Content for Deep Learning: The Lionbridge View

March 17, 2020

Here is a handy resource. Lionbridge AI shares “The Best 25 Datasets for Natural Language Processing.” The list is designed as a starting point for those just delving into NLP. Writer Meiryum Ali begins:

“Natural language processing is a massive field of research. With so many areas to explore, it can sometimes be difficult to know where to begin – let alone start searching for data. With this in mind, we’ve combed the web to create the ultimate collection of free online datasets for NLP. Although it’s impossible to cover every field of interest, we’ve done our best to compile datasets for a broad range of NLP research areas, from sentiment analysis to audio and voice recognition projects. Use it as a starting point for your experiments, or check out our specialized collections of datasets if you already have a project in mind.”

The suggestions are divided by purpose. For use in sentiment analysis, Ali notes one needs to train machine learning models on large, specialized datasets like the Multidomain Sentiment Analysis Dataset or the Stanford Sentiment Treebank. Some text datasets she suggests for natural language processing tasks like voice recognition or chatbots include 20 Newsgroups, the Reuters News Dataset, and Princeton University’s WordNet. Audio speech datasets that made the list include the audiobooks of LibriSpeech, the Spoken Wikipedia Corpora, and the Free Spoken Digit Dataset. The collection concludes with some more general-purpose datasets, like Amazon Reviews, the Blogger Corpus, the Gutenberg eBooks List, and a set of questions and answers from Jeopardy. See the write-up for more on each of these entries as well as the rest of Ali’s suggestions in each category.

This being a post from Lionbridge, an AI training data firm, it naturally concludes with an invitation to contact them when ready to move beyond these pre-made datasets to one customized for you. Based in Waltham, Massachusetts, the company was founded in 1996 and acquired by H.I.G. Capital in 2017.

Cynthia Murrell, March 17, 2020

LiveRamp: Data Aggregation Under the Marketing Umbrella

March 10, 2020

Editor’s Note: We posted a short item about Venntel. This sparked some email and phone calls from journalists wanting to know more about data aggregation. There are a number of large data aggregation companies. Many of these work with diverse partners. If the data aggregation companies do not sell directly to the US government, some of the partners of these firms might. One of the larger data aggregation companies positions itself as a specialist, a niche player. We have pulled some information from our files to illustrate what data aggregation, cross correlation, and identify resolution contributes to advertisers, political candidates, and other entities.


LiveRamp is Acxiom, and it occupies a leadership position in resolving identity across data sets.  The system can be used by a company to generate revenue from its information. The company says:

We’re innovators, engineers, marketers, and data ethics experts on a mission to make data safe and easy to use.

LiveRamp also makes it easy to a company to obtain certain types of data and services which can be made more accurate via LiveRamp methods. The information is first, second, and third party data. First means the company captures the data directly. Second means the data come from a partner. Third means that, like distant cousins, there’s mostly a tenuous relationship among the source of the data, the creator of the data, the collector of the data, and the intermediary who provides the data to LiveRamp. There’s a 2016 how to at this link.


According to a former LiveRamp employee:

LiveRamp doesn’t actually provide intelligence on the data, it just moves the data around effectively, quickly, seamlessly, and accurately.

The basic mechanism was explained in “The Hidden Value of of Acxiom’s LiveRamp”:

An alternative approach is to designate a single company to be the hub of all ID syncs. The hub can collect IDs from each participating ad tech partner and then form mutual ID syncs as needed. Think of this as a match maker who knows the full universe of eligible singles and can then introduce couples. LiveRamp has established itself as this match maker…

This is ID syncing; that is, figuring out who is who or what is what via anonymized or incomplete data sets.

There’s nothing unusual in what LiveRamp does. Oracle and other firms perform onboarding Why? Data are hot mess. Hot means that government agencies, companies, digital currency providers, and non governmental organizations will license access to these data. The mess means that information is messy, incomplete, and inaccurate. Cross correlation can address some, but not all, of these characteristics.

The Business: License Access to Data

Think of LiveRamp as an old-school mailing list company. There’s a difference. LiveRamp drinks protein shakes, follows a keto diet, and makes full use of digital technology.

According the the company:

We have a unique philosophy and approach to onboarding [that’s the LiveRamp lingo for importing data]. It’s not just about bringing offline data online. It’s about bringing siloed first-, second-, and third-party data together in a privacy-conscious manner and then resolving it to a single persistent identifier called an IdentityLink.

DarkCyber is no expert in the business processes of LiveRamp. We can express some of these ideas in our own words.

Onboarding means importing. In order to import data, LiveRamp, a Fiverr worker, or smart software has to convert the source data to a format LiveRamp can import. There are other steps to make sure the data is consistent, fields exist, and are what the bringer of the data says they are; for example, the number of records matches what the data provider asserts.

