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Showing posts with label Slider left. Show all posts

Thursday, October 11, 2018

Blockbid partners with Whale Tech and TMA Solutions to create Blockchain Development Centre



Cryptocurrency exchange Blockbid has teamed up with Whale Tech and TMA Solutions to offer end-to-end blockchain development solutions to external companies.
The partnership will aid key blockchain principles, such as smart contract creation, token launches/listings on the Blockbid exchange, smartcontract audits, wallet auditing and custodial services, as well as custom blockchain product development.
Blockbid, Whale Tech and TMA Solutions have recognised there is a global shortage of developers that understand and can develop for blockchain projects. Therefore, this partnership is aimed at helping foster the adoption of blockchain technology and making qualified developers readily available for external projects.
Whale Tech, founded by an experienced blockchain developer Bernard Peh, who himself has 20 years of software development experience on government and large commercial projects, helps governments, companies and individuals to adapt to the blockchain revolution by providing quality Blockchain development services and education.
TMA Solutions, established in 1997, provides software outsourcing services to leading companies worldwide. It is one of the largest software outsourcing companies in Vietnam with 2,400 engineers and has clients from 27 different countries.
The combination of the two, plus the expertise in crypto and trading that Blockbid provide, will make for an invaluable offering for the many companies looking to benefit from the advantages of blockchain technology in the coming years.
David Sapper, COO at Blockbid says: “The partnership with TMA Solutions allows us to scale-up our team almost instantly with a pool of readily available Blockchain developers of the highest possible standard, as educated by Bernard Peh who will be facilitating the upskilling and coursework”.
Bernard Peh, Founder of Whale Tech, says: “I’m very excited about the Blockchain Development Center because it is something that the global market desperately need. The BDC will be an all-in-one go to shop for anyone who needs help to turn their Blockchain ideas into reality. Most importantly, the center is backed by real industry experts.”
Dr. Nguyen Le – Chairman of TMA Solutions says: “The Blockchain Development Centre will leverage TMA’s 21 years of experience in building enterprise software solutions and engineering talents to support companies to develop end-to-end Blockchain solutions more quickly”.

Source: https://coinrivet.com/

Friday, October 5, 2018

The Blockchain-Enabled Intelligent IoT Economy

I. Setting the stage
The IoT and consumer hardware industry have seen multiple failures and a few exits over the last 12–18 months (while the B2B side has been doing a bit better overall) and some criticism has been recently made to the industry to slow down.
In spite though of the current push back, the sector is still increasing and attracting capital and talents. Clearly, there are multiple reasons as to why this is the case, but I firmly believe that one of those reasons is the convergence of IoT and Artificial Intelligence with Blockchain as the infrastructural backbone, which is unlocking the next step not only on the tech side but also on the business side.
The industry has indeed evolved from merely creating products, to create networks of products (namely, Internet of Things), to eventually creating Intelligent networks of products (I-IoT). The transition between the first and the second class was straightforward: it was enough to create more and different products and link them together. This generated many new possibilities, but it was clear from day one that it came with a series of issues hard to tackle, such as security/privacy, validation/authentication, and connectivity bottlenecks.
This is where AI and Blockchain come in. The second transition indeed is made possible through a combination of improvements in computing powers, device miniaturization, ubiquitous wireless connectivity and efficient algorithms (Porter and Heppelmann, 2014). The new class of smart products will be (and already are, to some extent) able to monitor, control, optimize, and automatize processes and products with an accuracy previously not imaginable.
Of course, as often happens, the bonus of integrating those fundamental technologies is that they ended up modifying IoT as much as IoT was impacting them in turn.
This convergence is however not accidental, but rather an inevitable necessity almost designed by default: AI needs data, IoT needs intelligence and insights, and both need security and transparent marketplaces.
The magnitude of this convergence is so high that will affect several sectors swinging from energy and manufacturing to home environment, robotics and drones, supply chain, logistics, and healthcare. Every field which is historically data-rich but information-poor will be touched (or should I say brutally hit?) by those technologies.
I will explore how in the next few sections.

