text:
Public Digital Backbone/Historical Context

= 2. Historical Context =

India's journey in building an extensive digital infrastructure stands out as a remarkable achievement on the global stage. This chapter chronicles the nation's strides in this domain, highlighting the catalysts for change, the challenges faced, and the milestones achieved. The aim is to provide a comprehensive overview of India's success story and its implications for other countries aiming to build similar infrastructures.


text:
Public Digital Backbone/3. India Stack: A Case Study

= 3. India Stack: A Case Study =

India Stack represents a suite of groundbreaking digital services that have transformed the subcontinent's digital landscape. From the world's largest biometric system to real-time payment platforms, this chapter provides an in-depth exploration of the elements that make up India Stack, emphasizing their individual and collective impact on the country's digital revolution

license: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/4.0/


text:
Public Digital Backbone/4. Benefits of Public Digital Backbone

= 4. What is served on my plate? Benefits of Public Digital Backbone =

Introduction:

Beyond the evident technological advancements, what does a robust Public Digital Backbone bring to the table for citizens, businesses, and governments? This chapter delves into the multifaceted benefits of such an infrastructure, ranging from enhanced service delivery to fostering innovation. We'll evaluate the ripple effects of these benefits on the broader economy, governance, and societal well-being.

*


text:
Public Digital Backbone/2. Historical Context

= 2. Historical Context =

India's journey in building an extensive digital infrastructure stands out as a remarkable achievement on the global stage. This chapter chronicles the nation's strides in this domain, highlighting the catalysts for change, the challenges faced, and the milestones achieved. The aim is to provide a comprehensive overview of India's success story and its implications for other countries aiming to build similar infrastructures.


text:
Public Digital Backbone/5. Building a Public Digital Backbone

= 5. Building a Public Digital Backbone =

Introduction:

In the intricate process of building a national-level Public Digital Backbone, the intellectual capital and research prowess of academia emerge as invaluable assets. This chapter delves into the pivotal role of academic institutions in informing and shaping policy design, underpinned by rigorous research and evidence-based insights. It is necessary to uncover the synergy between academic findings and practical implementations, underscoring how scholarly endeavors can lead to innovative solutions, ensuring seamless flows of people, money, and information within the Public Digital Backbone framework.

*

Stakeholder collaboration
Stakeholder collaboration is imperative when building Public Digital Backbone, especially in a diverse nation like India. For instance, the Aadhaar project, India's biometric identification system was developed by the Unique Identification Authority of India (UIDAI) in collaboration with the state governments, technology firms, and grassroots organizations. Another example is the Unified Payments Interface (UPI), a real-time payment system, developed by the National Payments Corporation of India (NPCI) with the support of the Reserve Bank of India (RBI) and Indian Banks' Association (IBA). These initiatives thrived because of the synergy between policymakers, technology providers, and end-users, ensuring widespread adoption and seamless integration into the daily lives of Indian citizens.

Role of government and regulators
The role of the government and regulators in India while building Public Digital Backbone has been instrumental in setting the vision, providing direction, and creating an enabling environment. For instance, the Indian government initiated the Digital India campaign in 2015, aiming to transform India into a digitally empowered society. Another example is the Unified Payments Interface (UPI) developed by the National Payments Corporation of India, a pivotal move by regulators to simplify peer-to-peer money transfers and boost the nation's digital economy. These initiatives demonstrate the proactive role of government and regulators in shaping and fostering Public Digital Backbone in India.

The private sector's contribution
While the provided content focuses on the role of academic institutions in shaping the Public Digital Backbone, it doesn't directly mention the private sector's contribution. However, drawing from broader knowledge, in India, the private sector played a significant role in building the nation's Public Digital Backbone. For instance, the development and rollout of the Unified Payments Interface (UPI) involved collaboration between the National Payments Corporation of India (NPCI) and several private sector banks and technology companies. Tech startups like PhonePe, Google Pay, and Paytm leveraged UPI to offer seamless digital payment solutions. Additionally, Reliance's Jio initiative significantly expanded internet accessibility, enabling a broader segment of the population to engage with digital public services. This kind of collaboration exemplifies the symbiotic relationship between the private sector's innovation capabilities and the government's vision for digital infrastructure.

