MachineFi’s main objective is to transition traditional IoT and machine verticals into MachineFi decentralized applications (Dapps) that will enable millions of users to participate in the machine economy with billions of smart devices.

The world has witnessed the boom of smart devices and automated machines, including smart home devices and smart cars. However, few have noticed inter-machine communication is significantly increasing. IoTeX CEO and Founder Dr. Raullen Chai, explains:

“Today, numerous machines have already started collaborating, producing, and distributing, and they consume information and resources collectively, forming a heterogeneous network of machines.” 

According to a McKinsey report, the Internet of Things (IoT) could unlock a global economic value of up to $12.6 trillion by 2030. Techjury estimates that over 125 billion devices will be connected to the Internet by the start of the next decade, powering that machine economy.

Dr. Chai explains that the convergence of artificial intelligence, blockchain, cloud computing, edge computing, the Internet of things, 5G, computer vision, and augmented/virtual reality pushes human society through the next digital revolution wave.

Companies that support this initiative include Perfios Software Solutions, PayU, CashRich, KUDOS, Huobi Global, TransUnion CIBIL, Easebuzz, Gupshup. IFTA finalists will demo innovations in diverse fintech verticals including banking infrastructure, artificial intelligence (AI), digital lending, cryptocurrency, RegTech, robo-advisory, digital identity, payments, InsurTech and more.

Past winners of IFTA have cumulatively raised over USD 3 billion in funding till date. Nitya Sharma, Co-Founder & CEO, Simpl and winner of IFTA Fintech Startup of the Year 2021 commented:

“We are both happy and proud to be awarded Fintech Startup of the Year, as an acknowledgement of the hard work we have put into building a product that completely reimagines the payment experience, both for merchants and consumers.”

Numerous fintech startups from across the globe apply for IFTA each year and go through two rounds of shortlisting by event partners, startup accelerators and experts from the sector. The finalists get an opportunity to demo their innovation in front of a distinguished Panel of Judges composed of eminent thought leaders from the finance and technology space. After thorough deliberation on numerous strong applications, 28 startups and 6 scaleup companies have been shortlisted to compete at IFTA 2021.

IFTA is proud to present the finalists for Fintech Startup of the Year 2021:

1. 3Cortex (India)
2. AccuraScan (UAE)
3. BukuWarung (Indonesia)
4. Card91 (India)
5. CredAble (India)
6. Credlix (India)
7. Decentro (India)
8. Epifi Technologies (India)
9. EpikinDiFi (India)
10. herecredit (India)
11. Inferyx (USA)
12. Karza Technologies (India)
13. KreditBee (India)
14. Lend Partners (India)
15. Money View (India)
16. Nerve Solutions (India)
17. Niyo Solutions (India)
18. PartnerHUB (Hungary)
19. Quicken (USA)
20. reach52 (Singapore)
21. Refyne (India)
22. Smartcoin Financials (India)
23. Stockal (USA)
24. Think360.ai (India)
25. Turtlemint (India)
26. WealthDesk (India)
27. Xpedize (India)
28. ZebPay (India)

IFTA is proud to present the finalists for Fintech Scaleup of the Year 2021:

1. Celusion Technologies (India) – Celusion Technologies develops Enterprise Applications focusing on Unified Account Opening and Digital Lending Platform.
2. Craft Silicon (India) – Craft Silicon’s product suite has been designed to cover the entire Microlending value chain including Core Loan Management, Group and Individual Loan Origination, Collection, Disbursement, etc.
3. Decimal Technologies (India)- Decimal Technologies is focused on digitalising BFSI, and offers solutions to reduce customer acquisition costs by moving the digital touch point closer to the customer.
4. IDfy (India) – IDfy provides verification solutions that identifies entities to prevent fraud and reduce risk.  They offer solutions to increase trust with minimal friction.
5. Intralinks (USA) – Intralinks facilitates strategic initiatives such as mergers and acquisitions, capital raising and investor reporting.
6. Sub-K (India) – Sub-K is a digital finance company that offers affordable, accessible and scalable financial and payment services to the BoP segment.

