proposed a Consumer Privacy Bill of Rights (CPBR). It stated that consumers have a right:

  • to expect that data collection and use will be consistent with the context in which consumers provide the data,
  • to secure and responsible handling of personal data,
  • to reasonable limits on the personal data that companies collect and retain,
  • to have their data handled in ways that adhere to the CPBR,
  • to individual control over what personal data companies collect from them and how they use it,
  • to easily understandable and accessible information about privacy and security practices, and
  • to access and correct personal data in usable formats.

The CPBR itself takes a two-pronged approach to the problem: it establishes obligations for data collectors and holders, which should be in effect whether the consumer does anything or even knows about them, and "empowerments" for the consumer. The obligations address the first four principles in the list, while the empowerments address the last three.

Part of the impetus for the CPBR is to allay some EU concerns over U.S. data protection. The framework calls for working with "international partners" on making the multiple privacy schemes interoperable, which will make things simpler for consumers and easier to negotiate for international business.

The EU is concerned with anyone that collects and tracks data, while in the U.S. the larger concern is government surveillance.

There has been little progress on the CPBR since its introduction. Congress has shown little appetite for addressing online privacy, before or after the administration's proposal. Senators John Kerry (now U.S. Secretary of State, then D-MA) and John McCain (R-AZ) introduced the Commercial Privacy Bill of Rights Act of 2011, and Senator John D. Rockefeller IV (D-WV) introduced the Do-Not-Track Online Act of 2013 neither bill made it out of committee. At present, the online privacy situation in the U.S. remains a mix of self-regulation and specific laws addressing specific kinds of information.

Data Privacy in Europe

As EFF's Rodriguez pointed out, the 2000 Charter of Fundamental Rights of the European Union has explicit provisions regarding data protection. Article 8 says,

"Everyone has the right to the protection of personal data concerning him or her. Such data must be processed fairly for specified purposes and on the basis of the consent of the person concerned or some other legitimate basis laid down by law. Everyone has the right of access to data which has been collected concerning him or her, and the right to have it rectified."

Even before the Charter's adoption, a 1995 directive of the European Parliament and the Council of the European Union read, "Whereas data-processing systems are designed to serve man whereas they must, whatever the nationality or residence of natural persons, respect their fundamental rights and freedoms." These documents establish the EU-wide framework and foundation for online privacy rights.

The roots of the concern, says Rodriguez, lie in the countries' memory of what happened under Nazi rule. "They understand that state surveillance is not only a matter of what the government does, but that a private company that holds the data can give it to the government," she says. Consequently, the EU is concerned with anyone that collects and tracks data, while in the U.S. the larger concern is government surveillance rather than corporate surveillance, "though I think that's changing."

The EU's principles cover the entire Union, but it is up to individual countries to carry them out in practice. "Implementation and enforcement varies from country to country," explains Rodriguez. "In Spain, Google is suffering a lot, but it's not happening so much in Ireland. It's not uniform."

In December 2013, the Spanish Agency for Data Protection fined Google more than $1 million for mismanaging user data. In May 2014, the European Court of Justice upheld a decision by the same agency that Google had to remove a link to obsolete but damaging information about a user from its results in response, Google set up a website to process requests for information removal, and by the end of that month claimed to have received thousands of requests.

Online Privacy in Japan

The legal framework currently governing data privacy in Japan is the 2003 Act Concerning Protection of Personal Information. The Act requires businesses handling personal information to specify the reason and purpose for which they are collecting it. It forbids businesses from changing the information past the point where it still has a substantial relationship to the stated use and prohibits the data collector from using personal information more than is necessary for achieving the stated use without the user's consent. The Act stipulates exceptions for public health reasons, among others.

Takashi Omamyuda, a staff writer for Japanese Information Technology (IT) publication Nikkei Computer, says the Japanese government was expected to revise the 2003 law this year, "due to the fact that new technologies have weakened its protections." Changes probably will be influenced by both the European Commission's Data Protection Directive and the U.S. Consumer Privacy Bill of Rights (as outlined in the Obama administration white paper), as well as by the Organization for Economic Co-operation and Development (OECD) 2013 privacy framework.

In preparation for such revisions, the Japanese government established a Personal Information Review Working Group. "Some Japanese privacy experts advocate that the U.S. Consumer Privacy Bill of Rights and FTC (Federal Trade Commission) staff reports can be applied in the revision," says Omamyuda, "but for now these attempts have failed." Meanwhile, Japanese Internet companies are arguing for voluntary regulation rather than legal restrictions, asserting such an approach is necessary for them to be able to utilize big data and other innovative technologies and to support international data transfer.