II. How Blockchain is changing IoT
Blockchain as a technology is basically providing the IoT stack with a secure data infrastructure to capture and validate data. As simple as that. At least it is a simple statement that contains three different nuances:
  • Securing data better: The first one is indeed the concept of storing data securely. We know that blockchain protocols are not designed to heavily store data (they are indeed ledgers, not databases), but they can provide “control points” to monitor data access (Outlier Ventures, 2018).
  • Creating the right incentive structure: A blockchain can create the right incentive structure to share IoT data, which is something we are currently missing. Cross-sectional data have been proved to have the most disruptive impact when applied across different industries, but the problem of how and why sharing data in the first place remains. Blockchain (and tokenization) can be used to solve this economic dilemma, and once data are shared can be more easily validated, authenticated and secured.
  • Creating a network of computers: Distributing the workload and implementing parallel computing tasks is something it is usually attributed to new AI or High-Performance Computing (HPC) applications, but a blockchain would be essential in this development for authenticating and validating the single nodes of those networks. Some companies that are working on this problem are Golem, iExec, Onai, Hadron, Hypernet, DeepBrain Chain, etc.
III. How Blockchain can change AI
As I have already previously mentioned, blockchain can affect AI in multiple ways:
  • Help AI explaining itself (and making us believe it): The AI black-box suffers from an explainability problem. Having a clear audit trail can improve the trustworthiness of the data as well as of the models and also provide a clear route to trace back the machine decision process, i.e., where data are coming from, who wrote the original algorithm, what data was used for training, etc. It can establish the foundations for “algorithms standards,” as for example which main algorithms, packages, and framework have been developed using a specific training set. This is also essential in machine-to-machine interactions and transactions (Outlier Ventures, 2017), and provides a secure way to share data and coordinate decisions, as well as a robust mechanism to reach a quorum. This is extremely relevant for swarm robotics and multiple agents scenarios, as mentioned by Rob May, who is a tech investor and Talla's CEO.
  • Increase AI effectiveness: A secure data sharing means more data (and more training data), and then better models, better actions, better results…and better new data. A network effect is all that matters at the end of the day. An example of a multi-application intelligence that uses different sets of data is provided by AIBlockchain.
  • Lower the market barriers to entry: Let’s go step by step. Blockchain technologies can secure your data. So why shouldn’t you store all your data privately and maybe sell it? Well, you probably will. So first of all, blockchain will foster the creation of cleaner and more organized personal data. Second, it will allow the emergence of new marketplaces such as a data marketplace, which is the low-hanging fruit and it has currently been pursued by companies such as Ocean Protocol, OpenMined, Neuromation, BurstIQ, AtMatrix, Effect.ai, Datum, Streamr, Deuro, Datawallet, etc., a models marketplace (e.g., Dbrain, etc.), and finally even an AI marketplace, that companies like SingularityNET, Fetch.ai, doc.ai, Computable Labs, Agorai, and similars are trying to build). Hence, easy data-sharing and new marketplaces, jointly with blockchain data verification, will provide a more fluid integration that lowers the barrier to entry for smaller players and shrinks the competitive advantage of tech giants. In the effort of lowering the barriers to entry, we are then actually solving two problems, such as providing a wider data access and a more efficient data monetization mechanism. It is also possible that a blockchain-enabled AI will eventually create new organizational structures for intelligent agents to cooperate or compete.
  • Reduce catastrophic risks scenario: An AI coded in a DAO with specific smart contracts will be able to only perform those actions, and nothing more because it will have a limited action space.