“LOVE is the culmination of working with the most extraordinary artists, technologists and designers to re-define how we communicate on the internet. Communication is about visual expression, speech and human connection. With those closest to you. And our technology caters for exactly that.”

LOVE focuses on video communication, innovates on visual expression supported by the world’s leading artists, replaces the keyboard as a human-machine interface by multi-modality, transcribes speech and translates into 50+ languages on the recipient’s end. The Company’s Co-Founder and CEO, Samantha Radocchia (‘Sam Rad’), commented:

“It is clear that the first promises of the internet have fallen short. Communication should have been enhanced by moving online. And yet, it hasn’t. Messengers are still very much like SMS, just free and faster, video calls are far from enjoyable, and social media have made communication impersonal, or even unsafe. We are on a bold mission with LOVE: a mission to restore connections and transcend boundaries, a mission to create a magical space for those closest to you, a mission to build a better internet. No ads, no likes, just a place where you can be unapologetically you.”

LOVE is ad-free, built to protect privacy and enact the right to forget. The initiative is committed to democratizing the platform by transferring ownership to its users within five years. Timm Kekeritz, the Company’s Co-Founder and Chief Design Officer, commented:

“Our goal is to make communication as simple as possible, liberating LOVE’s users from the confines of the keyboard, and creating an environment where a person can convey not only the content but also the essence of a conversation.”

LOVE’s technology stack is based on transformative AI in the domains of voice and face recognition, natural language processing, privacy and encryption, contextual analytics, and translation algorithms in order to make user interaction extremely simple. Jim Reeves, NYOUM’s Co-Founder and CTO, said:

“Many of the technologies we are incorporating into LOVE didn’t exist 2 years ago. It is one of the most exciting challenges as a technologist to be building with and innovating on the cutting edge of what’s possible.”

Hans-Ulrich Obrist, curator and artistic director at the Serpentine Galleries, London, added:

“LOVE is the future. I am excited that international artists like Ed Fornieles have engaged to shape this new democratic platform.”

The messaging interface of LOVE is built to resemble a high-tech, multimodal walkie-talkie. All a person needs to do to communicate is hit “start,” record a message, and hit “stop.” They then select a friend and hit send. The message is transcribed in real time, so the recipient can watch, listen, or read the message. Video calling is just as interesting, incorporating a physics engine to power a playful calling experience with one or many.

Python as a computer programming language can be used to build websites, create software, automate tasks, and conduct data analysis & prediction. Python is known as a general-purpose language, i.e. it can be used to create a variety of different programs and is not only limited to or specialized towards only a specific set of problems. The versatility provided and its beginner-user friendliness is also why it is the most used programming language today. It is loaded with support for multiple programming paradigms beyond object-oriented programming, such as procedural and functional programming.

Comparing Features: Python 3.9 V/s Python 3.10

In this article, we will compare the features of two of the most recent versions of the Python programming language, Python 3.9 and Python 3.10, with their respective examples and try to explore what is different and new. Enthusiasts and creators worldwide contribute to the updates of features and help the programming language be a better version of itself than before. The official Python version documentation has inspired all the code mentioned below.

Python 3.9

Support for the IANA Time Zone Database

Python 3.9 supports and has added a module named zoneinfo that lets you access and use the entire Internet Assigned Numbers Authority (IANA) time zone database. By default, zoneinfo will use the system’s time zone data if available.

Python 3.10

Although in development and fully released, the version can still be installed and tested for features.

Structural Pattern Matching

Version 3.10 introduces a new feature called Structural Pattern Matching. The matching technique allows us to perform the same match-case logic but based on whether the structure of our comparison object matches a given pattern. This feature completely changes the way one writes if-else cases.

