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250-722 exam Dumps Source : Implementation of DP Solutions for Windows using NBU 5.0

Test Code : 250-722

Test Name : Implementation of DP Solutions for Windows using NBU 5.0

Vendor Name : Symantec

: 114 Real Questions

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In their exploration of algorithms, we’ve applied many options to provide effects. Some ideas have used iOS-certain patterns while others had been extra generalized. even though it hasn’t been explicitly outlined, a few of their solutions have used a selected programming style called dynamic programming. while easy in idea, its application can every so often be nuanced. When utilized accurately, dynamic programming can have a powerful effect on the way you to write down code. during this essay, we’ll introduce the theory and implementation of dynamic programming.

retailer For Laterin case you’ve purchased something through Amazon.com, you’ll be conventional with the web site time period — “store For Later”. as the phrase implies, shoppers are offered the option to add items to their cart or store them to a “desire checklist” for later viewing. When writing algorithms, they commonly face a similar alternative of completing movements (performing computations) as information is being interpreted or storing the results for later use. Examples consist of retrieving JSON data from a RESTful provider or using the Core records Framework:

In iOS, design patterns can assist us time and coordinate how records is processed. particular concepts include multi-threaded operations (e.g. Grand valuable Dispatch), Notifications and Delegation. Dynamic programming (DP) on the other hand, isn’t necessarily a single coding approach, however somewhat how to suppose about moves (e.g. subproblems) that ensue as a function operates. The ensuing DP solution might range depending on the difficulty. In its easiest kind, dynamic programming relies on statistics storage and reuse to enhance algorithm effectivity. The procedure of statistics reuse is also referred to as memoization and can take many types. As we’ll see, this fashion of programming gives a lot of merits.

Fibonacci Revisitedin the essay on Recursion, they in comparison building the traditional sequence of Array values the usage of each iterative and recursive techniques. As mentioned, these algorithms were designed to provide an Array sequence, now not to calculate a particular influence. Taking this into consideration, they are able to create a new edition of Fibonacci to come a single Int cost:

func fibRecursive(n: Int) -> Int if n == 0 return 0if n <= 2 return 1

return fibRecursive(n: n-1) + fibRecursive(n: n-2)

at first look, it looks this seemingly small feature would even be efficient. however, upon further analysis, they see numerous recursive calls have to be made for it to calculate any influence. As proven under, on account that fibRecursive can't shop in the past calculated values, its recursive calls boost exponentially:

Fibonacci MemoizedLet’s try a unique technique. Designed as a nested Swift characteristic, fibMemoized captures the Array return price from its fibSequence sub-function to calculate a closing cost:

extension Int//memoized versionmutating func fibMemoized() -> Int

//builds array sequencefunc fibSequence(_ sequence: Array<Int> = [0, 1]) -> Array<Int>

var remaining = Array<Int>()

//mutated copyvar output = sequence

let i: Int = output.count number

//set base condition - linear time O(n)if i == self return output

let consequences: Int = output[i - 1] + output[i - 2]output.append(results)

//set iterationfinal = fibSequence(output)return ultimate

//calculate ultimate product - constant time O(1)let consequences = fibSequence()let reply: Int = outcomes[results.endIndex - 1] + results[results.endIndex - 2]return reply

even though fibSquence comprises a recursive sequence, its base case is determined by the variety of requested Array positions (n). In performance terms, they say fibSequence runs in linear time or O(n). This efficiency development is completed via memoizing the Array sequence crucial to calculate the remaining product. in consequence, each and every sequence permutation is computed as soon as. The improvement of this technique is considered when comparing the two algorithms, proven below:

Shortest PathsCode memoization can also increase a program’s efficiency to the element of creating seemingly problematic or practically unsolvable questions answerable. An illustration of this may also be viewed with Dijkstra’s Algorithm and Shortest Paths. To assessment, they created a unique information constitution named path with the goal of storing specific traversal metadata:

//the direction classification continues objects that comprise the "frontier" type routevar complete: Intvar vacation spot: Vertexvar previous: course?

//object initializationinit()destination = Vertex()total = 0What makes route effective is its skill to store statistics on nodes prior to now visited. akin to their revised Fibonacci algorithm, course retailers the cumulative edge weights all traversed vertices (total) in addition to a complete history of each and every visited Vertex. Used easily, this enables the programmer to reply questions such because the complexity of navigating to a particular vacation spot Vertex, if the traversal became indeed a success (in discovering the destination), as well because the listing of nodes visited all through. depending on the Graph measurement and complexity, no longer having this tips attainable might suggest having the algorithm take so long to (re)compute records that it becomes too slow to be useful, or now not being in a position to remedy essential questions because of inadequate facts.

The story of Symantec’s contemporary transformation starts with a strategic aspiration: to position the company as an immense disruptor in its chosen sector of cybersecurity. Over the span of five years, the business went through two principal divestitures, promoting Veritas to a personal equity group in 2016, and web site protection to DigiCert in 2017. The company additionally made two big acquisitions, Blue Coat in 2016 and LifeLock in 2017, adopted by three smaller ones. Symantec then initiated two intensive rounds of restructuring that protected cutting back head count, which laid the groundwork for a subsequent wave of growth.

In doing all this, the company reoriented its aim. It went from promoting commercial enterprise software to providing the area’s leading cybersecurity platforms for each buyers and global enterprises, and shifted business fashions from product orientation to subscription-based mostly. The enterprise also went through a deeply felt cultural alternate, together with a new emphasis on diversity at the appropriate administration degree. lastly, Symantec changed CEOs twice, discovering solid floor with Blue Coat alumnus Greg Clark at the helm.

Symantec chief tips officer Sheila Jordan performed — and continues to play — a pivotal function overseeing the redecorate and consolidation of the business’s technological infrastructure. Jordan joined Symantec in 2014, quickly after the initiative began. Jordan up to now served as a senior vice president at Cisco systems and Disney, not simply in IT but also in finance, supporting earnings and advertising and marketing. Her technical expertise and enthusiastic, rely-of-truth approach to simplifying the enterprise’s digital expertise became a model for Symantec’s customers. She’s additionally considered one of Silicon Valley’s most famous feminine executives, in part because of her ability to identify traits in the trade, and dwell ahead of the curve.

approach+company caught up with Jordan in her Mountain View, Calif., office to focus on Symantec’s transformation and the adjustments taking area in Silicon Valley these days.

S+B: How would you describe your role in Symantec’s transformation?JORDAN: i used to be hired back in 2014 to bring tips know-how returned in-apartment from a old outsourced model and to construct a global-class IT organization. I knew that would be a major problem, and i thought it might be lots of fun. at that time, I had no concept that the entire company changed into about to change.

Then came the Veritas separation. A divestiture of this kind is vastly extra complicated than an acquisition. They had been fitting two separate companies with their own independent ecosystems of techniques and systems: WANs, LANs, sites, records centers and labs, business aid planning (ERP) programs, applications, laptops, and cell gadgets. every thing had to be break up apart, together with all the statistics. They decided to do it in a finished manner, to take this probability to start cleaning up procedures and simplifying everything.

In 2016, with the Blue Coat acquisition, they made the same option. They could have just jammed the two businesses together, with many legacy and redundant functions running at the same time as. as an alternative, they chose to simplify.

We strategically changed every element of the enterprise and took the chance to suppose tactically and long-time period. This resulted in a few accomplishments, together with consolidating into one client relationship administration equipment. we're presently one release faraway from consolidating varied ERPs into one ERP; reworking their business processes and getting rid of product SKUs; and streamlining their distribution channels and techniques to make doing company with Symantec more straightforward.

S+B: What had to happen internal the enterprise to make the transformation be successful?JORDAN: people discuss “digital transformations” as in the event that they have been all about know-how. within the grand scheme of things, the technology is the effortless part. more importantly, you need to focal point on enhancing your consumers’ experience in purchasing your products and services. for instance, on the grounds that your customers use mobile instruments, your application interfaces have to be as cellular-oriented as your shoppers are. At Symantec, they concentrated on 4 factors: velocity, alignment, strategic selections, and conversation.

S+B: Let’s take these in order. What does specializing in velocity and alignment suggest in observe?JORDAN: We’ve develop into a whole lot sooner and extra aligned at Symantec. via this alignment, they were capable of boost a plan that built-in six businesses and divested two. Between April 2017 and November 2018, we went through eight foremost software releases, which included colossal adjustments to ERP, CRM, and foundational facts and reporting techniques, with minimal business disruption. here is unprecedented in the ERP world. every important liberate covered a standard of 24 different functions, from advertising to engineering to finance. This equates to just about a free up every different month. They used an agile method, with building, integration, and consumer acceptance all occurring concurrently. as a result of the company and IT alignment, the pleasant of each and every release turned into high-quality.

We used identical construct their world subscription platform, which is the platform used to promote their cloud SaaS [software-as-a-service] items. To boost pace and simplicity, they modeled their user interface on the Amazon event: in precisely a number of clicks your order is achieved.

consumers and companions need a seamless event. They don’t want to see interior organizational handoffs. assisting the company articulate the client event has [given us] a compelling technique to think horizontally, and from a consumer lens, versus a purposeful view.

We brought IT, the enterprise instruments, and the different features in sync. as an example, once they realized they couldn’t get everything executed in the April ERP device unlock, they determined to push some points to may. This intended the organizations would ought to soak up a guide system for a short length of time. They agreed to this in enhance because all of us shared their expectations up front and they spent a good period of time on communique.

i'm additionally super proud of the style their engineering, advertising, and IT groups work collectively. For the world subscription platform, engineering owns the ordinary cloud, where the safety SaaS items get provisioned. advertising and marketing owns the navigation, user experience, and content on the site for their direct small- and medium-company valued clientele. IT owns the ordering and cash programs — and naturally, connects the whole platform collectively. but all of us personal and are liable for the entire adventure.

In that context, i really like that IT individuals are naturally systemic manner thinkers; they see horizontally. They know the way customers journey the enterprise. they will add value in broader businesses by means of declaring duplications, gaps, and dependencies.

massive decisions and conversation

S+B: You said a further aspect was “strategic choices.” What does that suggest?JORDAN: i used to be regarding the style they equipped the design and implementation of the transformation exercise. There are two councils. every other Friday, a application council that oversees the details of the exchange technique meets for 2 hours to go through enterprise and IT considerations. Then, major strategic choices are mentioned as soon as a month with the aid of a extra senior neighborhood: the software board. This board comprises the CEO and business unit frequent managers. all over these sessions, they have modified their pricing constitution for small organizations, rethought their channel approach, and simplified their product offerings.