IV. How AI can change IoT
AI is feeding itself with the new stream of data coming from the physical world and the billions (if not trillions) of sensors and “things” that are capturing and monitoring everything we do.
At the same time though, as soon as an AI starts making sense of IoT data flows, it will:
  • Increase data efficiency: An AI will inform those sensors on what data should be captured and stored, and above all where those sensors should be placed to be both more efficient and more effective.
  • Save costs: It is fair to think that an algorithm performance should be tested continuously, and once reached the optimal level with data marginal return approaching zero - in other words, a point in which adding more data does not improve the prediction outcome -  an AI will not store or capture more data, resulting in energy, servers, computation, cloud, and infrastructural savings. In addition to that, unplanned downtime prediction is a second cost saving possibility an AI will open for an IoT ecosystem.
  • Increase security: An AI could clearly be able to not only fight potential external threats for an IoT network but even predict them. AnChain is doing some interesting work in this field.
  • Compute on-the-fly: Edge/fog computing is quickly becoming a hot topic since it allows on-device computation, which in turn reduces the response time for an action, limits the exposures to privacy and compliance issues and solves the huge connectivity bottleneck problem. A few startups are already working in this direction, as for example Foghorn, Mythic, Neureal, SONM, Nebula AI, as well as big incumbents as Google. The company recently released, in addition to federated learning, an entire stack made by an Edge TPU and a Cloud IoT Edge platform. However, things will likely change here due to the rapid development of specialized training and inference chips and the forthcoming introduction of the 5G. Cloud is still necessary for computationally intensive operations and to store data centrally to guarantee an extra layer of security (especially in case of "network disasters"), but custom chips and edge computing algorithms can do most of the operations the final customer needs directly on the device.
V. How AI can change Blockchain
Although extremely powerful, a blockchain has its own limitations as well. Some of these are technology-related while others come from the old-minded culture inherited from the financial services sector, but all of these can be affected by AI in a way or another:
  • Consensus mechanisms: The proof of work or proof of stake are the first consensus mechanisms created but definitely neither the only ones nor the most efficient. AION has recently created a new consensus mechanism called “Proof of Intelligence” where validators are asked to train a neural network and using the parameters of that NN as proof of computation.
  • Energy consumption: Mining is an incredibly hard task that requires a ton of energy and money to be completed (O’Dwyer and David Malone, 2014). AI has already proven to be very efficient in optimizing energy consumption, so I believe similar results can be achieved for the blockchain as well. This would probably also result in lower investments in mining hardware.
  • Scalability: The blockchain is growing at a steady pace of 1MB every 10 minutes and it already adds up to 85GB. Nakamoto (2008) first mentioned “blockchain pruning” (i.e., deleting unnecessary data about fully spent transactions in order to not hold the entire blockchain on a single laptop) as a possible solution, but AI can introduce new decentralized learning systems such as federated learning, for example, or new data sharding techniques to make the system more efficient. Matrix AI is a company that is leveraging AI to fix some of the intrinsic limits of the blockchain.
  • Security: Even if the blockchain is almost impossible to hack, its further layers and applications are not so secure - see what happened with the DAO, Mt Gox, Bitfinex, etc. The incredible progress made by machine learning in the last two years makes AI a fantastic ally for the blockchain to guarantee a secure applications deployment, especially given the fixed structure of the system. Have a look at what, for example, NuCypher is doing in this space.
  • Privacy: The privacy issue of owning personal data raises regulatory and strategic concerns for competitive advantages (Unicredit, 2016). Homomorphic encryption, which is performing operations directly on encrypted data, the Enigma project (Zyskind et al., 2015) or the Zerocash project (Sasson et al., 2014) are definitely potential solutions, but I see this problem as closely connected to the previous two, i.e., scalability and security, and I think they will go side by side.
  • Efficiency: Deloitte (2016) estimated the total running costs associated with validating and sharing transactions on the blockchain to be as much as $600 million a year. An intelligent system might be eventually able to compute on the fly the likelihood for specific nodes to be the first performing a certain task, giving the possibility to other miners to shut down their efforts for that specific transaction and cut down the total costs. Furthermore, even if some structural constraints are present, a better efficiency and a lower energy consumption may reduce the network latency allowing then faster transactions.
  • Hardware: Miners, not necessarily companies but also individuals, poured an incredible amount of money into specialized hardware components. Since energy consumption has always been a key issue, many solutions have been proposed and much more will be introduced in the future. As soon as the system becomes more efficient, some piece of hardware might be converted for neural nets use. The mining colossus Bitmain is already doing exactly this.
  • Lack of talent: This is a leap of faith, but in the same way we are trying to automate data science itself (unsuccessfully, to my current knowledge), I don’t see why we couldn’t create virtual agents that can create new ledgers themselves, and even interact on it and maintain it.
  • Data gates: In a future where all our data will be available on a blockchain and companies will be able to directly buy them from us, we will need help to grant access, track data usage, and generally make sense of what happens to our personal information at a computer speed. This is a job for (intelligent) machines.
VI. How IoT is affecting AI
The generation and analysis of data that were not available earlier open a new spectrum of possibilities for an AI to:
  • Become more efficient: This is pretty straightforward, but new both structured and unstructured data can feed an AI and be used for new use cases or achieve a better performance on the existing ones.
  • Improve existing design: Products and services are going to be designed differently from how we know them given the new data an algorithm can digest and analyze.
  • Change the buyer-seller dynamic: The internet of things shifts completely and perhaps counterintuitively the attention from the hardware to the software. The sensors (and their costs) are becoming irrelevant and the post-sales improvements that a manufacturer can do without changing the hardware are the real secret sauce to make an AI more efficient.
VII. How IoT could change blockchain
If there is a clear trend emerging, it is that decentralized systems are hard to work with and expensive to maintain. Although the relationship is less intuitive than other more direct links, IoT can help blockchain in:
  • The nodes structure: IoT devices often act as lightweight nodes of the chain, which are those nodes that simply pass data to the full nodes that instead store the data, create new blocks, and ensure validity. Better and more powerful devices, possibly powered by AI, can turn every lightweight node into a full one.
  • Reducing energy consumption: A network of more efficient hardware devices could indeed help to reduce the current energy consumption of blockchain stacks.
  • Reduce bandwidth and data burden: There are multiple ways to design an IoT-blockchain architecture (Reyna et al., 2018), and of course at least one of those architectures may result in a system where IoT devices communicate and share information between them and eventually load on the blockchain only the relevant data, therefore reducing both bandwidth and data burden.
VIII. Conclusions
As you might have noticed, the edges of the impact of one technology on the others and vice-versa often blur, and this is not by chance but an inevitable consequence of technologies that are born and developed to create an “intelligence flywheel.”
In addition to unlocking a set of new technological scenarios, the integration of blockchain, IoT and AI has generated new powerful business models. The shift from product to service and ownership to access is the key to understand the magnitude of the changes in the tech ecosystem. Even more radically, product-as-a-service and product-sharing business models are emerging and winning in almost every markets, leaving the manufacturer in charge of the ownership as well as maintaining the full responsibility of the product and service operation.
It is counterintuitive and even a bit absurd, if you think about it, that the surge in the hardware industry is in fact shifting the attention toward a “servitization” model (Porter and Heppelmann, 2014), which clearly makes more sense where the cost of service is a significant part of the greater cost of ownership (that is the case in the current technology landscape).
This integration does not come without issues, as we have seen, both technical and commercial, as much as of design. Data democratization may also soon erode the data moat barrier AI companies are nowadays building their empires on. Software and algorithms are no longer private but rather open-source. Computational power is now affordable and will be processed directly on-device. What does it all mean for the evolution of the industry? Who knows. I have no idea of how these phenomena will shape our businesses and lives, but I am sure that the changes will happen at an exponential rate.

