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DOD seeks classification “Clippy” to help classify data, control access
Enlarge (credit: Sean Gallagher)
Figuring out what information should be classified and controlling access to it has been an eternal headache for defense and national security organizations—a headache that got a lot of attention during the investigation into former Secretary of State Hillary Clinton’s use of personal emails. Even people with a more clear understanding of sensitive data classification may have difficulty determining when information needs to be marked and restricted in circulation. So the Department of Defense is looking for some help from machine-learning systems.
The DOD has issued a request for information (RFI) from industry in a quest for technology that will prevent the mislabeling and accidental (or deliberate) access and sharing of sensitive documents and data. In an announcement posted in May by the Defense Information Systems Agency (DISA), the Pentagon stated that the DOD CIO’s office—part of the Office of the Secretary of Defense—is “investigating the use of commercial solutions for labeling and controlling access to sensitive information.”
Defense IT officials are seeking software that “must be able to make real-time decisions about the classification level of the information and an individual’s ability to access, change, delete, receive, or forward the information based on the credentials of the sending and/or receiving individual, facility, and system.”
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Apple Removes iPhone USB Access Feature, Blocking Out Hackers, Law Enforcement
The move escalates tensions between the phone giant and federal law enforcement when it comes to mobile security.
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Thousands Of Academics Pledge To Boycott Springer’s New Machine Learning Title In Support Of Long-Established Open Access Journal
Among Techdirt’s many stories chronicling the (slow) rise of open access publishing, a number have been about dramatic action taken by researchers to protest against traditional publishers and their exploitative business model. For example, in 2012, a boycott of the leading publisher Elsevier was organized to protest against its high journal prices and its support for the now long-forgotten Research Works Act. In 2015, the editors and editorial board of the Elsevier title Lingua resigned in order to start up their own open access journal. Now we have another boycott, this time as a reaction against the launch of the for-profit Nature Machine Intelligence, from the German publishing giant Springer. Thousands of academics in the field have added their names to a statement about the new title expressing their concerns:
the following list of researchers hereby state that they will not submit to, review, or edit for this new journal.
We see no role for closed access or author-fee publication in the future of machine learning research and believe the adoption of this new journal as an outlet of record for the machine learning community would be a retrograde step. In contrast, we would welcome new zero-cost open access journals and conferences in artificial intelligence and machine learning.
The contact person for the statement is Thomas G. Dietterich, Distinguished Professor (Emeritus) and Director of Intelligent Systems at Oregon State University. He has a long history of supporting open access. In 2001, he was one of 40 signatories to another statement. It announced their resignation from the editorial board of the Machine Learning Journal (MLJ), which was not open access, and their support for the Journal of Machine Learning Research (JMLR), launched in 2000, which was open access. As they wrote:
our resignation from the editorial board of MLJ reflects our belief that journals should principally serve the needs of the intellectual community, in particular by providing the immediate and universal access to journal articles that modern technology supports, and doing so at a cost that excludes no one. We are excited about JMLR, which provides this access and does so unconditionally. We feel that JMLR provides an ideal vehicle to support the near-term and long-term evolution of the field of machine learning and to serve as the flagship journal for the field.
That confidence seems to have been justified. JMLR is now up to its 18th volume, and is flourishing. It is “zero cost” open access — it makes no charge either to read or to be published if a paper is accepted by the editors. The last thing this minimalist operation needs is a rival title from a well-funded publisher able to pour money into its new launch in order to attract authors and take over the market. Hence the current boycott of Nature Machine Intelligence, and the call for “new zero-cost open access journals and conferences in artificial intelligence and machine learning” instead.
As to why Springer decided to announce a competitor to a well-established, and well-respected journal, an article in The Next Web points out that the German publishing company is about to offer shares worth up to €1.6 billion (around $ 1.95 billion) in its imminent IPO. A new journal covering the super-hot areas of AI, machine learning and robotics is just the sort of thing to help give the share price a boost. And when there’s serious money to be made, who cares about the collateral damage to a much-loved open access title running on a shoestring?
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