S+B: How do you're making the councils work?JORDAN: They set expectations during the means they work. Flawless execution isn't non-compulsory for us. It’s obligatory. We’re all in this together, and we’re all responsible.

for instance, they learned to have fun what they name “reds.” These are the issues that americans can’t remedy on their own and must deliver up on the software councils. during the past, individuals weren’t relaxed asserting, “I’m purple this week.” They didn’t want their colleagues to understand. They were not leveraging the vigor of the room and their colleagues.

We created an ambiance where it feels safe to walk into a council meeting and say, “I’m red.” It just potential that you are off track and may need the room’s assist, even if it’s a trade-off with a colleague or every now and then the assist of the true management group, to get lower back heading in the right direction. setting that tone relieves force and stress, raises accountability, hurries up course corrections, and sets expectations along the manner.

this is where mutual have confidence and recognize amongst teams are vital. i will be able to say at a program meeting, “i will’t get that finished for this free up; it’s not possible.” Or, “My team says they checked out it 12 alternative ways, and it received’t work this time.” but then i will be able to present to position it within the subsequent unencumber and ask them, “What are the implications for you as a result of this resolution?” and be aware of they’ll answer candidly. That form of alternate-off and negotiation is staggering. I’ve labored on transformation and integration for 2 years, and i actually don’t consider there’s been one dramatic second. There were many suit debates, however it hasn’t develop into negative, with pointing fingers or a blame game. This tradition has allowed us to be so a hit. they are the use of their essential elements and mental power to solve real clients concerns and true enterprise complications.

S+B: What about the fourth factor you mentioned: conversation?JORDAN: i can’t articulate ample how critical frequent and critical communication is. change inevitably results in fear and uncertainty. The employees want many snippets of conversation, notwithstanding the leaders don’t have all the answers. They should reassure people, “it’s good enough” or “we’ll get this.” After joining Symantec and building an international-category IT group with tons of of contributors everywhere, I begun publishing an inner weekly weblog — simply a few paragraphs of important hobbies or initiatives, attention, calls to action, and news. I think I’ve neglected 4 weeks in 4 years. whenever I ignored it, I automatically heard from my personnel, “the place is the weblog?” americans need to hear what’s occurring. In an oblique way i am creating a community within the IT corporation. Infrastructure wants to understand what's going on within the application space and vice versa. employees like to comprehend their job is critical, and it’s up to leaders to clarify how it all suits together.

Bringing Cultures collectively

S+B: What changed into the cultural change at Symantec like out of your perspective?JORDAN: It [has been] large. four years ago, I likely would have pointed out, “subculture is vital. but it’s now not essential.” today, I think work on way of life is indispensable. Their company mission is to protect the area. anybody within the company can lean in towards that statement; it’s empowering, however you also have to set goals and be clear on the way you are going to meet that mission.

S+B: What different cultural concerns did you have got?JORDAN: With acquisitions, you have got distinct cultures to combine, like having a blended family. Symantec, Norton, Blue Coat, and LifeLock all had diverse cultures. It’s essential to take some time to establish the “new lifestyle” that takes the best of the highest quality from each acquisition. This takes time, so it’s vital to focus on the work that can be performed automatically. if you’re geared up as it should be, and you create a mix of the employees from different corporations, you grow to be with a diverse team and a tradition with distinctive perspectives and experiences. possibly it’s the nature of IT, with a significant demand and extent of work, or how the groups had been organized, but in their case, almost in a single day, it grew to be beside the point where a person got here from.

What mattered become that they showed up as a cohesive IT organization, fixing Symantec’s complex complications and dealing towards enhancing effectivity for their shoppers, companions, and personnel. The work will kind the subculture, mainly if you all believe like you’re in the equal boat, and it will force the stage of admire, have confidence, and credibility bigger.

S+B: if you needed to recommend a corporation going through identical changes, is there anything else noteworthy you’d inform them in regards to the transformation method that you simply haven’t outlined?JORDAN: have a good time successes, often. They managed the restructuring and acquisition whereas concurrently working the business, with quarter-ends, financial closes, and the entire general calls for on IT to run and operate a multibillion-greenback enterprise. The adventure is long, and individuals feel they’ll have fun when it’s carried out. however you’re by no means in fact accomplished. It’s going to be continual trade invariably. Take time along a way to make americans believe diagnosed and valued. offer reasonable celebrations or organize a community experience. in order to supply them the inducement and proposal to continue.

female leadership in technology

S+B: You’re probably the most well known girls in Silicon Valley, at a time when many tech businesses are attempting to raise their percentage of feminine executives. How does this concern come up at Symantec?JORDAN: Their range initiative is a large priority for their CEO, Greg Clark. It’s crucial to see male CEOs take this critically. They’re those who need to lead once they start to exchange deeply held considering and biases. [Clark is active in CEO Action for Diversity and Inclusion, a coalition of business leaders launched in 2017 to address these issues more effectively. Tim Ryan, PwC’s U.S. chair, is the chair of the group’s steering committee.] Their CHRO, Amy Cappellanti-Wolf, is also tremendous-smitten by this.

S+B: what's using this alternate? Why now?JORDAN: One factor, of course, is the broadening awareness of range considerations in the tech business. The more youthful technology is also forcing alternate. Millennials consider about variety in another way; they grew up going to faculty with people from diverse geographies and ethnicities, in addition to shifting gender norms and expectations. As they carry that mind-set into their corporations, it leads to a reverse mentoring for the rest of us. The millennials are educating us what it appears like to no longer have ingrained biases in opposition t other companies, and i love that.

at this time, girls make up 26 p.c of the world body of workers at Symantec, which is above ordinary for the industry. in accordance with Steve Morgan of Cybersecurity Ventures, ladies represent 20 p.c of the world cybersecurity body of workers and [that proportion] continues to develop.

additional, their most recent set of summer season interns were 60 percent women. That’s tons more suitable, however nonetheless not respectable ample.

S+B: Is the gender problem distinct in know-how than in other industries?JORDAN: it is, simply since the percentage of guys is so much larger. once I discuss with their banking or retail shoppers, as an example, there are greater ladies in every single place, at all stages of the organization. Of course, in practically any business, the greater you go within the hierarchy, the decrease the percent of ladies tends to be.

S+B: Do you consider that’s changing now?JORDAN: I’ve studied this for years. youngsters some ladies stop advancing in their careers when they hit very own existence pursuits, even if it’s having children or caring for ageing fogeys, many continue to face challenges around lack of mentorship, constrained access to opportunity, or emotions of isolation. They need to create techniques for americans to work that match lifestyles’s challenges and concurrently open up a chance. once more, millennials set an illustration. they're growing to be up in a global the place every little thing is a service. they could get whatever they desire: software, food, gas, a trip, clothing. They click and it’s there. fast-forward 10 years, and we’ll maybe have an open industry of labor assignments primarily based only on advantage. I’ll tackle a assignment that appears interesting for one enterprise; after which do one other challenge for a distinct business. In that context, probably ladies may have more advantageous access to probability, will experience less bias, and won’t choose out on the expense they're these days.

S+B: Does having a better feminine presence make a change within the method an organization handles a transformation?JORDAN: You frequently read that it does, since it is asserted that ladies are customarily more empathetic than men. however that can be a stereotype. To drive transformation, you need diverse pondering, inspite of age, gender, ethnic historical past, sexual orientation, or every other certain traits.

What matters most is the job they need to do. i am the CIO for Symantec. I don’t hide my identification as a woman; I wear dresses, jewelry, and makeup, and i actually can in my opinion relate to the constant challenges of a working mom with younger children. despite the fact, the proven fact that I’m a female doesn’t motivate any of my choices as CIO.

The way forward for Cybersecurity

S+B: How would you describe the result of Symantec’s transformation?JORDAN: Symantec now has two strategic company gadgets. Their enterprise company method is in line with Symantec’s integrated cyber-defense platform. On the customer side, with the acquisition of LifeLock, we’ve centered ways for people to independently give protection to their identities and their privateness. We simply introduced Norton privateness supervisor, an app that helps patrons bear in mind and take manage of their privateness and protect themselves online. We live in a new digital world where people are continually sharing their personal tips, and that counsel might be mined for earnings. via this app, they offer their purchasers the way to give protection to their information and their privateness, for themselves and their families.

The exciting a part of their method is that it addresses the historical fragmentation of the protection trade. Many CSOs have mentioned that they’re loaded up on protection tools of their ambiance. actually, their recent cyber web threat safety record (ITSR) indicated that on standard — in a large commercial enterprise enterprise — there are between sixty five and 85 protection tools. Eighty-five tools! Now that’s quite fragmented. I accept as true with Symantec is perfectly positioned to get rid of that complexity and enhance effectivity by using providing their integrated cyber-protection platform. finances-clever, this carrier customarily lowers fees for their consumers — it’s less difficult technically and it saves them funds.

We also be aware of that buyer and commercial enterprise security are interrelated. If individual personnel turn into more aware about security considerations and walk in the door more secured, with much less chance of compromise, that makes the job of any CIO more convenient.

S+B: How do you music the external traits when it comes to threats?JORDAN: Symantec operates the realm’s largest civilian possibility intelligence community, and probably the most finished collections of cybersecurity probability options. They even have hundreds of engineers within the company, together with those working directly on the items, who are engaged in danger intelligence. Symantec is liable for seeing and detecting issues earlier than any person else does, and we’re the usage of that intelligence to warn others.

Cybercriminals have become smarter. If normal cybersecurity is like locking the entrance door of your home, they’re discovering the right way to are available the side door, a window, or a crack within the molding. and they’re frequently lingering undetected and placing out, just gazing. You don’t even recognize they’re inner unless they act.

Cyber products now should have a big volume of artificial intelligence and computing device gaining knowledge of inbuilt. They have to go to new lengths to give protection to probably the most sensitive facts of an enterprise, akin to fee card trade [PCI] statistics, bank card suggestions, and now — with GDPR [General Data Protection Regulation] in effect — privacy facts. In prior instances, a protection operations center analyst used to analyze the information logs after a breach, searching for clues. today, they need to get at that needle in the haystack a great deal extra quickly.