Source: https://www.forbes.com/sites/cognitiveworld/2018/10/04/the-blockchain-enabled-intelligent-iot-economy/#73b94a652a59

IoT poses special cyber risks



 Internet-connected devices pose special risks for federal agencies, and the National Institute of Standards and Technology is developing guidance to meet the need.
Connected sensors, smart-building technology, drones and autonomous vehicles can't be managed in the same way as traditional IT, according to a NIST draft publication, Considerations for Managing Internet of Things (IoT) Cybersecurity and Privacy Risks. The document points out that basic cybersecurity capabilities often aren't available in IoT devices.
Federal agencies must “consider that IoT presents challenges in achieving those [cybersecurity] outcomes or there are challenges that IoT may present in achieving security controls -- and we wanted to highlight those,” Katerina Megas, program manager for NIST's Cybersecurity for Internet of Things program, told FCW at the Internet of Things Global Summit on Oct. 4.

"We felt putting out something initial on IoT was the most important -- to get something out as quickly as possible," she said. "There will be plans in the future to get more focused, more specialized."
One of NIST's next steps is to develop a potential baseline of cybersecurity standards for IoT devices, she said.
NIST is accepting comments on the draft through Oct. 24. Before a final version is published, Megas said, "we plan on starting to release iterative discussion documents to talk about if there were a baseline for IoT devices."
Robert S. Metzger, a government contracting attorney at Rogers Joseph O'Donnell, said that the federal government is exposed to the security and privacy risks of the IoT ecosystem through relationships with vendors.
"The IoT is all over us whether we know it or not,"  Metzger said. "Even if government is not buying it, so many surfaces upon which government depends are using it. Vendors are using it, and so the government becomes, if you will, not so much a hostage but among those exposed to the IoT deployment by commercial enterprises."
Although the IoT creates new and more attack surfaces for potential bad actors, and it opens up both networks and hardware to potential threats, that doesn’t mean it should be shunned, Metzger said at the conference.
One place the government can begin to ask for better security is in the procurement process for these technologies, according to Tom McDermott, the deputy assistant secretary of cyber policy at the Department of Homeland Security.
"We are always looking to think about how we can use federal procurement authority and federal procurement power to drive better cybersecurity outcomes," McDermott said.
A bill proposed by Sens. Mark Warner (D-Va.) and Cory Gardner (R-Colo.) last year would impose basic cybersecurity standards on IoT devices procured by the federal government, including changeable passwords and a requirement that software and firmware be patchable. So far, the bill hasn't advanced, although a companion measure was introduced in the House of Representatives.
Separately, NIST put out a call in April for ideas on lightweight encryption, with an eye to developing security measures that could be deployed on resource-constrained IoT devices.