Civil society and public participation
Civil society and public participation played a significant role in shaping the Public Digital Backbone in India. For instance, during the development and rollout of Aadhaar, India's unique identification project, several consultations were held with civil society organizations, activists, and experts to address concerns related to privacy, data protection, and potential misuse. Similarly, when the government launched the Unified Payments Interface (UPI) to simplify electronic money transfer, feedback from end-users, fintech startups, and the broader public was instrumental in refining its design and enhancing its security features. Such active participation ensured that India's digital infrastructure was not only technologically robust but also aligned with the needs and aspirations of its citizens.

Engaging academia for research and policy design

Engaging academia in the development of India's Public Digital Backbone has played a paramount role in ensuring informed and effective policy design. For instance, the evolution of the Aadhaar system, India's unique identity project, benefited from insights of researchers from institutions like the Indian Institutes of Technology (IITs) and the Indian Institute of Science (IISc), who provided technical expertise and recommendations for secure data handling. Similarly, the National Payments Corporation of India (NPCI) collaborated with academic experts to design the Unified Payments Interface (UPI), ensuring a robust and scalable digital payments system. These engagements ensured that policies and systems were underpinned by cutting-edge research and best practices, leading to infrastructures that could be efficiently integrated and widely adopted across the nation.

Design principles and components

In India, the design principles and components for building Public Digital Backbone have been driven by inclusivity, scalability, interoperability, and security. A hallmark example is the Aadhaar system, which aimed at providing a unique identification number to every resident, ensuring inclusivity. Designed for massive scale, Aadhaar can serve a population of over a billion people. Interoperability has been at the core of platforms like the Unified Payments Interface (UPI), which seamlessly connects different banking systems, enabling instant money transfers across a plethora of service providers. Moreover, the DigiLocker system showcases an emphasis on security and privacy, offering citizens a platform to store and share digital copies of their official documents securely. Together, these components have laid a robust foundation for India's Public Digital Backbone.

Implementation challenges and solutions

While building the Public Digital Backbone in India, various challenges emerged. One prominent challenge was the vast diversity in languages, cultures, and digital literacy levels across regions, making a uniform interface daunting. This was addressed by creating multilingual platforms and user-friendly UPI (Unified Payments Interface) systems to cater to users from various backgrounds. Another challenge was ensuring data security and privacy. The Aadhaar system, India's biometric ID, faced scrutiny over potential misuse of personal data. To mitigate this, the government strengthened data protection laws and implemented robust encryption standards. Additionally, issues of connectivity in remote areas were addressed by expanding digital infrastructure like BharatNet, aiming to provide high-speed internet in villages. These solutions were informed by academic research and practical feedback, creating a resilient Public Digital Backbone.


text:
Public Digital Backbone/6. Challenges and Risks

= 6. Challenges and Risks =

Introduction:

While the promise of a digital public infrastructure is immense, it isn't without its challenges. It is necessary to go deeper into the critical concerns of data privacy, security, and governance. Drawing from real-world instances and expert insights, we'll explore the intricacies of these challenges and the proactive measures that can be taken to mitigate them.

*

Data privacy concerns
Data privacy concerns in the context of Public Digital Backbone arise due to the vast amounts of personal and sensitive data that such infrastructures often handle. For instance, a national digital health system may contain records of millions of citizens, from medical histories to genetic data. If not appropriately protected, this information could be accessed and misused, leading to breaches of individual privacy. Another example is digital voting systems; if not properly secured, individuals' voting preferences could be exposed, or even manipulated. Public transportation systems using digital payment methods or tracking can inadvertently reveal a person's daily routine or location history. These instances underscore the need for robust data protection mechanisms within Public Digital Backbone to maintain the trust and confidence of the public.