Recently, deep learning, the basis of AI technology, has been increasingly advanced, and accordingly, deep learning research on image creation and conversion has been actively conducted. Conventional studies have focused on finding image information that is common in a domain, which is a set of images with multiple similar features. Thus, image information could not be properly used, limiting the performance of applicable data and models. Another limitation is that, because the image information used has a linearly simple structure, only one converted image can be obtained.

Professor Im’s research team hypothesized that the structure of image information may vary depending on the domain, and the structure may not always be simple, such as a linear structure. The research team designed a separator that could clearly divide image information into overall form information and style information. Based on this, they used a different weight for each domain to reflect the difference between the domains. Furthermore, they successfully developed a neural network structure to determine appropriate style information for each image composition using the correlation between the separated pieces of image information.

The developed neural network exhibits the advantage that image conversions can be easily performed for many domains, even with just one model. When the developed domain adaptation algorithm was applied to a visual recognition problem, the accuracy increased by more than double. Prof. Im says that

“In this study, a neural network that incorporates a new analysis for image information was developed, and we expect that if the relevant technology is improved a little more in the future, it can be applied to several fields, positively impacting the development of AI.“

Seunghoon Lee, a degree-linked course student majoring in Information and Communication Engineering, participated in this research as the first author. Furthermore, the paper was published in the IEEE Conference on Computer Vision and Pattern Recognition, a leading international journal in the AI field, and released online on Friday, June 25.

These robust digital fintech solutions have proven their worth by delivering the desired output to many segments of the industry. Also, FinTech is constantly evolving into a more sophisticated version. Perhaps this is the primary reason why the number of fintech projects is growing each year and bringing new progressive solutions to the financial sector.

To implement an excellent business idea, you don’t only need cutting-edge technologies, but most importantly a talented development team with the ability to create a high-quality, law-abiding software product.

1. Perfect Competencies:

An ideal set of competencies is the primary premise of a successful FinTech software development company. There should be certified developers and qualified engineers to make your software bug-free and helpful. Make sure the company you are dealing with has access to artificial intelligence, machine learning, predictive analysis, and data mining.

The FinTech solution provider should have a strong command of the coding knowledge to write clear, error-free codes for fueling your mobile financial software and system. However, standard programming languages used to develop financial solutions are C++, Java, C#, Python, Ruby, and Scala. Hence, verify that the company has dedicated experts in Java, .Net, Javascript, Python, etc.

Also, the developer should be familiar with the latest trends in the finance industry to develop a useful product. In addition, selecting an experienced provider will help you reduce the risks associated with creating a new product and achieve cost-effectiveness.

2. Cybersecurity:

Development of a Fintech software solution, security should be the biggest concern, since a minor error can affect your business drastically. So you and your policy partner should be very cognizant of the broader security issues. The FinTech solution company should be familiar with solutions to security problems. Therefore, verify their knowledge of sensitive information security procedures.

Also, verify that the IT company adheres to the proper rules and standards when developing your product. And the team should be cognizant of their role in risk mitigation and safety. All these things will assist you to make a better and informed decision.

3. Organization Capacity:

The size of your developmental team may vary at various phases of the developmental process. Ensure the organization has the right pool of engineers and other IT experts to handle the tasks of your project. Your developers must possess an adequate level of expertise and seniority. The development company should have all the dedicated members, i.e. developers, testers, UI/UX person, designer, finance expert, etc.

You must ensure that your strategic partner employs mature and efficient hiring processes to hire the best candidate. Plus, find out if your developers are familiar with the fundamentals of the fintech industry. All of this would assist you in properly analyzing your fintech software development team.

4. Previous Projects:

The projects carried out by the company are the primary measure of its success. When selecting a fintech software development partner, don’t hesitate to check out the company’s fintech project portfolio to see what types of products the team has worked with. If a company has experience in a variety of projects, its expertise grows. Engineers can also come up with new ideas based on what they have learned from previous projects.

5. Risk Management:

Fintech software development entails a high level of trust as the developed products work with highly sensitive customer data. Therefore, when selecting your development partner, ensure that the FinTech solution company has a risk mitigation plan in place to prevent future conflicts.