Source: https://fcw.com/articles/2018/10/04/iot-nist-cyber-leonard.aspx

Thursday, October 4, 2018

A Two-Minute Guide To Artificial Intelligence



If you keep hearing about artificial intelligence but aren’t quite sure what it means or how it works, you’re not alone. 
There’s been much confusion among the general public about the term, not helped by dramatic news stories about how “AI” will destroy jobs, or companies that overstate their abilities to “use AI.”
A lot of that confusion comes from the misuse of terms like AI and machine learning. So here’s a short text-and-video guide to explain them:


What’s the difference between AI and machine learning?
Think of it like the difference between economics and accounting.
Economics is a field of study, but you wouldn’t hire a Nobel Prize-winning economist to do your taxes. Likewise, artificial intelligence is the field of science covering how computers can make decisions as well as humans. But machine-learning refers to the popular, modern-day technique for creating software that learns from data.
The difference becomes important when money is at stake. Venture capital investors often dismiss AI as full of hype because they’ve got skin in the game. They prefer startups that make machine-learning software with a clear, commercial application, like a platform that can filter company emails with natural language processing or track customers in a store with facial recognition (these are real businesses).
On the other hand, universities and some large tech companies like Facebook and Google have large labs carrying out research that drives the wider field of AI forward. A lot of the tools they invent, like TensorFlow from Google or Pytorch from Facebook, are freely available online.

Why does the term “learning” (e.g., deep learning) crop up everywhere? 
Because the most exciting application of AI today gives computers the ability to “learn” how to carry out a task from data, without being programmed to do that task.
The terminology is confusing because this involves a mishmash of different techniques, many of which also have the word “learning” in their names.
There are, for instance, three core types of machine learning, which can all be carried out in different ways: unsupervised, supervised and reinforcement, and they can also be used with statistical machine learning, Baeysean machine learning or symbolic machine learning.
You don’t really need to be clued up on these though, since the most popular applications of machine learning use a neural network.

What’s a neural network?
It’s a computer system loosely inspired by the human brain that’s been going in and out of fashion for more than 70 years.

So what is “deep learning”? 
That’s a specific approach to using a neural network—essentially, a (deep) neural network with lots of layers. The technique has led to popular services we use today, including speech-recognition on smartphones and Google’s automatic translation. 
In practice, each layer can represent increasingly abstract features. A social media company might, for instance, use a “deep neural network” to recognize faces. One of the first layers describes the dark edges around someone’s head, another describes the edges of a nose and mouth, and another describes blotches of shading. The layers become increasingly abstract, but put together they can represent an entire face.

What does a neural network look like on a screen—a jumble of computer code? 
Basically, yes. Engineers at Google’s AI subsidiary DeepMind write nearly all their code in Python, a general purpose programming language first released in 1991.
Python has been used to develop all sorts of programs, both basic and highly complex, including some of the most popular services on the Web today: YouTube, Instagram and Google. You can learn the basics of Python here.

Does everyone agree that deep-learning neural networks is the best approach to AI? 
No. While neural networks combined with deep learning are seen as the most promising approach to AI today, that could all change in five years.

Source: https://www.forbes.com/sites/parmyolson/2018/10/03/a-two-minute-guide-to-artificial-intelligence/#af5b6cb61c0a

Wednesday, July 18, 2018

Testers knowledge towards automation skills



If you look around the testing landscape you will see that manual testing roles are disappearing, but not manual testing itself.

Despite this, this skill set of a manual tester is still needed. According to Brijesh Deb, Agile Testing Evangelist and High Tech Test Manager at Sogeti, this is simply because it is impossible to have zero manual intervention. Every software, be it a mobile app or a component of the NASA rocket, would have to go through some kind of a manual test at some level, at least.
Nevertheless, it appears that not all testers have the skill set to carry out automation tests.
Deb commented: “Test automation is a far bigger animal with a much greater scope where everything from the inception, to the design to the coding, everything is done through automation.

