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Blockchain Offers Internet of Things Data Quality and Data Security

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Blockchain and IoT

The rapid advances of blockchain technology and the Internet of Things are changing how business on the internet gets done. Blockchain provides superior data security and Data Quality and, as a consequence, is changing the way people approach big data. This can be quite useful, as security remains a primary concern for Internet of Things (IoT) ecosystems. IoT systems expose a variety of devices and huge amounts of data to security breaches. Blockchain has great potential for blocking hackers and providing security in a number of fields, ranging from banking to healthcare to smart cities.

In Japan, nearly 50 banks have partnered with Ripple, which is an open source blockchain organization, valued as Terra’s third largest market capitalization. This alliance enables the use of blockchain technology to provide instant, risk-free transactions at minimal costs. Traditional financial transactions can be quite expensive due to the large number of hacking risks involved. Partnering with Ripple allowed the banks to decrease the processing time of a transaction significantly and reduced associated security costs to nearly zero.

In healthcare, the privacy, integrity, and control over a patient’s data is very important. As the number of various IoT devices monitoring a patient’s medical data increases, the number of entry points for hackers also increases. Unfortunately, using blockchain as a solution will be difficult for older, larger healthcare institutions. It will require rebuilding database infrastructures, training and hiring new staff, and persuading directors that blockchain is worth the financial costs. Newer health-based start-ups are much more likely to initiate the use of blockchain, while older organizations will hesitate to invest in the transition.

The primary advantage of blockchain, in terms of security, is its decentralization. No single individual or company has control of the data entry or its integrity. The accuracy of the blockchain is continuously verified by each computer in the network. With all points holding the same information, corrupted data at point A is blocked from becoming part of the chain because it doesn’t match with the data at points B and C.

Combining Blockchain with the IoT

The Internet of Things continues to expand. However, these devices are often lacking the security needed to keep the data safe. Critical infrastructures can be damaged by hackers as they “break in,” penetrating an organization’s computer network. The use of blockchain across all aspects of the IoT ensures authentication, standardization, and trust.

The combination of encryption and distributed storage means data can be trusted as accurate by all involved in the chain of communication. The private keys providing write-access to the blockchain system are held by machines. A human cannot rewrite the record to present inaccurate information. Instead, machines securely record details of the transactions.

Blockchain also has the potential for protecting highly personal data, such as in smart home devices that offer hackers access to the intimate details of a person’s life and daily routines. While this data must be shared with other devices and services to be useful, it also allows openings for hackers to break in. Allowing data coming from the IoT to be managed by blockchains would provide an additional tier of encrypted security.

Additionally, smart contracts can be created by organizations working with certain blockchain networks (Ethereum being one example). These contracts allow for agreements to be executed as conditions are met. This is extremely useful when authorizing a system to make a payment, after certain tasks have been accomplished.

Bottlenecked Data

Currently, IoT techs and sensor installers connect the IoT to a centralized processing system, which in turn promotes data bottlenecks. (At present, this happens only occasionally.) An in-house computer or a centralized cloud with storage and processing services may be the source of a data bottleneck. With more and more devices connected to a centralized system, and IoT data streams continuing to steadily grow, there is the strong potential for bottlenecking, and its symptoms (slowness, timeouts, and CPU spikes). Blockchain, as a decentralized system, avoids this problem.

A blockchain storage network duplicates data across several hundred (possibly thousands) of devices and computers. This provides significant redundancy and means the data will always be available when needed. It cuts down transfer time and eliminates the concern a single server will fail, making stored data unavailable.

Combining Blockchain and Big Data Analytics

The use of blockchain adds another layer to the process of big data analytics. This additional data layer resolves two issues for big data analytics:

  • Blockchain-generated big data is secure and it cannot be forged or altered.
  • Blockchain-based big data is easy to use and it is complete, structured, and abundant.

The data in the blockchain ledger can be used for the trading of energy and real estate and can be used in a variety of other industries. It can also be used for fraud prevention, as the technology permits financial institutions to check every transaction in real-time. Blockchain allows banks to identify fraudulent or risky transactions, preventing fraud before it happens.

Smart Grids and Blockchain

There are two basic concerns regarding the security of smart grids. The use of smart grids is based on “remote access” and “automation.” The potential for an individual or an enemy government to hack into smart grids is quite real. Hackers have already accessed traditional electrical grids. Blockchain can be used with smart grid electrical management systems, and two of its security measures used to thwart hackers:

  • Authentication: Verification someone accessing the grid has been identified.
  • Authorization: Verification the person with access has the authority to make changes.