Security and potential breaches
Security is of utmost importance in the realm of Public Digital Backbone, especially in a vast nation like India. The nation has embarked on ambitious projects that underscore its commitment to digital growth. The Aadhaar system, for instance, is a pioneering initiative that offers a unique identification number to every Indian citizen using biometrics. While there were concerns about its security, it has largely been successful in its reach and has been constantly updated to enhance its safety measures. The GSTN (Goods and Services Tax Network), despite initial glitches, has showcased the nation's capacity to adapt and bolster security in response to challenges. These endeavors highlight India's proactive approach in addressing and improving cybersecurity, instilling greater confidence in the nation's Public Digital Backbone.

Inclusion: Ensuring no one is left behind
In India, the drive towards creating a digital public infrastructure has been notably focused on the principle of "Ensuring no one is left behind". This commitment to inclusion is evident in initiatives like the Aadhaar program, ensuring access to crucial government services and subsidies. The Unified Payments Interface (UPI), democratizes digital transactions, allowing users from urban centers to rural villages to transact seamlessly. Additionally, the DigiLocker project provides a digital locker for citizens to store personal documents, making public service requests more streamlined. Such endeavors aim to bridge the digital divide, ensuring benefits reach every stratum of the society, from bustling metropolitans to the remote villages of India.

Governance models and oversight
In India, the evolution of Public Digital Backbone has been marked by the emphasis on inclusion, ensuring that every citizen, regardless of socio-economic status, has access to essential digital services. The Aadhaar system is a prime example of this. As the world's largest biometric ID system, Aadhaar was designed to provide a unique identity for every Indian, facilitating better delivery of public and financial services. Yet, governance models and oversight are paramount in such ventures. Concerns over data privacy, potential misuse, and surveillance led to the Supreme Court of India setting boundaries on its usage in 2018. The court's intervention underscores the need for robust governance to balance technological advancements with individual rights. Another example is the Unified Payments Interface (UPI), which revolutionized digital payments in India. While UPI ensured broader financial inclusion, its governance by the National Payments Corporation of India (NPCI) ensures standardized protocols, security, and interoperability across banks, preserving trust in the system. Both instances exemplify how governance and oversight are critical to the success and inclusivity of Public Digital Backbone in India.


text:
Public Digital Backbone/Appendices

= Appendices =

title: Public Digital Backbone/Appendices


text:
Public Digital Backbone/Glossary

General Glossary of key terms
Aadhaar: India's biometric identification system that covers over 1.2 billion people, providing a unique 12-digit identity number based on biometric and demographic data.

Civil Society: Organizations and institutions that act in the public interest to promote collective values and goals, often independent of the government.

Data Privacy: The right of individuals to control or influence what information related to them may be collected and stored and by whom and to whom that information may be disclosed.

DEPA (Data Empowerment and Protection Architecture): An initiative in India that provides a framework for consent-based data sharing, allowing individuals to control their own data.

Digital Public Infrastructure: A digital framework that facilitates the flow of people, money, and information in a society in a secure and efficient manner, often seen as a public good.

Governance: The processes, customs, policies, laws, and institutions affecting the way a group or community is directed and controlled.

Inclusion: The action or state of including or of being included within a group or structure, ensuring that services are accessible to all members of society.

India Stack: A set of platforms and services in India, including Aadhaar, UPI, and DEPA, that collectively serve as a model of digital public infrastructure.

Public Digital Backbone: The foundational digital infrastructure that underpins the functioning of a modern society, facilitating efficient, secure, and inclusive flow of people, money, and information.

Regulators: Entities or organizations, often governmental, responsible for creating, monitoring, and enforcing rules and regulations in specific sectors.

Stakeholders: Individuals or groups with an interest in a particular decision or project, including government agencies, the private sector, non-profit organizations, and the general public.

UPI (Unified Payments Interface): A real-time payment system developed by the National Payments Corporation of India, facilitating inter-bank transactions by instantly transferring funds between two bank accounts on a mobile platform.

title: Public Digital Backbone/Glossary


text:
Public Digital Backbone/7. Global Relevance

7. Global Relevance
Introduction:

In our interconnected world, the lessons learned from pioneering digital infrastructures like India's are universally relevant. We attempt to examine the potential of Public Digital Backbones in varied global contexts, drawing parallels, pointing out divergences, and outlining potential roadmaps for nations at different stages of their digital evolution.