As one step in this process, the Japanese government announced a "policy outline" for the amendment of these laws in June 2014. "The main issue up for revision," says Omamyuda, "is permitting the transfer of de-identified data to third parties under the new 'third-party authority.'" The third-party authority would be an independent body charged with data protection. "No one is sure whether this amendment would fill the gap between current policy and the regulatory approaches to online privacy in the EU and U.S."

The Japanese government gathered public comments, including a supportive white paper from the American Chamber of Commerce in Japan which, unsurprisingly, urged that any reforms "take the least restrictive approach, respect due process, [and] limit compliance costs."

Conclusion

With the world's data borders becoming ever more permeable even as companies and governments collect more and more data, it is increasingly important that different regions are on the same page about these issues. With the U.S. trying to satisfy EU requirements for data protection, and proposed reforms in Japan using the EU's principles and the proposed U.S. CPBR as models, policies appear to be moving in that direction.

Further Reading

2014 Japanese Privacy Law Revision Public Comments, Keio University International Project for the Internet & Society http://bit.ly/1E8X3kR

Act Concerning Protection of Personal Information (Japan Law No. 57, 2003) http://bit.ly/1rIjZ3M

Charter of Fundamental Rights of the European Union http://bit.ly/1oGRu37

Directive 95/46/EC of the European Parliament and of the Council of 24 October 1995 on the protection of individuals with regard to the processing of personal data and on the free movement of such data http://bit.ly/1E8UxuT

Greenleaf, G.
Global Tables of Data Privacy Laws and Bills http://bit.ly/ZAygX7

Consumer Data Privacy in a Networked World: A Framework for Protecting Privacy and Promoting Innovation in the Global Digital Economy, Obama Administration White Paper, February 2012, http://1.usa.gov/1rRdMUw

The OECD Privacy Framework, Organization for Economic Co-operation and Development, http://bit.ly/1tnkiil

Author

Logan Kugler is a freelance technology writer based in Clearwater, FL. He has written for over 60 major publications.

Figures

Figure. Protesters marching in Washington, D.C., in 2013 in opposition to governmental surveillance of telephone conversations and online activity.

©2015 ACM 0001-0782/15/02

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IV and the Strange Band Debut “Son of Sin”

A famous family name in country music is both the greatest asset one can hold, and the most unbelievably burdensome yoke from the expectations it foists upon you. Just ask the father, and the grandfather of Coleman Williams, who is the great grandson of Hank Williams, who was the man who along with siring the most important singing family in the history of country music (with all due respect to The Carters), also put together arguably the greatest career in country music history, all while dying a year younger than when Coleman is just starting his own career.

At 30 years old, the son of Shelton Hank Williams III has decided to throw his hat in the ring, and enter the family business. When this was first revealed in early March by Saving Country Music, the situation went super viral with rabid curiosity about what Coleman, or simply “IV” had in store. Initially the plan was to release a single earlier in April, and then an EP today, 4/20. But after the incredible attention that flowed to this young man, word is new opportunities opened up, he was taken into the management stable of Patrick Files who also manages Coleman’s father, and the 4th-generation performer was heading back into the studio.

It wasn’t just due to a new generation of Hank entering the fray that created such ferocious interest in IV and The Strange Band, it was also due to the fact that his father has been so out-of-sight for going on eight years, leaving an appetite for the type of punk-inspired country Hank3 helped popularize like nobody else previously.

This is what IV and The Strange Band look to tap into with the first taste of music, “Son of Sin.” While it’s certain to be taken as a strange specimen of music by straight-laced traditional country listeners, or perhaps even more by the sedate Americana crowd, the point of this song is to delve into the seedy underbelly of the American South, where Gothic and Gospel/country and metal influences intertwine in the muck of muddy water, and create a musical amalgam meant for an audience of lost souls.

Help making this vision come to life is producer Jason Dietz, who is also known as the bass player for the band The Hardin Draw, and has worked with former Hank3 bass player Joe Buck in the past. Guitarist David Talley and fiddler Laura Beth Jewell also of The Hardin Draw are involved in bringing the project to life under what they call “The Strange Band.”

There is no doubt that the result is something a bit unusual to the modern ear. After all, underground country has always carried a niche appeal to begin with, while that scene has virtually dissolved over the last 5-7 years for a myriad of reasons, including the virtual disappearance of Hank3. But make no mistake, “Son of Sin” is underground country at its very kernel root incarnated into a debut single, and should be considered as such, as opposed to what someone might want to hear from the next generation of Hank.

Since this is truly the first, nascent taste of music from IV and the Strange Band—and word is they headed back in the studio and are still developing this project—I’m choosing to reserve any strong judgement until we hear what comes next. But with the name, and with the interest already flowing to this effort, IV and The Strange Band is most certainly something worth keeping an eye and ear on.