S+B: How should still right administration be thinking about these considerations?JORDAN: safety represents a huge possibility to any enterprise. We’ve seen all too many instances the place, if it’s now not managed neatly, it could actually have harmful implications. “Are they secure?” is a simple question. The answer is extraordinarily advanced. as an instance, how do you make certain each employee is protection mindful? What are you doing to keep away from somebody from by chance leaving a computer within the incorrect region?

“should you fix your cybersecurity, you’re well-nigh cleaning apartment; you now know your infrastructure, purposes, and information tons improved.”

In normal, boards should still spend greater time speaking about safety. In many ways, it is as vital as the financials of a company. The protection posture should still now not be delegated to a subcommittee. each member of the board definitely should remember the protection posture of their business. on the C-suite degree, cybersecurity is regularly assigned to the CIO or chief safety officer, however the accountability of safety has to be broader. security is a company method. simply as with different enterprise concepts, you should believe govt alignment, technique, coverage, conversation, and, of direction, technology.

It’s no longer well-nigh insurance policy. there's a cost and effectivity play worried. Your legacy servers and systems may additionally get used best as soon as a quarter, however they take a seat there each day without a monitoring, providing an extra manner for dangerous guys to enter. when you fix your cybersecurity, you’re virtually cleaning residence; you now recognize your infrastructure, functions, and information a whole lot more desirable. you could design your programs from the floor up to be extra security conscious, resilient, and less difficult to use.

It’s complicated to accept as true with, however the most widespread password in 2018 became – get able for it – “123456,” the winner and still champ six years running. based on information superhighway researchers, that primary numerical string accounted for roughly 4 percent of the online passwords in use throughout 2018.

in reality, greater than 10 percent of individuals use probably the most 25 most standard passwords on this Wikipedia web page – so hackers following that as a ebook have an improved than one in ten opportunity of gaining access to a victim’s account (absolutely that doesn’t encompass TNW readers; they’re too smart to make use of these easy to wager passwords).

At this element, it’s difficult to imagine that there are net users who are not privy to the risks of widespread passwords – so if greater than 10 percent are using the same regular passwords, it’s clear they will’t rely on individuals to give protection to themselves.

That’s one explanation for the rise in multi-factor authentication (MFA) – where a website requires, besides a password, the insertion of a code despatched by way of textual content message, the submission of a one-time password, using a physical token (like a dongle), or authentication by the use of biometrics (face scan, thumbprint, etc.).

With hacking and statistics breaches on the upward thrust, it’s no shock that the marketplace for MFA is envisioned to develop fourfold by using 2025. The demand for MFA is growing on both sides – each with the aid of carrier providers and buyers – all of whom are tired of the in no way-ending hack assaults we’re subjected to.

The query, then, is which MFA is highest quality, and for which aim? With the MFA panorama being so distinctive, which method will grab the lion’s share of the market?

while as mentioned there are a couple of to make a choice from, as an authentication skilled, I consider that the one with a purpose to capture the imaginations of each patrons and businesses is push authentication.

Push is a expertise that verifies the id of users with the aid of sending a push notification to a cell device associated with their account during the login technique – meaning that there is nothing to be aware; all that must happen is for the device to be within the arms of the grownup who owns the account they are attempting to entry. In essence, it turns the cellular device into an authentication token.

What’s improved about push?

Let’s take a look at these in my opinion:

essentially the most secure sort of MFA, specialists agree, is a physical token – whatever thing you've got that verifies who you are. in lots of groups, for instance, access is granted to a constructing or branch the usage of a dongle it's omitted a reader. Such dongles are also used for authentication on cozy web sites, with the individual in the hunt for to join a dongle to their cell’s vigour connection. That’s a comfortable equipment, however a dear one.

With push, the gadget itself becomes the “dongle.” The incontrovertible fact that the person has it — assuming it turned into no longer stolen or misplaced, in which case the thief do not need the password that connections will nonetheless require — is enough to establish that they are who they declare to be.

SMS is not a preferred components for authentication, according to no much less an authority than NIST, the countrywide Institute of specifications and expertise. NIST retracted its aid for SMS-based mostly MFA, recommending that “implementers of recent programs should still cautiously agree with option authenticators” and explicitly states that “OOB (out of band authentication) the use of SMS is deprecated, and might no longer be allowed in future releases of this tips.”

NIST isn’t such a huge fan of biometrics, either. whereas there is room for use of biometrics, the agency mentioned in its latest authentication instructions that to ensure that the device to be constructive, it “shall be used with one other authentication factor.” Biometric traits, stated NIST, “do not constitute secrets and techniques. They can be obtained on-line or with the aid of taking a picture of someone with a camera cellphone (e.g. facial images) with or with out their talents, lifted from through objects a person touches (e.g. latent fingerprints), or captured with excessive resolution photos (e.g. iris patterns).”

As outlined, push is a no-brainer – actually. there is nothing to bear in mind, no action to be taken. as a result of the equipment’s ease of use and superior safety, most carriers of authentication technologies in recent years have stronger their options to guide push authentication. And push authentication is based on industry specifications such as PCI DSS, and is compliant with regulatory necessities equivalent to HIPAA and GDPR.

organizations that provide push authentication as an option encompass RSA security, SecureAuth, Microsoft service provider, CA applied sciences, Symantec enterprise, Vasco information safety foreign Inc., Okta Inc., Ping id, Gemalto, Entrust Datacard enterprise, and HID international enterprise.

credit score: StatistaAccording to Statista, smartphone adoption costs will proceed to rise in the next few years, which means authenticator apps and speak to-as-a-token MFA will likely continue to enhance in recognition. in the meantime, they are expecting to see SMS and utility OTPs slowly develop into obsolete as a result of they’re much less comfortable and environment them up and using them is extra advanced for users.

other factors to accept as true with when considering a way to use push are how it will integrate into the corporation; how clients will respond to it; no matter if the solution is bendy sufficient to adapt to your firm’s network and server requirements; should push authentication be cloud-primarily based, on-premises, or hybrid; no matter if that you would be able to (or should) drop passwords altogether and use password-less push authentication, at the side of yet another authentication solution corresponding to biometrics; and, of path, the charge.

Of route, like with another important circulation, analysis is needed, and groups will must decide how, and even if, push can assist them be more secure. however given the historical past of records breaches – and regardless of the mountains of cash thrown at the difficulty – safety is getting worse, no longer more advantageous. agencies that need to give protection to themselves really need to believe out of the authentication box – and push could be simply the thing they want.

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The question of natural measures of complexity for objects other than strings and sequences, in particular suited for 2-dimensional objects, is an open important problem in complexity science and with potential applications to molecule folding, cell distribution, artificial life and robotics. Here they provide a measure based upon the fundamental theoretical concept that provides a natural approach to the problem of evaluating n-dimensional algorithmic complexity by using an n-dimensional deterministic Turing machine, popularized under the term of Turmites for n = 2, from which the so-called Langton’s ant is an example of a Turing universal Turmite. A series of experiments to validate estimations of Kolmogorov complexity based on these concepts is presented, showing that the measure is stable in the face of some changes in computational formalism and that results are in agreement with the results obtained using lossless compression algorithms when both methods overlap in their range of applicability. They also present a divide and conquer algorithm that they call Block Decomposition Method (BDM) application to classification of images and space–time evolutions of discrete systems, providing evidence of the soundness of the method as a complementary alternative to compression algorithms for the evaluation of algorithmic complexity. They provide exact numerical approximations of Kolmogorov complexity of square image patches of size 3 and more, with the BDM allowing scalability to larger 2-dimensional arrays and even greater dimensions.

The challenge of finding and defining 2-dimensional complexity measures has been identified as an open problem of foundational character in complexity science (Feldman & Crutchfield, 2003; Shalizi, Shalizi & Haslinger, 2004). Indeed, for example, humans understand 2-dimensional patterns in a way that seems fundamentally different than 1-dimensional (Feldman, 2008). These measures are important because current 1-dimensional measures may not be suitable to 2-dimensional patterns for tasks such as quantitatively measuring the spatial structure of self-organizing systems. On the one hand, the application of Shannon’s Entropy and Kolmogorov complexity has traditionally been designed for strings and sequences. However, n-dimensional objects may have structure only distinguishable in their natural dimension and not in lower dimensions. This is indeed a question related to the loss of information in dimension reductionality (Zenil, Kiani & Tegnér, in press). A few measures of 2-dimensional complexity have been proposed before building upon Shannon’s entropy and block entropy (Feldman & Crutchfield, 2003; Andrienko, Brilliantov & Kurths, 2000), mutual information and minimal sufficient statistics (Shalizi, Shalizi & Haslinger, 2004) and in the context of anatomical brain MRI analysis (Young et al., 2009; Young & Schuff, 2008). A more recent application, also in the medical context related to a measure of consciousness, was proposed using lossless compressibility for EGG brain image analysis was proposed in Casali et al. (2013).

On the other hand, for Kolmogorov complexity, the common approach to evaluating the algorithmic complexity of a string has been by using lossless compression algorithms because the length of lossless compression is an upper bound of Kolmogorov complexity. Short strings, however, are difficult to compress in practice, and the theory does not provide a satisfactory solution to the problem of the instability of the measure for short strings.

Here they use so-called Turmites (2-dimensional Turing machines) to estimate the Kolmogorov complexity of images, in particular space–time diagrams of cellular automata, using Levin’s Coding theorem from algorithmic probability theory. They study the problem of the rate of convergence by comparing approximations to a universal distribution using different (and larger) sets of small Turing machines and comparing the results to that of lossless compression algorithms carefully devising tests at the intersection of the application of compression and algorithmic probability. They found that strings which are more random according to algorithmic probability also turn out to be less compressible, while less random strings are clearly more compressible.