*

Estonia: Estonia has been a pioneer in digital government, and its e-Estonia initiative has made it possible for citizens to access a wide range of government services online, from tax filing to voting. Estonia's success has been attributed to its strong political will, its focus on open data and standards, and its investment in digital infrastructure.

South Korea: South Korea has also been a leader in digital government, and its government has invested heavily in high-speed internet and mobile connectivity. South Korea's digital infrastructure has helped to boost its economy and improve the lives of its citizens.

Brazil: Brazil is one of the largest countries in the world, and it is facing the challenge of providing digital public infrastructure to its citizens in a cost-effective way. Brazil has been experimenting with a number of different approaches, including public-private partnerships and the use of open source software.

Kenya: Kenya has been praised for its use of mobile technology to provide government services to its citizens. The M-PESA mobile money platform has been used to provide financial services to millions of Kenyans, and the government has also used mobile technology to deliver healthcare services and education.

United States: The United States has a long history of public investment in infrastructure, but it has lagged behind other countries in the development of digital public infrastructure. The US government is now investing in a number of initiatives to improve its digital infrastructure, including the Build Back Better Act and the Infrastructure Investment and Jobs Act.

Singapore: Singapore is another country that has made significant investments in digital public infrastructure. The government has created a number of digital platforms that make it easier for citizens to access government services, such as the SingPass national digital identity and the GovTech National Digital Repository.

Malaysia: Malaysia is developing a national digital infrastructure called MyDIGITAL, which aims to make the country a leader in the digital economy. MyDIGITAL includes a number of initiatives to improve the country's digital infrastructure, such as the development of a high-speed broadband network and the implementation of a national cloud computing platform.

Uruguay: Uruguay has been praised for its use of digital technology to improve the lives of its citizens. The government has used mobile technology to provide healthcare services, education, and financial services to remote rural communities.


text:
Public Digital Backbone/8. Conclusion

8. Conclusion
Introduction:

As we stand at the cusp of further technological evolution, what does the future hold for Public Digital Backbones? This concluding chapter casts an eye forward, speculating on future innovations, potential expansions, and the evolving needs of societies globally. Through forward-thinking discourse, we aim to paint a picture of a world continuously bettered by digital public infrastructure.

*

title: Public Digital Backbone/8. Conclusion


text: Web applications in React, Bootstrap, MongoDB, Express/Printable version


text:
Adventist Youth Honors Answer Book/Outreach/Christian Citizenship (Ghana)

 * 1) Describe the National, AY, Pathfinder and Christian flags.

[[File:Ghana flag 300.png|alt=This shows the colours RED, YELLOW, GREEN and BLACK.   |thumb|Ghana National flagRED represents the blood of those who died in the country's struggle for independence.GOLD represents the mineral wealth of the country.

GREEN symbolises the country's rich forests.

BLACK STAR stands for the lodestar of African Freedom.|243x243px]]

[[File:Ayflag.jpg|thumb|Adventist Youth FlagThe background is red and white,

Red symbolizing the blood of Christ and white representing purity. In the centre, there is a logo that has AY which means Adventist Youth and it has three angels meaning the 3 angels message of Revelation 14.]]


text:
Fractals/color mandelbrot

Coloring algorithms
In addition to plotting the set, a variety of algorithms have been developed to
 * efficiently color the set in an aesthetically pleasing way
 * show structures of the data (scientific visualisation)

general coloring algorithms: ( feature of the pixel -> color )
 * discrete gradient:
 * last iteration (integer value) = Level Set Method = LSM
 * continous gradient: takes normalised position (floating point number in [0.0, 1.0] range) as an input and gives color as an output, not related with fractals, but used in general computer graphic; $$T : scalarvalue \to colorvalue$$

Passing iterations into a color directly
One thing we may want to consider is avoiding having to deal with a palette or color blending at all. There are actually a handful of methods we can leverage to generate smooth, consistent coloring by constructing the color on the spot.