Siloed data are data kept apart from other data. The reason for creating separate, often locked down sets of data separate from other data is for secrecy, licensing compliance, or business policies; for example, a pharma outfit developing a Covid 19 treatment does not want those data floating around anywhere except in a very narrow slice of the research facility. Once siloed data appear anywhere, DarkCyber becomes quite curious about the who, what, when, where, why, and the all important how. How answers the question, “How did the data escape the silo?”

Privacy conscious is a phrase that seems a bit like Facebook lingo. No comment or further explanation is needed from DarkCyber’s point of view.

IdentityLink is essentially an accession number to a profile. Law enforcement gives prisoners numbers and gathers data in a profile. LiveRamp does it for the entities its cross correlative methods facilitate. Once an individual profile exists, other numerical procedures can be applied to assign “values” or “classifications” to the entities; for example, sports fans or maybe millennial big spender. One may be able to “resolve identity” if a customer does not know “who” an entity is.


Cookie data are available. These are useful for a range of specialized functions; for example, trying to determine where an individual has “gone” on the Internet and related operations.

In a nutshell, this is the business of LiveRamp.

Open Source Contributions

LiveRamp has more than three dozen repositories in GitHub. Examples include:

  • Cascading_ext which allows LiveRamp customers to build, debug, and run simple data workflows.
  • HyperMinHash-java. Cross correlation by any other name still generates useful outputs.
  • Munkres. Optimization made semi-easy.

The LiveRamp CEO is Scott Howe, who used to work at Microsoft. LiveRamp purchased Data Plus Math, a firm specializing in analyzing targeted ads on traditional and streaming TV. Data Plus Math co-founders, CEO John Hoctor and Chief Technology Officer Matthew Emans, allegedly have work experience with Mr. Howe and Microsoft’s advertising unit.

Interesting Customers
  • Advertising agencies
  • Political campaigns
  • Ad inventory brokers.

Stephen E Arnold, March 10, 2020

Enterprise Document Management: A Remarkable Point of View

March 3, 2020

DarkCyber spotted “What Is an Enterprise Document Management (EDM) System? How to Implement Full Document Control.” The write up is lengthy, running about 4,000 words. There are pictures like this one:


ECM is enterprise content management and in the middle is Enterprise Document Management which is abbreviated DMS, not EDM.

The idea is that documents have to be managed, and DarkCyber assumes that most organizations do not manage their content — regardless of its format — particularly well until the company is involved in a legal matter. Then document management becomes the responsibility of the lawyers.

In order to do any type of document or content management, employees have to follow the rules. The rules are the underlying foundation of the article. A company manufacturing interior panels for an automaker will have to have a product management system, an system to deal with drawings (paper and digital), supplier data, and other bits and pieces to make sure the “door cards” are produced.

The problem is that guidelines often do not translate into consistent employee behavior. One big reason is that the guidelines don’t fit into the work flows and the incentive schemes do not reward the time and effort required to make sure the information ends up in the “system.” Many professionals write something, text it, and move on. Enterprise systems typically do not track fine grained information very well.

Like enterprise search, the “document management” folks try to make workers who may be concerned about becoming redundant, a sick child, an angry boss, or any other perturbation in the consultant’s checklist ignore many information rules.

There is an association focused on records management. There are companies concerned with content management. There are vendors who focus on images, videos, audio, and tweets.

The myth that an EDM, ECM, or enterprise search system can create an affordable, non invasive, legally compliant, and effective way to deal with the digital fruit cake in organizations is worth lots of money.

The problem is that these systems, methods, guidelines, data lakes, federation technologies, smart software, etc. etc. don’t work.

The article does a good job of explaining what a consultant recommends. The information it presents provides fodder for the marketing animals who are going to help sell systems, training, and consulting.

The reality is that humans generate information and use a range of systems to produce content. Tweets about a missed shipment from a person mobile phone may be prohibited. Yeah, explain that to the person who got the order in the door and kept the commitment to the customer.

There are conferences, blogs, consulting firms, reports, and BrightPlanet videos about managing information.

The write up states:

There is no use documenting and managing poor workflows, processes, and documentation. To survive in business, you have to adapt, change and improve. That means continuously evaluating your business operations to identify shortfalls, areas for improvements, and strengths for continuous investment. Regular internal audits of your management systems will enable you to evaluate the effectiveness of your Enterprise Document Management solution.

Right. When these silver bullet, pie-in-the-sky solutions cost more than budgeted, employees quit using them, and triage costs threaten the survival of the company — call in the consultants.

Today’s systems do not work with the people actually doing information creation. As a result, most fail to deliver. Sound familiar? It should. You, gentle reader, will never follow the information rules unless you are specifically paid to follow them or given an ultimatum like “do this or get fired.”

Tweet that and let me know if you managed that information.