Compression algorithms have proven to be signally applicable in several domains (see e.g., Li & Vitányi, 2009), yielding surprising results as a method for approximating Kolmogorov complexity. Hence their success is in part a matter of their usefulness. Here they show that an alternative (and complementary) method yields compatible results with the results of lossless compression. For this they devised an artful technique by grouping strings that their method indicated had the same program-size complexity, in order to construct files of concatenated strings of the same complexity (while avoiding repetition, which could easily be exploited by compression). Then a lossless general compression algorithm was used to compress the files and ascertain whether the files that were more compressed were the ones created with highly complex strings according to their method. Similarly, files with low Kolmogorov complexity were tested to determine whether they were better compressed. This was indeed the case, and they report these results in ‘Validation of the Coding Theorem Method by Compressibility’. In ‘Comparison of Km and compression of cellular automata’ they also show that the Coding theorem method yields a very similar classification of the space–time diagrams of Elementary Cellular Automata, despite the disadvantage of having used a limited sample of a Universal Distribution. In all cases the statistical evidence is strong enough to suggest that the Coding theorem method is sound and capable of producing satisfactory results. The Coding theorem method also represents the only currently available method for dealing with very short strings and in a sense is an expensive but powerful “microscope” for capturing the information content of very small objects.

Kolmogorov–Chaitin complexityCentral to algorithmic information theory (AIT) is the definition of algorithmic (Kolmogorov–Chaitin or program-size) complexity (Kolmogorov, 1965; Chaitin, 1969): (1)KTs=min|p|,Tp=s.

That is, the length of the shortest program p that outputs the string s running on a universal Turing machine T. A classic example is a string composed of an alternation of bits, such as (01)n, which can be described as “n repetitions of 01”. This repetitive string can grow fast while its description will only grow by about log2(n). On the other hand, a random-looking string such as 011001011010110101 may not have a much shorter description than itself.

Uncomputability and instability of KA technical inconvenience of K as a function taking s to the length of the shortest program that produces s is its uncomputability (Chaitin, 1969). In other words, there is no program which takes a string s as input and produces the integer K(s) as output. This is usually considered a major problem, but one ought to expect a universal measure of complexity to have such a property. On the other hand, K is more precisely upper semi-computable, meaning that one can find upper bounds, as they will do by applying a technique based on another semi-computable measure to be presented in the ‘Solomonoff–Levin Algorithmic Probability’.

The invariance theorem guarantees that complexity values will only diverge by a constant c (e.g., the length of a compiler, a translation program between U1 and U2) and that they will converge at the limit.

Invariance Theorem (Calude, 2002; Li & Vitányi, 2009): If U1 and U2 are two universal Turing machines and KU1(s) and KU2(s) the algorithmic complexity of s for U1 and U2, there exists a constant c such that for all s: (2)|KU1s−KU2s|<c.

Hence the longer the string, the less important c is (i.e., the choice of programming language or universal Turing machine). However, in practice c can be arbitrarily large because the invariance theorem tells nothing about the rate of convergence between KU1 and KU2 for a string s of increasing length, thus having an important impact on short strings.

Solomonoff–Levin Algorithmic ProbabilityThe algorithmic probability (also known as Levin’s semi-measure) of a string s is a measure that describes the expected probability of a random program p running on a universal (prefix-free1) Turing machine T producing s upon halting. Formally (Solomonoff, 1964; Levin, 1974; Chaitin, 1969), (3)ms=∑p:Tp=s1/2|p|.

Levin’s semi-measure2 m(s) defines a distribution known as the Universal Distribution (a beautiful introduction is given in Kircher, Li & Vitanyi (1997)). It is important to notice that the value of m(s) is dominated by the length of the smallest program p (when the denominator is larger). However, the length of the smallest p that produces the string s is K(s). The semi-measure m(s) is therefore also uncomputable, because for every s, m(s) requires the calculation of 2−K(s), involving K, which is itself uncomputable. An alternative to the traditional use of compression algorithms is the use of the concept of algorithmic probability to calculate K(s) by means of the following theorem.

Coding Theorem (Levin, 1974): (4)|−log2ms−Ks|<c.

This means that if a string has many descriptions it also has a short one. It beautifully connects frequency to complexity, more specifically the frequency of occurrence of a string with its algorithmic (Kolmogorov) complexity. The Coding theorem implies that (Cover & Thomas, 2006; Calude, 2002) one can calculate the Kolmogorov complexity of a string from its frequency (Delahaye & Zenil, 2007b; Delahaye & Zenil, 2007a; Zenil, 2011; Delahaye & Zenil, 2012), simply rewriting the formula as: (5)Kms=−log2ms+O1.

An important property of m as a semi-measure is that it dominates any other effective semi-measure μ, because there is a constant cμ such that for all s, m(s) ≥ cμμ(s). For this reason m(s) is often called a Universal Distribution (Kircher, Li & Vitanyi, 1997).

The Coding Theorem methodLet D(n, m) be a function (Delahaye & Zenil, 2012) defined as follows: (6)Dn,ms=|T∈n,m:T produces s||T∈n,m:T halts | where (n, m) denotes the set of Turing machines with n states and m symbols, running with empty input, and |A| is, in this case, the cardinality of the set A. In Zenil (2011) and Delahaye & Zenil (2012) they calculated the output distribution of Turing machines with 2-symbols and n = 1, …, 4 states for which the Busy Beaver (Radó, 1962) values are known, in order to determine the halting time, and in Soler-Toscano et al. (2014) results were improved in terms of number and Turing machine size (5 states) and in the way in which an alternative to the Busy Beaver information was proposed, hence no longer needing exact information of halting times in order to approximate an informative distribution.

Here they consider an experiment with 2-dimensional deterministic Turing machines (also called Turmites) in order to estimate the Kolmogorov complexity of 2-dimensional objects, such as images that can represent space–time diagrams of simple systems. A Turmite is a Turing machine which has an orientation and operates on a grid for “tape”. The machine can move in 4 directions rather than in the traditional left and right movements of a traditional Turing machine head. A reference to this kind of investigation and definition of 2D Turing machines can be found in Wolfram (2002), one popular and possibly one of the first examples of this variation of a Turing machine is Lagton’s ant (Langton, 1986) also proven to be capable of Turing-universal computation.

In ‘Comparison of Km and approaches based on compression’, they will use the so-called Turmites to provide evidence that Kolmogorov complexity evaluated through algorithmic probability is consistent with the other (and today only) method for approximating K, namely lossless compression algorithms. They will do this in an artful way, given that compression algorithms are unable to compress strings that are too short, which are the strings covered by their method. This will involve concatenating strings for which their method establishes a Kolmogorov complexity, which then are given to a lossless compression algorithm in order to determine whether it provides consistent estimations, that is, to determine whether strings are less compressible where their method says that they have greater Kolmogorov complexity and whether strings are more compressible where their method says they have lower Kolmogorov complexity. They provide evidence that this is actually the case.

In ‘Comparison of Km and compression of cellular automata’ they will apply the results from the Coding theorem method to approximate the Kolmogorov complexity of 2-dimensional evolutions of 1-dimensional, closest neighbor Cellular Automata as defined in Wolfram (2002), and by way of offering a contrast to the approximation provided by a general lossless compression algorithm (Deflate). As they will see, in all these experiments they provide evidence that the method is just as successful as compression algorithms, but unlike the latter, it can deal with short strings.

Deterministic 2-dimensional Turing machines (Turmites)Turmites or 2-dimensional (2D) Turing machines run not on a 1-dimensional tape but in a 2-dimensional unbounded grid or array. At each step they can move in four different directions (up, down, left, right) or stop. Transitions have the format {n1, m1} → {n2, m2, d}, meaning that when the machine is in state n1 and reads symbols m1, it writes m2, changes to state n2 and moves to a contiguous cell following direction d. If n2 is the halting state then d is stop. In other cases, d can be any of the other four directions.

Let (n, m)2D be the set of Turing machines with n states and m symbols. These machines have nm entries in the transition table, and for each entry {n1, m1} there are 4nm + m possible instructions, that is, m different halting instructions (writing one of the different symbols) and 4nm non-halting instructions (4 directions, n states and m different symbols). So the number of machines in (n, m)2D is (4nm + m)nm. It is possible to enumerate all these machines in the same way as 1D Turing machines (e.g., as has been done in Wolfram (2002) and Joosten (2012)). They can assign one number to each entry in the transition table. These numbers go from 0 to 4nm + m − 1 (given that there are 4nm + m different instructions). The numbers corresponding to all entries in the transition table (irrespective of the convention followed in sorting them) form a number with nm digits in base 4nm + m. Then, the translation of a transition table to a natural number and vice versa can be done through elementary arithmetical operations.

We take as output for a 2D Turing machine the minimal array that includes all cells visited by the machine. Note that this probably includes cells that have not been visited, but it is the more natural way of producing output with some regular format and at the same time reducing the set of different outputs.

Figure 1: Top: Example of a deterministic 2-dimensional Turing machine. Bottom: Accumulated runtime distribution for (4, 2)2D.Figure 1 shows an example of the transition table of a Turing machine in (3, 2)2D and its execution over a ‘0’-filled grid. They show the portion of the grid that is returned as the output array. Two of the six cells have not been visited by the machine.

An Approximation to the Universal DistributionWe have run all machines in (4, 2)2D just as they have done before for deterministic 1-dimensional Turing machines (Delahaye & Zenil, 2012; Soler-Toscano et al., 2014). That is, considering the output of all different machines starting both in a ‘0’-filled grid (all white) and in a ‘1’-filled (all black) grid. Symmetries are described and used in the same way than in Soler-Toscano et al. (2014) in order to avoid running a larger number of machines whose output can be predicted from other equivalent machines (by rotation, transposition, 1-complementation, reversion, etc.) that produce equivalent outputs with the same frequency.

We also used a reduced enumeration to avoid running certain trivial machines whose behavior can be predicted from the transition table, as well as filters to detect non-halting machines before exhausting the entire runtime. In the reduced enumeration they considered only machines with an initial transition moving to the right and changing to a different state than the initial and halting states. Machines moving to the initial state at the starting transition run forever, and machines moving to the halting state produce single-character output. So they reduce the number of initial transitions in (n, m)2D to m(n − 1) (the machine can write any of the m symbols and change to any state in {2, …, n}). The set of different machines is reduced accordingly to k(n − 1)(4nm + m)nm−1. To enumerate these machines they construct a mixed-radix number, given that the digit corresponding to the initial transition now goes from 0 to m(n − 1) − 1. To the output obtained when running this reduced enumeration they add the single-character arrays that correspond to machines moving to the initial state at the starting transition. These machines and their output can be easily quantified. Also, to take into account machines with the initial transition moving in a different direction than the right one, they consider the 90, 180 and 270 degree rotations of the strings produced, given that for any machine moving up (left/down) at the initial transition, there is another one moving right that produces the identical output but rotates −90 (−180/−270) degrees.