Here
 * v refers to a normalized exponentially mapped cyclic iter count
 * f(v) refers to the sRGB transfer function

A naive method for generating a color in this way is by directly scaling v to 255 and passing it into RGB as such

rgb = [v * 255, v * 255, v * 255] One flaw with this is that RGB is non-linear due to gamma; consider linear sRGB instead. Going from RGB to sRGB uses an inverse companding function on the channels. This makes the gamma linear, and allows us to properly sum the colors for sampling.

srgb = [v * 255, v * 255, v * 255]

Continuous (smooth) coloring
The escape time algorithm is popular for its simplicity. However, it creates bands of color, which, as a type of aliasing, can detract from an image's aesthetic value. This can be improved using an algorithm known as "normalized iteration count", which provides a smooth transition of colors between iterations. The algorithm associates a real number $$\nu$$ with each value of z by using the connection of the iteration number with the potential function. This function is given by

 * $$\phi(z) = \lim_{n \to \infty} (\log|z_n|/P^{n}),$$

where zn is the value after n iterations and P is the power for which z is raised to in the Mandelbrot set equation (zn+1 = znP + c, P is generally 2).

If we choose a large bailout radius N (e.g., 10100), we have that

 * $$\log|z_n|/P^{n} = \log(N)/P^{\nu(z)}$$

for some real number $$\nu(z)$$, and this is

 * $$\nu(z) = n - \log_P (\log|z_n|/\log(N)),$$

and as n is the first iteration number such that |zn| > N, the number we subtract from n is in the interval [0, 1).

For the coloring we must have a cyclic scale of colors (constructed mathematically, for instance) and containing H colors numbered from 0 to H − 1 (H = 500, for instance). We multiply the real number $$\nu(z)$$ by a fixed real number determining the density of the colors in the picture, take the integral part of this number modulo H, and use it to look up the corresponding color in the color table.

For example, modifying the above pseudocode and also using the concept of w:linear interpolation would yield for each pixel (Px, Py) on the screen do x0:= scaled x coordinate of pixel (scaled to lie in the Mandelbrot X scale (-2.5, 1)) y0:= scaled y coordinate of pixel (scaled to lie in the Mandelbrot Y scale (-1, 1)) x:= 0.0 y:= 0.0 iteration:= 0 max_iteration:= 1000 // Here N = 2^8 is chosen as a reasonable bailout radius. while x*x + y*y ≤ (1 << 16) and iteration < max_iteration do xtemp:= x*x - y*y + x0        y:= 2*x*y + y0         x:= xtemp iteration:= iteration + 1 // Used to avoid floating point issues with points inside the set. if iteration < max_iteration then // sqrt of inner term removed using log simplification rules. log_zn:= log(x*x + y*y) / 2 nu:= log(log_zn / log(2)) / log(2) // Rearranging the potential function. // Dividing log_zn by log(2) instead of log(N = 1<<8) // because we want the entire palette to range from the // center to radius 2, NOT our bailout radius. iteration:= iteration + 1 - nu    color1:= palette[floor(iteration)] color2:= palette[floor(iteration) + 1] // iteration % 1 = fractional part of iteration. color:= linear_interpolate(color1, color2, iteration % 1) plot(Px, Py, color)

Exponentially mapped and cyclic iterations
Typically when we render a fractal, the range of where colors from a given palette appear along the fractal is static. If we desire to offset the location from the border of the fractal, or adjust their palette to cycle in a specific way, there are a few simple changes we can make when taking the final iteration count before passing it along to choose an item from our palette.

When we have obtained the iteration count, we can make the range of colors non-linear. Raising a value normalized to the range [0,1] to a power n, maps a linear range to an exponential range, which in our case can nudge the appearance of colors along the outside of the fractal, and allow us to bring out other colors, or push in the entire palette closer to the border.

$$v = ((\mathbf{i} / max_i)^\mathbf{S}\mathbf{N})^{1.5} \bmod \mathbf{N} $$

where:
 * i is our iteration count after bailout
 * max_i is our iteration limit
 * S is the exponent we are raising iters to
 * N is the number of items in our palette.

This scales the iter count non-linearly and scales the palette to cycle approximately proportionally to the zoom.