Stephen E Arnold, March 3, 2020

After Decades of Marketing Chaff, Data Silos Thrive

March 2, 2020

Here’s another round of data silo baloney—“Top 4 Ways to Eliminate Data Fragmentation Within Your Organization” from IT Brief. Surveys have found that many businesses are not making the most of all that data they’ve been collecting, and it has become common to blame data silos. It is true that some organizations could store and access their data more efficiently. There’s just one problem, and it is one we have mentioned before—there are some very good reasons to keep some data fragmented. Silos exist because of things like government requirements, legal processes, sensitive medical data, experts protecting their turf, and basic common sense.

The article asserts:

“Many organizations are finding it difficult to extract meaningful value from their data due to one endemic problem: mass data fragmentation. With mass data fragmentation, data volumes continue to rise exponentially, but companies struggle to manage that data because it’s scattered across locations and infrastructure silos, both in on-premises data centers and in the cloud. Organizations often don’t know what data exists, where it is and whether it’s being stored securely and in compliance with regulations.”

Of course, entities must ensure data is stored securely and that they comply with regulations. Also, the write-up’s advice to keep redundancies to a minimum and to understand how one’s data is being stored and accessed in the cloud are good ones. However, the exhortation to eliminate silos entirely is off the mark; trying to do so can be a fruitless exercise in expense and frustration.


  1. A person wants to hoard his or her information
  2. Rules or regulations prevent sharing to those “not in the fox hole”
  3. Lawyers and HR professionals don’t want legal documents available and “people” managers definitely do not want employee health and salary data flying around like particles motivated by Brownian motion.

Net net: Reality has silos. Accept it. Omit the marketing silliness.

Stephen E Arnold, March 2, 2020


Graph QL: The Future Five Years Later

February 28, 2020

Graph QL is “is a query language for APIs and a runtime for fulfilling those queries with your existing data.” The technology allegedly was a result of Facebook’s technical wizardry in 2012. The digital information weapon vendor released Graph QL to open source in 2015. You can get insights, links, and techno babble on the Graph QL Foundation Web site.

DarkCyber noted that Hasura snagged about $10 million to make Graph QL easier to use. The story appeared in TechCrunch on February 26, 2020. Is Hasura a frillback pigeon?








Or is the company one of those lovable creatures found in Washington Square Park in the spring?







As it turns out, Graph QL is becoming a mini boomlet in the database universe. There are the companies supporting the Graph QL Facebook innovation; for example:







Plus others like IBM and the PR world’s fave Twitter.

However, there are other companies in the “graph” business; for example:

Also, another dozen or so innovators.







Altexsoft asserts that GraphQL is that the technology is good for complex systems. Other upsides include:

  • Retrieves data with a single call
  • Delivers just what’s needed
  • Permits validation and type checks
  • Auto generates API documentation
  • Supports rapid application prototyping (the move fast and break things approach perhaps?)

There are some downsides; for example:

  • Complexity
  • Performance
  • The ever helpful a HTTP status code of 200 (helpful indeed)
  • Complexity (Oh, sorry, I mentioned that).

Now back to the TechCrunch story about Hasura. The reason the company was funded may relate to the firm’s unique selling proposition: Our approach makes GraphQL easy.

Will easy sell? Worth watching in order to determine what breed of pigeon is flying through disparate sets of big data.

Stephen E Arnold, February 28, 2020

NoSQL DBMS: A Surprising Inclusion

February 12, 2020

Top Databases Used in Machine Learning Project” is a listicle. The information in the write up is similar to the lists of “best” products whipped up by Silicon Valley type publications, mid tier consulting firms (a shade off the blue chip outfits like McKinsey, Booz, and BCG), and 20 somethings fresh from university.

The interesting inclusion in the list of DBMS is?

If you said, Elasticsearch you would be correct. Elasticsearch is an open source play doing business as Elastic. The open source version is at its core a search and retrieval system. (Does this mean the index is the data and the database?)

DarkCyber is not going to get into a discussion of whether an enterprise search system can be a database management system. Both sides in the battle are less interested in resolving the fuzzy language than making sales.

Maybe Elasticsearch is just doing what other enterprise search systems have done since the 1980s? Vendors describe search and retrieval as the solution to the world’s data management Wu Flu.

Net net: Without boundaries, why make distinctions? Just close the deal. Distinctions are irrelevant for some business tasks.

Stephen E Arnold, February 12, 2020

Blockchain: Now What Is That Use Case?

February 7, 2020

The DarkCyber team invested some time in figuring out Amazon’s blockchain-related inventions. (A free executive summary is available at this link.) There were some interesting use cases explained in these public documents. But blockchain in Amazon is different in blockchain in the world of a specialist blockchain firm if the information in “Major Blockchain Developer ConsenSys Announces Job Losses” is accurate.

The write up states:

Major blockchain developer ConsenSys has laid off around 14% of its workforce, it said on Tuesday, a move that comes as companies around the world frantically search for applications for the much-hyped technology.