Setting the runtimeThe Busy Beaver runtime value for (4, 2) is 107 steps upon halting. But no equivalent Busy Beavers are known for 2-dimensional Turing machines (although variations of Turmite’s Busy Beaver functions have been proposed (Pegg, 2013)). So to set the runtime in their experiment they generated a sample of 334 × 108 random machines in the reduced enumeration. They used a runtime of 2,000 steps for the runtime sample, this is 10.6% of the machines in the reduced enumeration for (4, 2)2D, but 1,500 steps for running all (4, 2)2D. These machines were generated instruction by instruction. As they have explained above, it is possible to assign a natural number to every instruction. So to generate a random machine in the reduced enumeration for (n, m)2D they produce a random number from 0 to m(n − 1) − 1 for the initial transition and from 0 to 4nm + m − 1 for the other nm − 1 transitions. They used the implementation of the Mersenne Twister in the Boost C++ library. The output of this sample was the distribution of the runtime of the halting machines.

Figure 1 shows the probability that a random halting machine will halt in at most the number of steps indicated on the horizontal axis. For 100 steps this probability is 0.9999995273. Note that the machines in the sample are in the reduced enumeration, a large number of very trivial machines halting in just one step having been removed. So in the complete enumeration the probability of halting in at most 100 steps is even greater.

But they found some high runtime values—precisely 23 machines required more than 1,000 steps. The highest value was a machine progressing through 1,483 steps upon halting. So they have enough evidence to believe that by setting the runtime at 2,000 steps they have obtained almost all (if not all) output arrays. They ran all 6 × 347 Turing machines in the reduced enumeration for (4, 2)2D. Then they applied the completions explained before.

Output analysisThe final output represents the result of 2(4nm + m)2 executions (all machines in (4, 2)2D starting with both blank symbols ‘0’ and ‘1’). They found 3,079,179,980,224 non-halting machines and 492,407,829,568 halting machines. A number of 1,068,618 different binary arrays were produced after 12 days of calculation with a supercomputer of medium size (a 25×86-64 CPUs running at 2,128 MHz each with 4 GB of memory each, located at the Centro Informático Científico de Andalucía (CICA), Spain.

Let D(4, 2)2D be the set constructed by dividing the occurrences of each different array by the number of halting machines as a natural extension of Eq. (6) for 2-dimensional Turing machines. Then, for every string s, (7)Km,2Ds=−log2D4,2s using the Coding theorem (Eq. (3)). Figure 2 shows the top 36 objects in D(4, 2)2D, that is the objects with lowest Kolmogorov complexity values.

Figure 2: The top 36 objects in D(4, 2)2D preceded by their Km,2D values, sorted by higher to lower frequency and therefore from smaller to larger Kolmogorov complexity after application of the Coding theorem. Only non-symmetrical cases are displayed. The grid is only for illustration purposes. Evaluating 2-dimensional Kolmogorov complexityD(4, 2)2D denotes the frequency distribution (a calculated Universal Distribution) from the output of deterministic 2-dimensional Turing machines, with associated complexity measure Km,2D. D(4, 2)2D distributes 1,068,618 arrays into 1,272 different complexity values, with a minimum complexity value of 2.22882 bits (an explanation of non-integer program-size complexity is given in Soler-Toscano et al. (2014) and Soler-Toscano et al. (2013)), a maximum value of 36.2561 bits and a mean of 35.1201. Considering the number of possible square binary arrays given by the formula 2d×d (without considering any symmetries), D(4, 2)2D can be said to produce all square binary arrays of length up to 3 × 3, that is ∑d=132d×d=530 square arrays, and 60,016 of the 2(4×4) = 65,536 square arrays with side of length (or dimension) d = 4. It only produces 84,104 of the 33,554,432 possible square binary arrays of length d = 5 and only 11,328 of the possible 68,719,476,736 of dimension d = 6. The largest square array produced in D(4, 2)2D is of side length d = 13 (Left of Fig. 3) out of a possible 748 × 1048; it has a Km,2D value equal to 34.2561.

Figure 3: Top: Frequency of appearance of symmetric “checkerboard” patterns sorted from more to less frequent according to D(4, 2)2D (displayed only non-symmetrical cases under rotation and complementation). The checkerboard of size 4 × 4 doesn’t occur. However, all 3 × 3 as seen in Fig. 6, including the “checkerboard” pattern of size 3 × 3 do occur. Bottom: Symmetry breaking from a fully deterministic set of symmetric computational rules. Bottom Left: With a value of Km,2D = 6.7 this is the simplest 4 × 4 square array after the preceding all-blank 4 × 4 array (with Km,2D = 6.4) and before the 4 × 4 square array with a black cell in one of the array corners (with complexity Km,2D = 6.9). Bottom Right: The only and most complex square array (with 15 other symmetrical cases) in D(4, 2)2D with Km,2D = 34.2561. Another way to see this array is as one among those of length 13 with low complexity given that it occurred once in the sampled distribution in the classification unlike all other square arrays of the same size that are missing in D(4, 2)2D.What one would expect from a distribution where simple patterns are more frequent (and therefore have lower Kolmogorov complexity after application of the Coding theorem) would be to see patterns of the “checkerboard” type with high frequency and low random complexity (K), and this is exactly what they found (see Fig. 3), while random looking patterns were found at the bottom among the least frequent ones (Fig. 4).

Figure 4: Symmetry breaking from fully deterministic symmetric computational rules. Bottom 16 objects in the classification with lowest frequency, or being most random according to D(4, 2)2D. It is interesting to note the strong similarities given that similar-looking cases are not always exact symmetries. The arrays are preceded by the number of occurrences of production from all the (4, 2)2D Turing machines.We have coined the informal notion of a “climber” as an object in the frequency classification (from greatest to lowest frequency) that appears better classified among objects of smaller size rather than with the arrays of their size, this is in order to highlight possible candidates for low complexity, hence illustrating how the process make low complexity patterns to emerge. For example, “checkerboard” patterns (see Fig. 3) seem to be natural “climbers” because they come significantly early (more frequent) in the classification than most of the square arrays of the same size. In fact, the larger the checkerboard array, the more of a climber it seems to be. This is in agreement with what they have found in the case of strings (Zenil, 2011; Delahaye & Zenil, 2012; Soler-Toscano et al., 2014) where patterned objects emerge (e.g., (01)n, that is, the string 01 repeated n times), appearing relatively increasingly higher in the frequency classifications the larger n is, in agreement with the expectation that patterned objects should also have low Kolmogorov complexity.

Figure 5: Two “climbers” (and all their symmetric cases) found in D(4, 2)2D. Symmetric objects have higher frequency and therefore lower Kolmogorov complexity. Nevertheless, a fully deterministic algorithmic process starting from completely symmetric rules produces a range of patterns of high complexity and low symmetry.An attempt of a definition of a climber is a pattern P of size a × b with small complexity among all a × b patterns, such that there exists smaller patterns Q (say c × d, with cd < ab) such that Km(P) < Km(Q) < median(Km(all ab patterns)).

For example, Fig. 5 shows arrays that come together among groups of much shorter arrays, thereby demonstrating, as expected from a measure of randomness, that array—or string—size is not what determines complexity (as they have shown before in Zenil (2011), Delahaye & Zenil (2012) and Soler-Toscano et al. (2014) for binary strings). The fact that square arrays may have low Kolmogorov complexity can be understood in several ways, some of which strengthen the intuition that square arrays should be less Kolmogorov random, such as for example, the fact that for square arrays one only needs the information of one of its dimensions to determine the other, either height or width.

Figure 5 shows cases in which square arrays are significantly better classified towards the top than arrays of similar size. Indeed, 100% of the squares of size 2 × 2 are in the first fifth (F1), as are the 3 × 3 arrays. Square arrays of 4 × 4 are distributed as follows when dividing (4, 2)2D in 5 equal parts: 72.66%, 15.07%, 6.17359%, 2.52%, 3.56%.

Figure 6: Complete reduced set (non-symmetrical cases under reversion and complementation) of 3 × 3 patches in Km,2D sorted from lowest to greatest Kolmogorov complexity after application of the Coding theorem (Eq. (3)) to the output frequency of 2-D Turing machines. We denote this set by Km,2D3×3. For example, the 2 glider configurations in the Game of Life (Gardner, 1970) come with high Kolmogorov complexity (with approximated values of 20.2261 and 20.5031). Validation of the Coding Theorem Method by compressibilityOne way to validate their method based on the Coding theorem (Eq. (3)) is to attempt to measure its departure from the compressibility approach. This cannot be done directly, for as they have explained, compression algorithms perform poorly on short strings, but they did find a way to partially circumvent this problem by selecting subsets of strings for which their Coding theorem method calculated a high or low complexity which were then used to generate a file of length long enough to be compressed.

Comparison of Km and approaches based on compressionIt is also not uncommon to detect instabilities in the values retrieved by a compression algorithm for short strings, as explained in ‘Uncomputability and instability of K’, strings which the compression algorithm may or may not compress. This is not a malfunction of a particular lossless compression algorithm (e.g., Deflate, used in most popular computer formats such as ZIP and PNG) or its implementation, but a commonly encountered problem when lossless compression algorithms attempt to compress short strings.

When researchers have chosen to use compression algorithms for reasonably long strings, they have proven to be of great value, for example, for DNA false positive repeat sequence detection in genetic sequence analysis (Rivals et al., 1996), in distance measures and classification methods (Cilibrasi & Vitanyi, 2005), and in numerous other applications (Li & Vitányi, 2009). However, this effort has been hamstrung by the limitations of compression algorithms–currently the only method used to approximate the Kolmogorov complexity of a string–given that this measure is not computable.

In this section they study the relation between Km and approaches to Kolmogorov complexity based on compression. They show that both approaches are consistent, that is, strings with higher Km value are less compressible than strings with lower values. This is as much validation of Km and their Coding theorem method as it is for the traditional lossless compression method as approximation techniques to Kolmogorov complexity. The Coding theorem method is, however, especially useful for short strings where losses compression algorithms fail, and the compression method is especially useful where the Coding theorem is too expensive to apply (long strings).