Testing evolution

“The quality parameters have changed with a lot of additional weight now being given to non-functional parameters such as performance and security.
“What this means is that the skill set of the testers has also had to evolve. With this changing outlook of the software testing industry and the evolution in testing, it is imminent that testers add automation to their repertoire as manual testing alone is not going to be enough.
“About a decade or so ago, there was a lot of impetus being given to UI tests and UI was the primary candidate for automation alongside regression tests. Despite this, manual testing can be ubiquitous as the tests and code written for automation are, typically, written manually.”
Furthermore, Anand Bagmar, Founder of Essence Testing, believes automation is NOT the only skill for a tester to contribute and be effective.
”There are many other areas where they can add value – but they need to be able to learn, understand and show a willingness to get close to technology and code – that is non-negotiable from my perspective,” Bagmar added.

Skills & capabilities

In order to build good quality software that will give value to the users of that functionality and in-turn, the creator of the product as well, Bagmar recommends testers must have the following skills and capabilities:
  • Have a testing-mindset
  • Understand and radiate risk
  • Be smart and effective in ways of working
  • Optimise where possible
  • Evolve in learning and understanding
  • Ability/willingness/freedom to experiment and learn from what works well, or not
  • Collaborate with all relevant roles for deeper and shared understanding.
Deb continued: “Most of the time teams try to write code for tests which are executable manually and call it test automation. What confuses this, even more, is the testing vs checking debate.
“Be it the approach of testing the software manually or with automated tests, one common skill the testers must possess is “Test Craftsmanship”. Test Craftsmanship is a combination of the right testing mindset, with the knowledge of various testing tools and techniques. For manual testing, a solid grounding in test craftsmanship i.e., right testing mindset and the knowledge of various test design techniques might just be enough as it is more procedural in nature.
“Automation testing, on the other hand, requires knowledge of additional tool and languages for the tool to work. Depending on the context, both approaches serve different purposes and are equally important.”

DevOps & continuous delivery

Since the last decade, the focus is moving really quickly to DevOps. This means continuous integration (CI) and continuous delivery (CD) is absolutely impossible without continuous testing (CT). Yet, the fastest (or only) way to achieve CT is through test automation.
“In the fast-moving delivery and release life-cycles, manual testing does not provide much value. We need to focus on a healthy combination of exploratory testing and test automation (of all applicable types) to be effective as a team to build a good quality product. Any test that is important to be re-executed over a period of time needs to be automated at an appropriate level in test automation,” added Bagmar.
“While there is so much focus on test automation, one of the hindering factors for testers from taking up test automation as a career option is the ‘Fear of Code’”.
Since test automation involves writing code that involves accurate knowledge of one or more programming language(s), this scares a lot of engineers, according to Deb. There is a common misconception in the testing world that testers normally do not have access to the code and are more often than not involved in black box testing. So, there is no need to actually learn to programme.
“What testers do not realise is the fact that the knowledge of code will help them, investigate defects, debug errors and expand their avenues more by helping them find the unknown. Testing IMO is more than just finding defects. It is about finding the unknown and helping make the software better,” continued Deb.

Technology involvement

The most important skill required from the tester is the ability to get hands-on involved in technology. This involvement can be at various levels according to Bagmar:
  • Be able to understand/read code and make sense out of the same (logic)
  • Be able to understand/read the existing automated tests to know what “intents” have been automated – this reduces waste by having to repeat the same intent-validations manually
  • Do effective gap-analysis based on what has already been automated, and what would add additional value if automated. Thus, the knowledge of what does not need to be automated, hence focus on more deeper learning, understanding and exploration using human-mind
  • Contribute to enhancing automated test suite (unit/integration, API, UI/end-2-end /performance/security/etc.)
  • Contribute to building a more testable and functional architecture.
 Source: http://www.softwaretestingnews.co.uk/testers-knowledge-towards-automation-skills/

Friday, December 15, 2017

Microsoft unveils new AI development tools at its Connect(); conference


Microsoft Corp. today kicked off Connect(); 2017, its annual developer conference in New York, with the introduction of several new tools designed to help companies build artificial intelligence applications and other modern software.

The first additions are rolling out for Visual Studio, the company’s popular coding platform. Chief among them is a package called Visual Studio Tools for AI that lets developers quickly get started with a new project using their machine learning engine of choice. The bundle supports Microsoft’s CNTK framework, as well as popular outside alternatives such as the Google Inc.-created TensorFlow.

Once a machine learning model is ready, Visual Studio Tools for AI can be used to ship off the code to Azure Batch AI. It’s a service within Microsoft’s public cloud that provides a convenient environment for training machine learning models.

Specifically, the offering enables developers to have their AI applications practice on sample data until they become accurate enough for real-world use. At that point, a team can employ one of the new deployment tools that Microsoft is launching alongside the machine learning package to roll out the model to the target platform.