We can then plug v into whatever algorithm we desire for generating a color.

HSV coloring
HSV Coloring can be accomplished by mapping iter count from [0,max_iter) to [0,360), taking it to the power of 1.5, and then modulo 360.

We can then simply take the exponentially mapped iter count into the value and return

hsv = [powf((i / max) * 360, 1.5) % 360, 100, (i / max) * 100]

This method applies to HSL as well, except we pass a saturation of 50% instead.

hsl = [powf((i / max) * 360, 1.5) % 360, 50, (i / max) * 100]

where:

 * $$ h = (\frac{i}{max_i} 360)^{1.5}\ mod \ 360 $$

LCH coloring
One of the most perceptually uniform coloring methods involves passing in the processed iter count into LCH. If we utilize the exponentially mapped and cyclic method above, we can take the result of that into the Luma and Chroma channels. We can also exponentially map the iter count and scale it to 360, and pass this modulo 360 into the hue.

$\begin{array}{lcl} x & \in & \mathbb{Q+} \\ s_i & = & (i/max_i)^\mathbf{x} \\ v & = & 1.0 - cos^2(\pi s_i)\\ L & = & 75 - (75v) \\ C & = & 28 + (75 - 75v) \\ H & = & (360s_i)^{1.5} \bmod 360 \end{array} $

One issue we wish to avoid here is out-of-gamut colors. This can be achieved with a little trick based on the change in in-gamut colors relative to luma and chroma. As we increase luma, we need to decrease chroma to stay within gamut. s = iters/max_i; v = 1.0 - powf(cos(pi * s), 2.0); LCH = [75 - (75 * v), 28 + (75 - (75 * v)), powf(360 * s, 1.5) % 360];

Histogram coloring
A more complex coloring method involves using a histogram which pairs each pixel with said pixel's maximum iteration count before escape/bailout. This method will equally distribute colors to the same overall area, and, importantly, is independent of the maximum number of iterations chosen.

This algorithm has four passes. The first pass involves calculating the iteration counts associated with each pixel (but without any pixels being plotted). These are stored in an array: IterationCounts[x][y], where x and y are the x and y coordinates of said pixel on the screen respectively.

The first step of the second pass is to create an array of size n, which is the maximum iteration count: NumIterationsPerPixel. Next, one must iterate over the array of pixel-iteration count pairs, IterationCounts[][], and retrieve each pixel's saved iteration count, i, via e.g. i = IterationCounts[x][y]. After each pixel's iteration count i is retrieved, it is necessary to index the NumIterationsPerPixel by i and increment the indexed value (which is initially zero) -- e.g. NumIterationsPerPixel[i] = NumIterationsPerPixel[i] + 1.

for (x = 0; x < width; x++) do for (y = 0; y < height; y++) do i:= IterationCounts[x][y] NumIterationsPerPixel[i]++

The third pass iterates through the NumIterationsPerPixel array and adds up all the stored values, saving them in total. The array index represents the number of pixels that reached that iteration count before bailout.

total: = 0 for (i = 0; i < max_iterations; i++) do total += NumIterationsPerPixel[i]

After this, the fourth pass begins and all the values in the IterationCounts array are indexed, and, for each iteration count i, associated with each pixel, the count is added to a global sum of all the iteration counts from 1 to i in the NumIterationsPerPixel array. This value is then normalized by dividing the sum by the total value computed earlier.

hue[][]:= 0.0 for (x = 0; x < width; x++) do for (y = 0; y < height; y++) do iteration:= IterationCounts[x][y] for (i = 0; i <= iteration; i++) do hue[x][y] += NumIterationsPerPixel[i] / total ''/* Must be floating-point division. */'' ... color = palette[hue[m, n]] ...

Finally, the computed value is used, e.g. as an index to a color palette.

This method may be combined with the smooth coloring method below for more aesthetically pleasing images.

Muency
color From the Mandelbrot Set Glossary and Encyclopedia, by Robert Munafo, (c) 1987-2023

Java script version by Albert Lobo

title: Fractals/color mandelbrot