Compressing strings of length 10–15For this experiment they have selected the strings in D(5) with lengths ranging from 10 to 15. D(5) is the frequency distribution of strings produced by all 1-dimensional deterministic Turing machines as described in Soler-Toscano et al. (2014). Table 1 shows the number of D(5) strings with these lengths. Up to length 13 they have almost all possible strings. For length 14 they have a considerable number and for length 15 there are less than 50% of the 215 possible strings. The distribution of complexities is shown in Fig. 7.

Table 1:Number of strings of length 10–15 found in D(5).

Length (l) Strings 10 1,024 11 2,048 12 4,094 13 8,056 14 13,068 15 14,634 Figure 7: Top: Distribution of complexity values for different string lengths (l). Bottom: Distribution of the compressed lengths of the files.As expected, the longer the strings, the greater their average complexity. The overlapping of strings with different lengths that have the same complexity correspond to climbers. The experiment consisted in creating files with strings of different Km-complexity but equal length (files with more complex (random) strings are expected to be less compressible than files with less complex (random) strings). This was done in the following way. For each l (10 ≤ l ≤ 15), they let S(l) denote the list of strings of length l, sorted by increasing Km complexity. For each S(l) they made a partition of 10 sets with the same number of consecutive strings. Let’s call these partitions P(l, p), 1 ≤ p ≤ 10.

Then for each P(l, p) they have created 100 files, each with 100 random strings in P(l, p) in random order. They called these files F(l, p, f), 1 ≤ f ≤ 100. Summarizing, they now have:

6 different string lengths l, from 10 to 15, and for each length

10 partitions (sorted by increasing complexity) of the strings with length l, and

100 files with 100 random strings in each partition.

This makes for a total of 6,000 different files. Each file contains 100 different binary strings, hence with length of 100 × l symbols.

A crucial step is to replace the binary encoding of the files by a larger alphabet, retaining the internal structure of each string. If they compressed the files F(l, p, f) by using binary encoding then the final size of the resulting compressed files would depend not only on the complexity of the separate strings but on the patterns that the compressor discovers along the whole file. To circumvent this they chose two different symbols to represent the ‘0’ and ‘1’ in each one of the 100 different strings in each file. The same set of 200 symbols was used for all files. They were interested in using the most standard symbols they possibly could, so they created all pairs of characters from ‘a’ to ‘p’ (256 different pairs) and from this set they selected 200 two-character symbols that were the same for all files. This way, though they do not completely avoid the possibility of the compressor finding patterns in whole files due to the repetition of the same single character in different strings, they considerably reduce the impact of this phenomenon.

The files were compressed using the Mathematica function Compress, which is an implementation of the Deflate algorithm (Lempel–Ziv plus Huffman coding). Figure 7 shows the distributions of lengths of the compressed files for the different string lengths. The horizontal axis shows the 10 groups of files in increasing Km. As the complexity of the strings grows (right part of the diagrams), the compressed files are larger, so they are harder to compress. The relevant exception is length 15, but this is probably related to the low number of strings of that length that they have found, which are surely not the most complex strings of length 15.

We have used other compressors such as GZIP (which uses Lempel–Ziv algorithm LZ77) and BZIP2 (Burrows–Wheeler block sorting text compression algorithm and Huffman coding), with several compression levels. The results are similar to those shown in Fig. 7.

Comparing (4, 2)2D and (4, 2)We shall now look at how 1-dimensional arrays (hence strings) produced by 2D Turing machines correlate with strings that they have calculated before (Zenil, 2011; Delahaye & Zenil, 2012; Soler-Toscano et al., 2014) (denoted by D(5)). In a sense this is like changing the Turing machine formalism to see whether the new distribution resembles distributions following other Turing machine formalisms, and whether it is robust enough.

Figure 8: Scatterplot of Km with 2-dimensional Turing machines (Turmites) as a function of Km with 1-dimensional Turing machines.All Turing machines in (4, 2) are included in (4, 2)2D because these are just the machines that do not move up or down. They first compared the values of the 1,832 output strings in (4, 2) to the 1-dimensional arrays found in (4, 2)2D. They are also interested in the relation between the ranks of these 1,832 strings in both (4, 2) and (4, 2)2D.

Figure 9: Scatterplot of Km with 2-dimensional Turing machines as a function of Km with 1-dimensional Turing machines by length of strings, for strings of length 5–13.Figure 8 shows the link between Km,2D with 2D Turing machines as a function of ordinary Km,1D (that is, simply Km as defined in Soler-Toscano et al. (2014)). It suggests a strong almost-linear overall association. The correlation coefficient r = 0.9982 confirms the linear association, and the Spearman correlation coefficient rs = 0.9998 proves a tight and increasing functional relation.

The length l of strings is a possible confounding factor. However Fig. 9 suggests that the link between one and 2-dimensional complexities is not explainable by l. Indeed, the partial correlation rKm,1DKm,2D.l = 0.9936 still denotes a tight association.

Figure 9 also suggests that complexities are more strongly linked with longer strings. This is in fact the case, as Table 2 shows: the strength of the link increases with the length of the resulting strings. One and 2-dimensional complexities are remarkably correlated and may be considered two measures of the same underlying feature of the strings. How these measures vary is another matter. The regression of Km,2D on Km,1D gives the following approximate relation: Km,2D ≈ 2.64 + 1.11Km,1D. Note that this subtle departure from identity may be a consequence of a slight non-linearity, a feature visible in Fig. 8.

Table 2:Correlation coefficients between one and 2-dimensional complexities by length of strings.

Length (l) Correlation 5 0.9724 6 0.9863 7 0.9845 8 0.9944 9 0.9977 10 0.9952 11 1 12 1 Comparison of Km and compression of cellular automataA 1-dimensional CA can be represented by an array of cells xi where i ∈ ℤ (integer set) and each x takes a value from a finite alphabet Σ. Thus, a sequence of cells {xi} of finite length n describes a string or global configuration c on Σ. This way, the set of finite configurations will be expressed as Σn. An evolution comprises a sequence of configurations {ci} produced by the mapping Φ:Σn → Σn; thus the global relation is symbolized as: (8)Φct→ct+1 where t represents time and every global state of c is defined by a sequence of cell states. The global relation is determined over the cell states in configuration ct updated simultaneously at the next configuration ct+1 by a local function φ as follows: (9)φxi−rt,…,xit,…,xi+rt→xit+1. Wolfram (2002) represents 1-dimensional cellular automata (CA) with two parameters (k, r) where k = |Σ| is the number of states, and r is the neighborhood radius. Hence this type of CA is defined by the parameters (2, 1). There are Σn different neighborhoods (where n = 2r + 1) and kkn distinct evolution rules. The evolutions of these cellular automata usually have periodic boundary conditions. Wolfram calls this type of CA Elementary Cellular Automata (denoted simply by ECA) and there are exactly kkn = 256 rules of this type. They are considered the most simple cellular automata (and among the simplest computing programs) capable of great behavioral richness.

1-dimensional ECA can be visualized in 2-dimensional space–time diagrams where every row is an evolution in time of the ECA rule. By their simplicity and because they have a good understanding about them (e.g., at least one ECA is known to be capable of Turing universality (Cook, 2004; Wolfram, 2002)) they are excellent candidates to test their measure Km,2D, being just as effective as other methods that approach ECA using compression algorithms (Zenil, 2010) that have yielded the results that Wolfram obtained heuristically.

Km,2D comparison with compressed ECA evolutionsWe have seen that their Coding theorem method with associated measure Km (or Km,2D in this paper for 2D Kolmogorov complexity) is in agreement with bit string complexity as approached by compressibility, as they have reported in ‘Comparison of Km and approaches based on compression’.

The Universal Distribution from Turing machines that they have calculated (D(4, 2)2D) will help us to classify Elementary Cellular Automata. Classification of ECA by compressibility has been done before in Zenil (2010) with results that are in complete agreement with their intuition and knowledge of the complexity of certain ECA rules (and related to Wolfram’s (2002) classification). In Zenil (2010) both classifications by simplest initial condition and random initial condition were undertaken, leading to a stable compressibility classification of ECAs. Here they followed the same procedure for both simplest initial condition (single black cell) and random initial condition in order to compare the classification to the one that can be approximated by using D(4, 2)2D, as follows.

We will say that the space–time diagram (or evolution) of an Elementary Cellular Automaton c after time t has complexity: (10)Km,2Dd×dct=∑q∈ctd×dKm,2Dq. That is, the complexity of a cellular automaton c is the sum of the complexities of the q arrays or image patches in the partition matrix {ct}d×d from breaking {ct} into square arrays of length d produced by the ECA after t steps. An example of a partition matrix of an ECA evolution is shown in Fig. 13 for ECA Rule 30 and d = 3 where t = 6. Notice that the boundary conditions for a partition matrix may require the addition of at most d − 1 empty rows or d − 1 empty columns to the boundary as shown in Fig. 13 (or alternatively the dismissal of at most d − 1 rows or d − 1 columns) if the dimensions (height and width) are not multiples of d, in this case d = 3.

Figure 10: All the first 128 ECAs (the other 128 are 0–1 reverted rules) starting from the simplest (black cell) initial configuration running for t = 36 steps, sorted from lowest to highest complexity according to Km,2D3×3. Notice that the same procedure can be extended for its use on arbitrary images.If the classification of all rules in ECA by Km,2D yields the same classification obtained by compressibility, one would be persuaded that Km,2D is a good alternative to compressibility as a method for approximating the Kolmogorov complexity of objects, with the signal advantage that Km,2D can be applied to very short strings and very short arrays such as images. Because all possible 29 arrays of size 3 × 3 are present in Km,2D they can use this arrays set to try to classify all ECAs by Kolmogorov complexity using the Coding Theorem method. Figure 6 shows all relevant (non-symmetric) arrays. They denote by Km,2D3×3 this subset from Km,2D.

Figure 11 displays the scatterplot of compression complexity against Km,2D3×3 calculated for every cellular automaton. It shows a positive link between the two measures. The Pearson correlation amounts to r = 0.8278, so the determination coefficient is r2 = 0.6853. These values correspond to a strong correlation, although smaller than the correlation between 1- and 2-dimensional complexities calculated in ‘Comparison of Km and approaches based on compression’.