What are data structures?

As the name suggests, data structures hold data in the form of structures or code. In other words, data helps store collections of related data or information. Data structures are mostly used to modify, navigate and access information. They are critical in building real-life applications. To increase the efficiency of the programme, and reduce computational time, one must be aware of which data structures fit their present solutions.

Python has four in-built data structures:

• Lists or Array
• Dictionaries
• Tuples
• Sets
• List

These array-like structures allow developers to store data of different types in a sequential manner. For every element in a list, a unique address– called Index, is assigned.

To create a list, one has to use square brackets and add the element inside of it, accordingly. The elements can be added using the append(), extend(), and insert() functions. An empty list will produce an empty output.

There are other functions that can be used while working with lists:

• len() function returns the length of the list
• index() returns the index value of the value passed
• count() finds the count of the value passed
• sorted() and sort() sort the values of the list
• append() to add an item to the end of the list
• clear() to clear all items from a list

Queues

Linear data structure queue stores data in the first-in-first-out format. That is, unlike lists, a programmer cannot access elements by index. They can only extract the next oldest ement, making it usable for order-sensitive tasks such as online order processing or voicemail storage.

One can, however, use append() and pop() to implement a queue. Insert and delete operations in queues are called enqueue and dequeue. Queues are used for operations on shared resources such as a printer or CPU core, or to serve as temporary storage for batch systems.

Stack

Stacks are collections of objects supporting last-in-first-out semantics for inserts and deletes. The linear data structures are built using array structures. However, unlike arrays or lists, stacks do not allow random access to objects.

Adding elements to a stack is called push and removing is called pop. Push operations use the append() method, and pop operations use pop().

Stacks are used in language parsing, reversing words, for undo mechanisms in editors and for runtime memory management.

Graphs

These are basically pictorial representations of objects. Graph data structures represent the visual relationship between data vertices or nodes of a graph. Links connected to the vertices are called edges and are used to store data. Usually edges do not indicate the direction of flow between vertices. Directed graphs, like in linked lists, define the direction of relationship.

Graphs are usually used to convey visual web-structure networks in the form of code. They are used by Google Maps and Uber. Additionally, graphs are used to model social networking sites such as Facebook.

In Python, graphs are best implemented using dictionaries.

As we’ve noted before, C++ has historically been used for a particular set of functions in investment banks and financial services firms. By virtue of its low level memory access and therefore speed, it’s often the language of choice for high speed trading systems. This is why JPMorgan, for example, is currently hiring a C++ engineer for its JISU low-latency platform, why hedge fund Citadel wants a C++ engineer for its own market making systems, and why Goldman looks for C++ expertise for its systematic trading team.

As high speed electronic trading systems become an increasingly important differentiator and algorithmic trading takes hold beyond the equities markets, C++ expertise stands to become more sought-after in finance. Paul Bilokon, a former credit quant at Deutsche Bank and founder of AI company Thalesians, has long been an exponent. Bilokon points out that Bjarne Stroustrup, the Danish computer scientist who created C++, described it as a language for defining and using light-weight abstractions, and that this makes it peculiarly appropriate in banks and hedge funds.

“Finance is full of abstractions. And there is a lot of demand for their light-weight implementations – in derivatives pricing and, most pertinently, in high-frequency trading, where there are few alternatives to C++,” he says.

As C++ evolves, Bilokon says its use is spreading. Hedge fund Millennium specifies that its quantitative developers have, “substantial modern C++ programming experience,” a designation that it doesn’t define and that can mean different things to different people. He adds:

“Modern C++ used to mean C++11 and above, but nowadays may be taken to mean C++17 or even C++20 and above.” 

In finance and elsewhere, the more recent iterations of C++ have considerable advantages over their predecessors. There’s less use of the old C-style idioms and the language is both cleaner and more powerful, which can make users more productive. Bilokon says the upshot is C++ has caught up with Python by introducing range-based “for” loops and powerful lambda expressions. “C++20 is all about modules, coroutines, concepts, and the ranges library.”