According to Benjamin Gu, the chairman and founder of Daex (a cryptocurrency), Blockchain’s technology may provide improved security for data exchanges, interconnectivity, and permission control. Gu explained:

“The underlying technology has identification security through public-private encryption with key access. Those who attempt to ‘get in’ must verify their identity, and their authorization, to do so. If key access codes are kept safe and secure, Blockchain will do the rest.”

Blockchain can also empower cities to be innovative in using big data as they deal with local businesses. The combination promotes the efficient use of things such as transportation, energy, and even voting. Cities can benefit from the increased efficiency (and lower costs). Blockchain has the potential to change how we structure investments and start businesses.

Smart Cities and Blockchain

Blockchain provides the security needed to keep a smart city from being hacked. It promotes the secure and efficient use of a city’s infrastructure. The Internet of Things is like a city’s nervous system, with each device interconnected with the others, so they can communicate with each other and make decisions that manage the resources of a city’s population. The IoT provides a system of communicating devices, in turn supporting smart solutions for daily problems. All smart city solutions require the Internet of Things to make decisions.

The use of blockchain is a new concept to smart cities. Blockchain technology, when integrated into smart cities, can connect all of a city’s services, while simultaneously increasing security and transparency. It is anticipated blockchain will allow cities to use smart contracts, with the terms of the agreement written directly into lines of code.

Benjamin Gu has also suggested governments can use blockchain to develop smart cities as a way of dealing with increasing populations and dwindling resources. This includes handling waste management and traffic controls more efficiently. The technology is available, and many cities have begun experimenting, offering:

  • Smart buildings that can be programmed with heating, cooling, and lighting systems, based on real-time needs, as well as established patterns.
  • Street lights designed to be turned on as a person or vehicle approaches, and off as they pass and move away, per smart grids.
  • Water supplies controlled to respond to usage and breakdowns.
  • Waste that is tracked and recorded, allowing for more efficient recycling.
  • Traffic monitoring that informs commuters of the best routes in real-time.
  • Smart srids that warm sidewalks and roads when icy conditions take place.

Photo Credit: Shutterstock.com

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Source: https://www.dataversity.net/blockchain-offers-internet-things-data-quality-data-security/

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Colonial Pipeline hackers stole data on Thursday – Bloomberg News

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(Reuters) – The hackers who caused Colonial Pipeline to shut down on Friday began their cyberattack against the top U.S. fuel pipeline operator a day earlier and stole a large amount of data, Bloomberg News reported citing people familiar with the matter.

The attackers are part of a cybercrime group called DarkSide and took nearly 100 gigabytes of data out of Colonial’s network in just two hours on Thursday, Bloomberg reported https://www.bloomberg.com/news/articles/2021-05-09/colonial-hackers-stole-data-thursday-ahead-of-pipeline-shutdown?sref=SCAzRb9t late Saturday, citing two people involved in the company’s investigation.

Colonial did not immediately reply to an email from Reuters seeking comment outside usual U.S. business hours.

Colonial Pipeline shut its entire network, the source of nearly half of the U.S. East Coast’s fuel supply, after a cyber attack that involved ransomware.

(Reporting by Aakriti Bhalla in Bengaluru; Editing by Himani Sarkar)

Image Credit: Reuters

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Source: https://datafloq.com/read/colonial-pipeline-hackers-stole-data-thursday-bloomberg-news/14521

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Musk boosts his brand, and NBCUniversal’s, on ‘Saturday Night Live’

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(Corrects word in quote in paragraph six, and spelling of mother’s name in paragraph seven)

By Joseph White

DETROIT (Reuters) -Billionaire Elon Musk dropped a surprise early in his hotly anticipated turn as host of “Saturday Night Live,” saying in his monologue that he “is the first person with Asperger’s” to host the show, before clowning through skits for the first global livestream of the NBCUniversal comedy show.

Musk, one of the world’s richest individuals, opened his monologue by telling an audience in more than 100 countries he is “the first person with Asperger’s to host SNL. At least the first to admit it.” The billionaire made light of his tendency to speak in a monotone, adding “I’m pretty good at running human in emulation mode.”

Asperger syndrome is a condition on the autism spectrum that is associated with difficulty in social interaction, and sometimes is referred to as high functioning autism.

Comedian and Saturday Night Live alumni Dan Aykroyd has spoken in interviews about being diagnosed with a mild form of Asperger’s. He hosted Saturday Night Live in 2003.