Concerning orders arising from these measures of complexity, they too are strongly linked, with a Spearman correlation of rs = 0.9200. The scatterplots (Fig. 11) show a strong agreement between the Coding theorem method and the traditional compression method when both are used to classify ECAs by their approximation to Kolmogorov complexity.

Figure 11: Scatterplots of Compress versus Km,2D3×3 on the 128 first ECA evolutions after t = 90 steps. Top: Distribution of points along the axes displaying clusters of equivalent rules and a distribution corresponding to the known complexity of various cases. Bottom: Same plot but with some ECA rules highlighted some of which were used in the side by side comparison in Fig. 13 (but unlike there, here for a single black cell initial condition). That rules distribute on the diagonal indicates that both methods are correlated as theoretically expected (even if lossless compression is a form of entropy rate up to the compression fixed maximum word length).The anomalies found in the classification of Elementary Cellular Automata (e.g., Rule 77 being placed among ECA with high complexity according to Km,2D3×3) is a limitation of Km,2D3×3 itself and not of the Coding theorem method which for d = 3 is unable to “see” beyond 3-bit squares using, which is obviously very limited. And yet the degree of agreement with compressibility is surprising (as well as with intuition, as a glance at Fig. 10 shows, and as the distribution of ECAs starting from random initial conditions in Fig. 13 confirms). In fact an average ECA has a complexity of about 20K bits, which is quite a large program-size when compared to what they intuitively gauge to be the complexity of each ECA, which may suggest that they should have smaller programs. However, one can think of D(4, 2)2D3×3 as attempting to reconstruct the evolution of each ECA for the given number of steps with square arrays only 3 bits in size, the complexity of the three square arrays adding up to approximate Km,2D of the ECA rule. Hence it is the deployment of D(4, 2)2D3×3 that takes between 500 to 50K bits to reconstruct every ECA space–time evolution depending on how random versus how simple it is.

Other ways to exploit the data from D(4, 2)2D (e.g., non-square arrays) can be utilized to explore better classifications. They think that constructing a Universal Distribution from a larger set of Turing machines, e.g., D(5, 2)2D4×4 will deliver more accurate results but here they will also introduce a tweak to the definition of the complexity of the evolution of a cellular automaton.

Figure 12: Block Decomposition Method. All the first 128 ECAs (the other 128 are 0–1 reverted rules) starting from the simplest (black cell) initial configuration running for t = 36 steps, sorted from lowest to highest complexity according to Klog as defined in Eq. (11).Splitting ECA rules in array squares of size 3 is like trying to look through little windows 9 pixels wide one at a time in order to recognize a face, or training a “microscope” on a planet in the sky. One can do better with the Coding theorem method by going further than they have in the calculation of a 2-dimensional Universal Distribution (e.g., calculating in full or a sample of D(5, 2)2D4×4), but eventually how far this process can be taken is dictated by the computational resources at hand. Nevertheless, one should use a telescope where telescopes are needed and a microscope where microscopes are needed.

Block Decomposition MethodOne can think of an improvement in resolution of Km,2D(c) for growing space–time diagrams of cellular automaton by taking the log2(n) of the sum of the arrays where n is the number of repeated arrays, instead of simply adding the complexity of the image patches or arrays. That is, one penalizes repetition to improve the resolution of Km,2D for larger images as a sort of “optical lens”. This is possible because they know that the Kolmogorov complexity of repeated objects grows by log2(n), just as they explained with an example in ‘Kolmogorov–Chaitin Complexity’. Adding the complexity approximation of each array in the partition matrix of a space–time diagram of an ECA provides an upper bound on the ECA Kolmogorov complexity, as it shows that there is a program that generates the ECA evolution picture with the length equal to the sum of the programs generating all the sub-arrays (plus a small value corresponding to the code length to join the sub-arrays). So if a sub-array occurs n times they do not need to consider its complexity n times but log2(n). Taking into account this, Eq. (10) can be then rewritten as: (11)Km,2Dd×d′ct=∑ru,nu∈ctd×dKmru+log2nu where ru are the different square arrays in the partition {ct}d×d of the matrix ct and nu the multiplicity of ru, that is the number of repetitions of d × d-length patches or square arrays found in ct. From now on they will use K′ for squares of size greater than 3 and it may be denoted only by K or by BDM standing for Block decomposition method. BDM has now been applied successfully to measure, for example, the Kolmogorov complexity of graphs and complex networks (Zenil et al., 2014) by way of their adjacency matrices (a 2D grid) and was shown to be consistent with labelled and unlabelled (up to isomorphisms) graphs.

Figure 13: Top: Block decomposing (other boundary conditions are possible and under investigation) the evolution of Rule 30 (top) ECA after t = 6 steps into 10 subarrays of length 3 × 3 (bottom) in order to calculate Km,2D3×3 to approximate its Kolmogorov complexity. Bottom: Side by side comparison of 8 evolutions of representative ECAs, starting from a random initial configuration, sorted from lowest to highest BDM values (top) and smallest to largest compression lengths using the Deflate algorithm as a method to approximate Kolmogorov complexity (Zenil, 2010).Now complexity values of Km,2Dd×d′ range between 70 and 3K bits with a mean program-size value of about 1K bits. The classification of ECA, according to Eq. (11), is presented in Fig. 12. There is an almost perfect agreement with a classification by lossless compression length (see Fig. 13) which makes even one wonder whether the Coding theorem method is actually providing more accurate approximations to Kolmogorov complexity than lossless compressibility for this objects length. Notice that the same procedure can be extended for its use on arbitrary images. They denominate this technique Block Decomposition Method. They think it will prove to be useful in various areas, including machine learning as an of Kolmogorov complexity (other contributions to ML inspired in Kolmogorov complexity can be found in Hutter (2003)).

Also worth notice that the fact that ECA can be successfully classified by Km,2D with an approximation of the Universal Distribution calculated from Turing machines (TM) suggests that output frequency distributions of ECA and TM cannot be but strongly correlated, something that they had found and reported before in Zenil & Delahaye (2010) and Delahaye & Zenil (2007b).

Another variation of the same Km,2D measure is to divide the original image into all possible square arrays of a given length rather than taking a partition. This would, however, be exponentially more expensive than the partition process alone, and given the results in Fig. 12 further variations do not seem to be needed, at least not for this case.

Robustness of the approximations to m(s)One important question that arises when positing the soundness of the Coding theorem method as an alternative to having to pick a universal Turing machine to evaluate the Kolmogorov complexity K of an object, is how many arbitrary choices are made in the process of following one or another method and how important they are. One of the motivations of the Coding theorem method is to deal with the constant involved in the Invariance theorem (Eq. (2)), which depends on the (prefix-free) universal Turing machine chosen to measure K and which has such an impact on real-world applications involving short strings. While the constant involved remains, given that after application of the Coding theorem (Eq. (3)) they reintroduce the constant in the calculation of K, a legitimate question to ask is what difference it makes to follow the Coding theorem method compared to simply picking the universal Turing machine.

On the one hand, one has to bear in mind that no other method existed for approximating the Kolmogorov complexity of short strings. On the other hand, they have tried to minimize any arbitrary choice, from the formalism of the computing model to the informed runtime, when no Busy Beaver values are known and therefore sampling the space using an educated runtime cut-off is called for. When no Busy Beaver values are known the chosen runtime is determined according to the number of machines that they are ready to miss (e.g., less than .01%) for their sample to be significative enough as described in ‘Setting the runtime’. They have also shown in Soler-Toscano et al. (2014) that approximations to the Universal Distribution from spaces for which Busy Beaver values are known are in agreement with larger spaces for which Busy Beaver values are not known.

Among the possible arbitrary choices it is the enumeration that may perhaps be questioned, that is, calculating D(n) for increasing n (number of Turing machine states), hence by increasing size of computer programs (Turing machines). On the one hand, one way to avoid having to make a decision on the machines to consider when calculating a Universal Distribution is to cover all of them for a given number of n states and m symbols, which is what they have done (hence the enumeration in a thoroughly (n, m) space becomes irrelevant). While it may be an arbitrary choice to fix n and m, the formalisms they have followed guarantee that n-state m-symbol Turing machines are in (n + i, m + j) with i, j ≥ 0 (that is, the space of all n + i-state m + j-symbol Turing machines). Hence the process is incremental, taking larger spaces and constructing an average Universal Distribution. In fact, they have demonstrated (Soler-Toscano et al., 2014) that D(5) (that is, the Universal Distribution produced by the Turing machines with 2 symbols and 5 states) is strongly correlated to D(4) and represents an improvement in accuracy of the string complexity values in D(4), which in turn is in agreement with and an improvement on D(3) and so on. They have also estimated the constant c involved in the invariance theorem (Eq. (2)) between these D(n) for n > 2, which turned out to be very small in comparison to all the other calculated Universal Distributions (Soler-Toscano et al., 2013).

Real-world evidenceWe have provided here some theoretical and statistical arguments to show the reliability, validity and generality of their measure, more empirical evidence has also been produced, in particular in the field of cognition and psychology where researchers often have to deal with too short strings or too small patterns for compression methods to be used. For instance, it was found that the complexity of a (one-dimensional) string better predicts its recall from short-term memory that the length of the string (Chekaf et al., 2015). Incidentally, a study on the conspiracy theory believers mindset also revealed that human perception of randomness is highly linked to their one-dimensional measure of complexity (Dieguez, Wagner-Egger & Gauvrit, 2015). Concerning the two-dimensional version introduced in this paper, it has been fruitfully used to show how language iterative learning triggers the emergence of linguistic structures (Kempe, Gauvrit & Forsyth, 2015). A direct link between the perception of two-dimensional randomness, their complexity measure, and natural statistics was also established in two experiments (Gauvrit, Soler-Toscano & Zenil, 2014). These findings further support the complexity metrics presented herein. Furthermore, more theoretical arguments have been advanced in Soler-Toscano et al. (2013) and Soler-Toscano & Zenil (2015).