While C++ isn’t exactly taking over from Python in finance (there are currently 2,150 Python roles advertised on eFinancialCareers versus just 785 for C++), this does mean that the language is becoming easier to use and is venturing beyond some of its historic niches. Goldman Sachs, for example, is migrating its SecDB risk and pricing system away from its proprietary language, Slang, and is looking for people who can code in both Java and C++ to help make the transition. C++ is also well-used in analytics systems, site reliability engineering and for strats roles relating to pricing, risk and P&L calculations.

Python has become a necessary language to learn if you want a job in finance. However, while students everywhere are becoming minor Python coders, the fact that C++ is harder to master can be a differentiator when it comes to getting a job. At the same time, more recent versions of C++ are easier to use than those that came before. C++ 20 has improved support for large-scale dependable software, says Bilokon

And yet, programmers rarely see the spotlight in the way that famous celebrities, CEOs, and other public figures do. Here are some of the most influential programmers of all time:

1. Larry Page

Larry Page, along with his co-founder Sergey Brin, invented Google. Today, Google continues to serve as the world’s leading search engine, as it makes the internet easily accessible to billions of people around the world.

Interestingly, Google PageRank is named after Larry Page, since Page was the programmer who created the innovative PageRank search engine algorithm.

Now, as CEO of Alphabet (Google’s parent company), Page continues to oversee cutting-edge companies that revolve around human aging, Artificial Intelligence (AI), self-driving cars, and much more.

2. Dennis Ritchie

Dennis Ritchie was an American programmer, founder of the C programming language, and co-developer of UNIX.

The C programming language is efficient, portable, and powerful. It was invented sometime between the years 1969 and 1973, at Bell Labs. Best of all, C is still one of the most popular programming languages today, as it’s commonly used in embedded hardware programming, open source software, systems programming, 3D movies, and more.

Since just about everything on the web uses C and UNIX, Ritchie’s contributions to the world of programming are immense.

3. Bill Gates

Bill Gates is a computer programmer and co-founder of Microsoft, which is the largest software company in the world. Together, Bill Gates and Paul Allen revolutionized the world of software and computing.

Despite being on track to earn a net worth of over $1 trillion dollars, Gates continues to invest billions of dollars in some of the world’s most important philanthropic causes, like improving global access to healthcare, and reducing extreme poverty.

Gates is ultimately one of the most well-known and generous programmers in the world.

4. Mark Zuckerberg

While Facebook is a topic that usually sparks some debate, there’s no doubt that Mark Zuckerberg changed the world forever when he invented the world’s first hyperconnected social network.

Through Facebook, billions of people are able to communicate with one another free of charge, regardless of one’s geographic location.

This is quite an amazing feat that has improved global communication and connectivity by a large margin.

5. Ken Thompson

Ken Thompson, who is often considered one of the pioneers of computer science, designed and implemented the original UNIX operating system. Today, UNIX and its variants continue to run on smartphones, supercomputers, military systems, global banking networks, and more.

Thompson, along with Dennis Ritchie, also re-wrote most of UNIX into the C programming language in 1973, which made development and porting significantly easier. Additionally, Thompson went on to create Belle, which was the first machine to achieve master-level play in chess.

Overall, Thompson is a programming legend who has made life significantly better for programmers everywhere.

6. Linus Torvalds

Linus Torvalds is a Finnish-American software engineer and creator of the Linux kernel, which became the kernel for operating systems like Linux OS, Chrome OS, and Android. Torvalds also created the version control system Git.

Torvalds believes “open source is the only right way to do software,” and has won numerous awards for his contributions in the technology arena. He’s an exceptionally talented and influential coder.

7. Satoshi Nakamoto

Satoshi Nakamoto is a bit of a strange case, because there is some uncertainty about the true identity of the pseudonymous Bitcoin founder. And, there are still many questions about the future that Bitcoin holds.