A company building an AI-powered mobile app, for example, would use AI Toolkit for Azure IoT Edge. The framework allows for models to be deployed to connected devices by way of Microsoft’s public cloud. Another new framework in turn lets companies run AI software directly inside their Azure SQL Database deployments.

Microsoft debuted the artificial intelligence tools alongside a number of more general-purpose products that should appeal to even more developers. Most notably, the company has unveiled a managed version of the popular MariaDB database and Azure Databricks, a new service based on the popular Apache Spark analytics engine. It’s the fruit of a yearlong collaboration with Databricks Inc., the recently funded startup that was founded by project’s creators to commercialize their work.

Back in Visual Studio, a new tool called Live Share aims to ease collaboration for development teams. Microsoft says that the capability will enable multiple programmers to edit and debug code in a single view a la Google Docs.

The company has also announced several smaller enhancements across its development and cloud portfolios that are being spotlighted at Connect(); as well. A complete list of the features unveiled at the event so far can be found here.

Source: Siliconangle

Thursday, September 14, 2017

Learning How to Code: The Best iOS Apps

One of the most powerful skills that can be acquired in an era of electronic and technological change is the ability to code various programs. While this may have been reserved for those who have a formal education, many online resources have been made available to enable everyone to learn. From basic programming to more advanced algorithms, the notion of learning to program has been democratized significantly so that anyone from anywhere in the world can begin. To further this elimination of barriers, apps can serve as a mobile source of education which this article explores in depth.
Encode: Learn to Code
Websites are one of the most popular things to start creating when a user wishes to learn how to code. As a basic but comprehensive guide on how to develop webpages, Encode takes users through step-by-step lessons that cover everything from images to styles. By offering the lessons in smaller portions as opposed to larger lectures, comprehension and retention is maximized, especially for beginners. Beyond just being able to learn how the code works, Encode enables users to also code right on the app with a series a tests and challenges to reinforce what they have learnt. Through learning HTML and CSS, real webpages can be created directly on the app with the ability to quickly refer to the lessons if needed. As an all-in-one tool for those who wish to learn the basics and fundamentals of web programming, Encode is a highly recommended app that gives lessons, the interface to code, and the ability to make real websites in a single location.
Khan Academy: you can learn anything
As Khan Academy is well recognized for providing learning materials for student beyond the classroom, a large portion of the available content is dedicated to teaching various aspects of programming. From basic web programming to mobile apps, Khan Academy covers nearly the entire realm of coding in the form of videos that are broken down into logical segments that are easy to follow. As the lessons are done through videos instead of text, it is much easier to follow along with the material which can be played multiple times and wherever a user is so that learning can take place from anywhere. With only the most qualified instructors to give the lesson, Khan Academy is perfect for those who are just starting out with programming to those who are professionals looking to expand their skillset. Given that so many programming languages are offered in the app, it is one of the best tools available on the App Store.
Learn to Code with Python
Python is one of the most sought after programming languages that are out there as a result of the applications that are possible to build with it. To learn Python right on the phone, Learn to Code not only teaches the fundamentals, but also lets users code right on the app so that no special program needs to be downloaded to begin. Furthermore, the app gamifies the learning process by introducing various badges when a user achieves a certain level or milestone in their coding career. In doing so, it becomes more of a game and challenge to learn the language as opposed to a typical classroom lesson. To further this, those on the app can engage in coding challenges that involves levels, experience points, and various rounds to make it significantly more fun to learn. With access to the community of Python coders, Learn to Code is a highly recommended app for those who wish to learn the language with a highly engaged and like-minded community.
Swifty: Learn to code tutorials for Swift
As Swift is the new standard for iOS apps, it is only natural to be able to program iOS apps right from the phone. Swiftly is a tutorial-based app designed to help beginners and experts further develop their skills ranging from the fundamentals of “ifs” and “variables” to “arrays”. Similarly to the previous coding apps, Swiftly also lets its users practice through incorporating challenges that can be completed to gauge how well a person is progressing in their learning. As learning to code can be a tedious task that is frustrating for many, the app implements various mechanisms to encourage motivation and consistency in learning. For example, by having achievements that can be earned once specific milestones have been met, users are encouraged to learn more while the streaks helps incentivize users to return to the lessons on a consistent basis otherwise the streak will be restarted. For those who are interested in learning how to develop apps for iOS, Swiftly is one of the most comprehensive tools that can help anybody learn or improve.
Mimo: Learn to code on the go
For an exhaustive curriculum that encompasses everything from websites to apps, Mimo is an extremely popular source of technical content for new and veteran developers alike. From websites to apps and even hacking, users can choose which field they would like to pursue and then be given the corresponding lessons. With challenges that make each lesson more engaging, no matter which field of programming a person may be interested in, they can learn at a much faster rate than simply reading books. Furthermore, one of the best features of Mimo is that real projects can be developed so that coding can be done whenever it is most convenient for the user right from the app. Similar to the other apps listed in this article, achievements can be earned as a means to encourage progress. With so many different languages available on Mimo, the app is one that is highly recommended for anybody looking to further their development skills.
As software and programming becomes increasingly important, the apps listed in this article can significantly benefit those who are new to coding or are professionals seeking to expand their skillset. From websites to algorithms, mobile apps have greatly contributed to the democratization of learning code from only those who are formally educated in the space to nearly anyone with a computer.