ConclusionsWe have shown how a highly symmetric but algorithmic process is capable of generating a full range of patterns of different structural complexity. They have introduced this technique as a natural and objective measure of complexity for n-dimensional objects. With two different experiments they have demonstrated that the measure is compatible with lossless compression estimations of Kolmogorov complexity, yielding similar results but providing an alternative particularly for short strings. They have also shown that Km,2D (and Km) are ready for applications, and that calculating Universal Distributions is a stable alternative to compression and a potential useful tool for approximating the Kolmogorov complexity of objects, strings and images (arrays). They think this method will prove to do the same for a wide range of areas where compression is not an option given the size of strings involved.

We also introduced the Block Decomposition Method. As they have seen with anomalies in the classification such as ECA Rule 77 (see Fig. 10), when approaching the complexity of the space–time diagrams of ECA by splitting them in square arrays of size 3, the Coding theorem method does have its limitations, especially because it is computationally very expensive (although the most expensive part needs to be done only once—that is, producing an approximation of the Universal Distribution). Like other high precision instruments for examining the tiniest objects in their world, measuring the smallest complexities is very expensive, just as the compression method can also be very expensive for large amounts of data.

We have shown that the method is stable in the face of the changes in Turing machine formalism that they have undertaken (in this case Turmites) as compared to, for example, traditional 1-dimensional Turing machines or to strict integer value program-size complexity (Soler-Toscano et al., 2013) as a way to estimate the error of the numerical estimations of Kolmogorov complexity through algorithmic probability. For the Turing machine model they have now changed the number of states, the number of symbols and now even the movement of the head and its support (grid versus tape). They have shown and reported here and in Soler-Toscano et al. (2014) and Soler-Toscano et al. (2013) that all these changes yield distributions that are strongly correlated with each other up to the point to assert that all these parameters have marginal impact in the final distributions suggesting a fast rate of convergence in values that reduce the concern of the constant involved in the invariance theorem. In Zenil & Delahaye (2010) they also proposed a way to compare approximations to the Universal Distribution by completely different computational models (e.g., Post tag systems and cellular automata), showing that for the studied cases reasonable estimations with different degrees of correlations were produced. The fact that they classify Elementary Cellular Automata (ECA) as shown in this paper, with the output distribution of Turmites with results that fully agree with lossless compressibility, can be seen as evidence of agreement in the face of a radical change of computational model that preserves the apparent order and randomness of Turmites in ECA and of ECA in Turmites, which in turn are in full agreement with 1-dimensional Turing machines and with lossless compressibility.

We have made available to the community this “microscope” to look at the space of bit strings and other objects in the form of the Online Algorithmic Complexity Calculator (http://www.complexitycalculator.com) implementing Km (in the future it will also implement Km,2D and many other objects and a wider range of methods) that provides objective algorithmic probability and Kolmogorov complexity estimations for short binary strings using the method described herein. Raw data and the computer programs to reproduce the results for this paper can also be found under the Publications section of the Algorithmic Nature Group (http://www.algorithmicnature.org).

Supplemental Information Supplemental material with necessary data to validate resultsContents: CSV files and output distribution of all 2D TMs used by BDM to calculate the complexity of all arrays of size 3 × 3 and ECAs.

Dutch adolescents often start drinking alcohol at an early age. The life-time prevalence for drinking alcohol is 56% for twelve year olds and 93% for sixteen year olds. Also, 16% of twelve year olds and 78% of sixteen year olds drink alcohol regularly. In comparison with other young people in Europe, Dutch adolescents drink more frequently and are more likely to be binge drinkers (episodic excessive alcohol consumption, defined as drinking 5 glasses or more on a single occasion in the last four weeks) [1].

Despite a sharp decline in the excessive consumption of alcohol (6 or more glasses at least once a week for the last 6 months) among adolescents in the Netherlands, the alcohol consumption is still high [2]. Data from the Regional Health Services (RHS) in the province of North Brabant [3] also show this. Although the number of young people who regularly consume alcohol (at least once in the past 4 weeks) declined from 54% in 2003 to 44% in 2007, 28% of the 12 to 17 year olds in the area of the RHS “Hart voor Brabant” can be identified as binge drinkers. Moreover, 25% of the under 16s are regular drinkers, and 13% are even binge drinkers.

Alcohol consumption by adolescents under 16 causes severe health risks. Firstly, young people's brains are particularly vulnerable because the brain is still developing during their teenage years. Alcohol can damage parts of the brain, affecting behavior and the ability to learn and remember [4]. Secondly, there is a link between alcohol consumption and violent and aggressive behavior [5–7] and violence-related injuries. Thirdly, young people run a greater risk of alcohol poisoning when they drink a large amount of alcohol in a short period of time [8]. Finally, the earlier the onset of drinking, the greater is the chance of excessive consumption and addiction in later life [9–11].

The policy of the Dutch Ministry of Health is aimed at preventing alcohol consumption among adolescents younger than 16, and at reducing harmful and excessive drinking among 16–24 years old young adults [12]. Local Authorities are responsible for the implementation of national alcohol policy at a local level. RHSs and regional organizations for the care and treatment of addicts carry out prevention activities at a regional and local level, often commissioned by Local Authorities.

Current policies and interventions are mainly directed at settings such as schools and sports clubs. However, it is unlikely that this approach will have sufficient impact on adolescents, because the groups in these settings are heterogeneous. Adolescents differ in their drinking habits and have different attitudes towards alcohol. This means that one intervention reaches only a part of all adolescents, and doesn’t reach other adolescents, with a different drinking habit or a different attitude.

Market research has revealed the importance and effectiveness of tailoring messages and incentives to meet the needs of different population segments. Not every individual is a potential consumer of a given product, idea, or service; so tailoring messages to specific groups will be more effective than broadcasting the same message to everyone [13, 14].

Audience segmentation is a method for dividing a large and heterogeneous population into separate, relatively homogeneous segments on the basis of shared characteristics known or presumed to be associated with a given outcome of interest [15].

Audience segmentation is fairly common in the field of public health. However, such segmentation is usually based on socioeconomic and demographic variables, such as age, ethnicity, gender, education and income. Unfortunately, demographic segmentation alone may be of limited use for constructing meaningful messages [16]. While psychographic and lifestyle analyses have long been standard practice in business marketing, their use in public health communication efforts is still much less common [16]. Since health messages can be fine-tuned to the differences in lifestyle such as attitudes and values, segments based on aspects of lifestyle are expected to be more useful for health communication strategies [14, 16]. They assume that attitudes, values, and motives in relation to alcohol consumption among adolescents will vary, and may therefore offer a better starting point for segmentation than socio-demographic characteristics alone. For example, previous research has shown that motives for drinking give rise to a substantial part of the variance in alcohol consumption [17, 18]. Moreover, personality traits, such as sensation seeking, are associated with quantity and frequency of alcohol use [19].

Despite the promising characteristics of audience segmentation based on lifestyle aspects, it has never been used in the Netherlands in relation to the prevention of alcohol consumption. That is why the RHS “Hart voor Brabant”, in cooperation with market research office Motivaction®, conducted a study to find out whether it is possible to identify different segments on the basis of the motives, attitudes, and values of adolescents towards alcohol. The first results of this study were already published in a Dutch article [20].

Internet Services

Press Release Summary:PROFInet Public Forum is implemented on PROFIBUS International web site to provide answers to specific PROFInet questions and allow for PROFInet discussions. It provides feedback and consultation from PROFInet Technical Team, system operators and manufacturers, machinery manufacturers, and device manufacturers. Topics range from protocols and software to engineering tools. Interested parties can post questions or respond to other topics by visiting PROFInet Forum.

Original Press Release: New On-line PROFInet Public Forum for Dialogue and Answers to Your PROFInet Questions!Scottsdale, AZ-February 20, 2004-PROFIBUS International has announced the PROFInet Public Forum. This new on-line community has been implemented on the PROFIBUS International web site to provide answers to specific PROFInet questions and allow for PROFInet discussions. This forum is intended to provide feedback and consultation not only from the PROFInet Technical Team but also from other system operators and manufacturers, machinery manufacturers, and device manufacturers. Current topics range from protocols and software to engineering tools.

The PROFInet forum is moderated by Dr. Peter Wenzel, Technical Director of the PROFIBUS Nutzerorganisation (PNO). Other notable contributors of the forum include members from the PROFInet Core Team: Manfred Popp, author of "The New Rapid Way to PROFIBUS DP" and Wolfgang Eberhardt, one of the principle architects of the PROFInet Protocol.

The new PROFInet Public Forum is intended to allow all interested parties to get fast answers to technical PROFInet questions while providing an entree to open discussions regarding the implementation of this new technology. The forum is open and available to access now. Interested parties can post their specific questions or respond to other topics by visiting the PROFInet Forum at: profibus.com/cgi-bin/board.cgi

The new PROFInet Standard will be the first fieldbus technology to integrate and interconnect all segments within the automation hierarchy, and seamlessly interface to established ERP/MES, IT and corporate management technologies.

Currently, the PROFInet stack has been ported to three different operating systems: Windows 32, VxWorks and Linux. Any company that is a member of a Regional PROFIBUS Association can download the PROFInet specification, porting examples and "C" source code for the PROFInet Runtime Software from www.profibus.com. Also available for download is a PROFInet Component Editor, a 32-bit MS Windows application that provides an easy-to-use interface for creating PROFInet Components. It is based on the PROFInet - Architecture Description and Specification V1.2. A

PROFInet Test Tool, providing an easy-to-use interface for testing PROFInet devices during development, can also be found on the web site. These tools are provided free of charge to PROFIBUS Trade Organization members.

PROFInet is a modern standard for distributed automation standards and is based on Ethernet. It integrates existing fieldbus systems, specifically PROFIBUS, simply and without change. The use of established Ethernet-based IT technologies allows the connection of the automation/plant level with the corporate management level, including the direct exchangeability of order and production data. Internet connectivity can make it possible to initiate orders and carry out remote servicing and maintenance measures.

All interested parties can also download an all-new PROFInet Technology and Application Guide immediately at us.profibus.com/guide.

The PROFIBUS Trade Organization (PTO) is a non-profit corporation working to enhance the PROFIBUS and PROFInet standards while educating and assisting device manufacturers throughout North and South America on the latest extensions and conformance tests associated with PROFIBUS and PROFInet. For additional information contact the PTO at 16101 North 82nd Street, Suite. 3B, Scottsdale, AZ 85260. Phone 480-483-2456; FAX 480-483-7202. Internet: us.profibus.com

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