But as of today, with the world-changing trajectory that Bitcoin is headed towards, it’s safe to say that Nakamoto is a programmer who has already impacted how financial transactions will be conducted forever. In addition to designing Bitcoin, Nakamoto also created the first blockchain database.

8. Ada Lovelace

Ada Lovelace was an English mathematician, and the world’s first computer programmer. She was born in the year 1815, and eventually recognized that the Analytical Engine could be used for purposes beyond just crunching numbers.

Lovelace examined how technology related to humans and society, and then went on to create the first algorithm that could be used by the Analytical Engine.

She was truly ahead of her time, and had a tremendous influence on the history of computers.

9. Tim Berners-Lee

Tim Berners-Lee is the inventor of the internet. He imagined an open platform where people everywhere could freely share information, access opportunities, and work with one another despite geographical limitations.

In many ways, Lee’s vision has come to fruition, as the internet has become an amazing place where programmers are free to collaborate on any projects that they like. Thanks to Tim Berners-Lee, the world wide web continues to provide abundant opportunities for web developers, game programmers, and people from all walks of life.

10. Alan Turing

Alan Turing was a computer scientist, mathematician, logician, and creator of the Turing machine, which simulates computer algorithms. The Turing machine played a vital role in deciphering codes used during the Second World War, and therefore made Alan Turing one of the most important figures of WW2.

It’s for this reason that many people actually consider Alan Turing to be the greatest hero of WW2, and the “father” of modern day computing.

Today, Turing’s name lives on through the Turing Prize, which is the highest award that one can achieve in the field of computing.

GitLab’s latest survey of 4,300 developers and managers finds some enterprises are releasing code ten times faster than in previous surveys. Almost all respondents, 84%, say they’re releasing code faster than before, and 57% said code is being released twice as fast, from 35% a year ago. Close to one in five, 19%, say their code goes out the door ten times faster.

Developers’ roles are shifting toward the operations side as well, the survey shows. Developers are taking on test and ops tasks, especially around cloud, infrastructure and security.

Fully 43% of our survey respondents have been doing DevOps for between three and five years — “that’s the sweet spot where they’ve known success and are well-seasoned,” the survey’s authors point out. Emma Gautrey, manager of development operations at Aptum, noted:

“Just like any agile framework, DevOps requires buy-in. If the development and operational teams are getting along working in harmony that is terrific, but it cannot amount to much if the culture stops at the metaphorical IT basement door.  Without the backing of the whole of the business, continuous improvement will be confined to the internal workings of a single group.”

Matthew Tiani, executive vice president at iTech AG added

“DevOps is a commitment to quick development/deployment cycles, enhanced by, among other things, an enhanced technical toolset — source code management, CI/CD, orchestration. But it takes more than toolsets, he adds. Successful DevOps also incorporates “a compatible development methodology such as agile and scrum, and an organizational commitment to foster and encourage collaboration between development and operational staff. Wider adoption of DevOps within the IT services space is common because the IT process improvement goal is more intimately tied to the overall organizational goals. Larger, more established companies may find it hard to implement policies and procedures where a complex organizational structure impedes or even discourages collaboration.  In order to effectively implement a DevOps program, an organization must be willing to make the financial and human investments necessary for maintaining a quick-release schedule.”

According to Emma Gautrey, what’s missing from many current DevOps efforts is:

“the understanding and shared ownership of committing to DevOps. Speaking to the wider community, there is often a sense that the tools are the key, and that once in place a state of enlightenment is achieved.  That sentiment is a little different from the early days of the internet, where people would create their website once and think ‘that’s it, I have a web presence.'”

With DevOps, there is a danger in losing interaction with individuals over the pursuit of tools and processes. Gautrey stated that nothing is more tempting than to apply a blanket ruling over situations because it makes the automation processes consistent and therefore easier to manage. Responding to change means more than how quickly you can change 10 servers at once because customer collaboration is key.