Blockchain in frantic search for applications? Yikes.

The issues blockchain faces range from “good enough”, better known alternatives to scaling.

The write up explains:

Companies from banks and oil traders to retailers and tech vendors, drawn to its promise of making cumbersome processes more efficient and secure, have invested billions as they look to find uses for the technology. Many have turned to blockchain development startups in the process for technical expertise. Yet there have so far there have been few major breakthroughs in the practical application of blockchain, despite the spate of tests and pilots.

Complexity, performance, cost, and security may be barriers. Just what catches Amazon’s attention?

Stephen E Arnold, February 7, 2020



Buzzword Alert: Programmable Networks

February 5, 2020

DarkCyber noted “University Researchers Succeed in Boosting Computer Speeds by 2.5 Times.” The headline suggests zippy computers. Well, sort of. One bottleneck is accessing data written to a storage device. The innovation or insight, if it is economically and technically implementable, trims data access bottlenecks. DarkCyber noted:

Current data storage systems use only one storage server to process information, making them slow to retrieve information to display for the user. A backup server only becomes active if the main storage server fails. The new approach, called FLAIR, optimizes data storage systems by using all the servers within a given network. Therefore, when a user makes a data request, if the main server is full, another server automatically activates to fill it, the scientists state.

The approach exploits programmable networks. A network of servers is like a microprocessor. The shift is to meta-think about these components. Therefore, create a wrap up layer like the one described in the write up.

Popping up a level sometimes make sense. Marketing a meta-play may be even more beneficial.

Stephen E Arnold, February 3, 2020

A Solution to the Blockchain Trilemma?

February 3, 2020

Struggling to deliver a blockchain application which is decentralized, secure, and scalable? A solution may have been developed. Navigate to “Ex-Microsoft Researcher Says He’s Solved the Blockchain Scalability Problem.” Despite the hype about blockchain, there’s a problem mixed with the promise:

…It says that it’s easy to have a blockchain with two of three key attributes: decentralization, security, and scalability. What’s difficult is getting all three; so far, cranking up the volume has always meant sacrificing on another.

The alleged solution comes from Asensys, led by former Microsoft lead researcher JiaPing Wang. The alleged solution is avoiding “going off-chain or sharding transactions.” The idea is to eliminate duplicative processes:

instead spreading the workload across the entire network by creating multiple “zones” within it that work independently and asynchronously.

For now, this is a work in progress. And those marketing assurances about decentralization, security, and scalability? Yeah, right.

Stephen E Arnold, February 3, 2020

Amazon Blockchain: How Secure?

January 27, 2020

This write up does not address Amazon’s blockchain innovations. We have a summary of our Amazon blockchain technology which points out specific systems and methods, the online bookstore has “invented” to make blockchain more secure. (Keep in mind, Amazon is the inventor of S3 buckets, which in some circumstances, are somewhat leaky.) You can get a copy of the free DarkCyber Amazon Blockchain report using the information at the end of this blog post.

The article “Trust No One. Not Even a Blockchain” suggests that one of the most hyped data management technologies may have a weakness. Technology experts are not fond of weaknesses. Technology is a solution, and solutions must not have fatal flaws like mere humans working at a giant company or in the semi isolation of a coffee shop.

The write up points out:

Similarly, just because a person claims to have uploaded all of her photographs to a blockchain—like Mila’s mother in Parker’s story—does not mean there are no other pictures from her life. Omitted data, bad data, too much data: These dynamics rob a blockchain of the claim of being a source of truth. Garbage in, garbage out. This concept in computer science means that an input consisting of flawed data will generate a flawed output. So it is with blockchain technology. We can record false claims on a blockchain. We can omit data. Suddenly, that source of truth does not appear so honest.

The essay concludes with this observation:

Distortion of reality is a growing threat. Deepfakes, synthetic videos that replace an image of one person with that of another, may soon become indistinguishable from authentic videos. Today, deepfakes may largely be used in the making of memes, face-swapping celebrities, but their proliferation will undoubtedly have major implications on everything from political campaigns to policies around pornography. What makes the threat of deepfakes so profound is that they render a medium formerly viewed as reliable—namely video—undependable. We cannot trust the very thing that we are supposed to trust. This constitutes the most substantial danger to a society’s notion of reality. If we are supposed to trust whatever is on a blockchain, then we are in trouble indeed. After all, the blockchain is only as good as the data we put on it.

Amazon’s blockchain inventions address the “control” of the information placed in the blockchain. That may give Amazon an advantage in the policeware market.

If you want a copy of the DarkCyber executive summary for our 54 page report about Amazon’s blockchain and some of the implications of these inventions, send an email to darkcyber333 at yandex dot com. No charge for the summary. The full report, however, is not free.

Stephen E Arnold, January 27, 2020

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