Many had wondered how Musk would handle himself during a live comedy show. The answer was that he, and the show’s writers, sought to soften the rough edges of Musk’s public persona. Throughout the show, Musk gently poked fun at himself, including his penchant for provocative tweets and the time he smoked a joint on a podcast.

“To anyone I’ve offended I just want to say, I reinvented electric cars and I’m sending people to Mars on a rocket ship. Did you think I would be a chill, normal dude?”

Musk’s mother, Maye, joined him on stage and the two made a joke about Dogecoin, the cryptocurrency Musk has touted. Cryptocurrency jokes popped up throughout the show. In one sketch, Musk was cast as a bow-tie wearing cryptocurrency expert on the show’s Weekend Update segment. Dogecoin and other digital currencies had surged in price ahead of Musk’s SNL appearance.

Dogecoin fell during the show and was last down about 12% from late Friday at around $0.53. It hit a record high Thursday above $0.73.

Musk was most convincing playing a version of himself as head of SpaceX dealing with an emergency on a Martian colony. The crisis had a happy ending, until it didn’t.

In the end, Musk will keep his day jobs. Still, the “Saturday Night Live” appearance offered plenty of synergies with his real gigs as “technoking” and Chief Executive of Tesla Inc, head of rocket launch company SpaceX and even chief of the Boring Company, a tunnel construction venture.

Musk got days of attention across all forms of media ahead of the show, and shared the spotlight with a prototype of Tesla’s futuristic Cybertruck that Tesla brought to Manhattan on Friday. Video of the hulking, angular pickup prowling Manhattan streets blew up on social media.

During the show, a Tesla supercharger made an incongruous cameo appearance in a skit set in an old West saloon. Musk played a gunslinger who had developed an electric horse, and advocated tunneling through the earth to escape a shootout.

Musk often boasts that Tesla doesn’t spend billions on advertising the way established automakers do. He doesn’t have to so long as he has access to platforms like Twitter or “Saturday Night Live.”

Musk’s appearance also boosted NBCUniversal. The media company used Musk’s global celebrity – and the controversy surrounding his appearance on a stage normally reserved for film stars or professional comedians – to get attention for launching the “Saturday Night Live” franchise beyond the confines of broadcast television. The company said Saturday’s show was streamed live via Alphabet Inc’s YouTube to more than 100 countries.

(Reporting By Joe White; Editing by Simon Cameron-Moore)

Image Credit: Reuters

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Source: https://datafloq.com/read/musk-boosts-brand-nbcuniversals-saturday-night-live/14520

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If you did not already know

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Metalog Distribution google


In economics, business, engineering, science and other fields, continuous uncertainties frequently arise that are not easily- or well-characterized by previously-named continuous probability distributions. Frequently, there is data available from measurements, assessments, derivations, simulations or other sources that characterize the range of an uncertainty. But the underlying process that generated this data is either unknown or fails to lend itself to convenient derivation of equations that appropriately characterize the probability density (PDF), cumulative (CDF) or quantile distribution functions. The metalog distributions are a family of continuous univariate probability distributions that directly address this need. They can be used in most any situation in which CDF data is known and a flexible, simple, and easy-to-use continuous probability distribution is needed to represent that data. Consider their uses and benefits. Also consider their applications over a wide range of fields and data sources. …

Long Short-Term Attention (LSTA) google


Egocentric activity recognition is one of the most challenging tasks in video analysis. It requires a fine-grained discrimination of small objects and their manipulation. While some methods base on strong supervision and attention mechanisms, they are either annotation consuming or do not take spatio-temporal patterns into account. In this paper we propose LSTA as a mechanism to focus on features from spatial relevant parts while attention is being tracked smoothly across the video sequence. We demonstrate the effectiveness of LSTA on egocentric activity recognition with an end-to-end trainable two-stream architecture, achieving state of the art performance on four standard benchmarks. …

Gaussian Process Posterior Sampling Reinforcement Learning (GPPSTD) google


Efficient Reinforcement Learning usually takes advantage of demonstration or good exploration strategy. By applying posterior sampling in model-free RL under the hypothesis of GP, we propose Gaussian Process Posterior Sampling Reinforcement Learning(GPPSTD) algorithm in continuous state space, giving theoretical justifications and empirical results. We also provide theoretical and empirical results that various demonstration could lower expected uncertainty and benefit posterior sampling exploration. In this way, we combined the demonstration and exploration process together to achieve a more efficient reinforcement learning. …

SkinnerDB google


SkinnerDB is designed from the ground up for reliable join ordering. It maintains no data statistics and uses no cost or cardinality models. Instead, it uses reinforcement learning to learn optimal join orders on the fly, during the execution of the current query. To that purpose, we divide the execution of a query into many small time slices. Different join orders are tried in different time slices. We merge result tuples generated according to different join orders until a complete result is obtained. By measuring execution progress per time slice, we identify promising join orders as execution proceeds. Along with SkinnerDB, we introduce a new quality criterion for query execution strategies. We compare expected execution cost against execution cost for an optimal join order. SkinnerDB features multiple execution strategies that are optimized for that criterion. Some of them can be executed on top of existing database systems. For maximal performance, we introduce a customized execution engine, facilitating fast join order switching via specialized multi-way join algorithms and tuple representations. We experimentally compare SkinnerDB’s performance against various baselines, including MonetDB, Postgres, and adaptive processing methods. We consider various benchmarks, including the join order benchmark and TPC-H variants with user-defined functions. Overall, the overheads of reliable join ordering are negligible compared to the performance impact of the occasional, catastrophic join order choice. …

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Source: https://analytixon.com/2021/05/08/if-you-did-not-already-know-1391/

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Exclusive: U.S. Justice Department probing Kabbage, fintechs over PPP loan calculations – sources

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By Koh Gui Qing and Pete Schroeder

WASHINGTON (Reuters) – The U.S. Department of Justice is investigating whether financial technology companies including Atlanta-based Kabbage Inc may have erred while distributing billions of dollars in pandemic aid to struggling small businesses, three people with knowledge of the matter told Reuters.

The investigation, led by the Justice Department’s civil division, is examining whether Kabbage and other fintech companies miscalculated how much aid borrowers were entitled to from the Paycheck Protection Program (PPP) due to confusion over how to account for payroll taxes, the three people said.

A fourth person with direct knowledge of the matter said a number of fintechs were being probed over the PPP tax issues but declined to provide names.

A probe does not necessarily indicate wrongdoing and it was unclear if the investigation will result in penalties against the companies, two of the three sources said.

A spokesperson for the Justice Department declined to comment.

Kabbage Inc., which manages Kabbage’s PPP loans and also goes by the brand K Servicing, did not respond to multiple calls and emails seeking comment. Spokespeople for American Express Co., which in August bought most of Kabbage’s assets other than its loan portfolio, declined to comment.

The previously unreported probe underscores how the unprecedented $780 billion program, which was launched by the Small Business Administration (SBA) on April 3, 2020, to mitigate the fallout from COVID-19 shutdowns, has created legal and reputational risks for some lenders.

Reuters reported https://www.reuters.com/article/us-health-coronavirus-usa-ppp-exclusive/exclusive-u-s-watchdogs-train-sights-on-lender-misconduct-in-pandemic-aid-program-idUSKBN2841MT in November that a handful of federal agencies were scrutinizing lenders over a range of PPP issues, including failing to properly vet borrowers’ payroll expense calculations and potentially discriminatory lending policies.

Under the PPP, big banks, community lenders and fintechs have dispensed millions of government-backed loans to small businesses hurt by the pandemic lockdowns. If borrowers spend the money on payroll and other business expenses, the government repays the lender on behalf of the borrower.

While the program has been hailed as a lifeline for small businesses, its launch was rushed and many of its rules were initially unclear. One challenge lenders faced in April 2020 was how to account for federal, state and local taxes when calculating a company’s overall payroll costs, which determined their maximum allowable loan.

Some lenders over-accounted for taxes, potentially inflating loans, while others under-accounted for taxes, potentially denying borrowers aid they were entitled to, the two sources said.

Fintechs have attracted government scrutiny because they processed loans at high speed using software that in some cases had glitches, causing errors in applications, one of the sources said. Other industry sources also said that fintechs’ use of automated lending platforms with few manual checks caused errors to be replicated across thousands of loans.

SBA data showed fintechs have issued around $26.5 billion worth of PPP loans. Kabbage made nearly 300,000 PPP loans worth $7 billion between April 3, 2020, and Aug. 8, 2020, according to its website.

Lenders have said they were under enormous pressure to lend vast sums of money to millions of businesses quickly, while having to keep up with ever-changing PPP rules.

A spokeswoman for the SBA declined to comment.

(Additional reporting by Chris Prentice; Editing by Michelle Price and Sonya Hepinstall)

Image Credit: Reuters

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Source: https://datafloq.com/read/exclusive-us-justice-department-probing-kabbage-fintechs-ppp-loan-calculations-sources/14511

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