Source: Appolicious

Thursday, September 7, 2017

3 Helpful Ways For Developers To Improve IoT Security On Their Devices

Following these three simple tips, you can enhance your IoT security without creating a needless hassle or paying too much as a developer.
The much-beloved Internet of Things has come to fundamentally reshape how firms in virtually every industry operate. Nonetheless, the 21st century phenomenon that’s connected us all has some significant downsides, chief among them its vulnerability to outside attacks.
As consumers and producers of IoT gadgets alike are finding their privacy and security increasingly jeopardized, many don’t know where to turn to for advice. By following these three simple tips, you can enhance your IoT security without creating a needless hassle or paying too much.
Ensure your gadgets are patchable
A staggeringly large amount of everyday IoT gadgets sold on the market come equipped with pre-prepared passwords which are essentially impossible to change, or, even worse, are just entirely impossible to patch. In the ever-changing digital world of the 21st century, IoT devices need to be patchable so they can be updated to resist the latest trends in malware attacks.
While leaders of the industry such as Apple or Microsoft take steps to send out regular updates and prevent their products from being vulnerable to the latest attack, many smaller companies fail to do the same. As more and more computers and sensors become embedded in virtually everything in society – from our infrastructure to our businesses – an unpatchable nightmare is developing. If this problem isn’t remedied soon on the supply-side of the IoT, it could unravel into an unfixable mess.
Focus on simplicity
The IoT industry is already well-acquainted with the idea of simplicity, as it’s long been a hallmark of digital gadgets. Nonetheless, many firms don’t take the steps necessary to ensure that the process of using and, critically, updating their gadgets and software is as easy as possible on the consumer-end of things.
Companies responsible for protecting your device should make the process of patching their products as easy as possible for their consumers, many of whom may not be particularly tech-literate. Users should be alerted about the latest security breaches, and receive a number of messages detailing what specific steps they need to maintain their security.
Small measures like this, which help strengthen the weakest-link in most security structures, everyday human users, can go incredibly far in strengthening your IoT security. Making it simple and easy for your users to regularly change their usernames or passwords, for instance, is a relatively stress-free change developers of IoT gadgets and applications could make to remove some vulnerabilities.
As attackers rely on hijacking huge numbers of relatively under-protected (and sometimes entirely unprotected) devices, the small steps that make even a few gadgets harder to break into can have a huge impact on their ability to successfully carry out attacks.
Don’t rely on a silver bullet
Many producers and consumers of IoT devices often have unrealistic security expectations, meaning the battle has been lost before it’s even really begun. While guaranteeing perfect security is a pipedream, those tasked with securing the IoT aren’t entirely helpless, and shouldn’t rely on a magical silver bullet to come and save them.
There is no true consensus for what the best IoT security practices are – many different experts and companies take their own unique approaches to the problem, and their responses are often very different from one another. Rather than waiting on innovation to deliver a god-sent solution to their problems, security experts would be well advised to keep pushing the envelope and trying to develop multi-faceted approaches to security.
As Wind River points out in their white paper on IoT security, itself entitled “searching for the silver bullet”, security experts would be better suited to take the developments of the last 25 years and attempt to modernize them to meet today’s problems. Constant reengineering will be necessary to meet the ever-shifting demands of tomorrow, but IoT users would be letting decades of progress go to waste if they turned their backs on yesterday’s achievements.
Building security infrastructure “from the bottom up,” as Wind River encourages, is the only effective way to guarantee the IoT well into the future. While we’re unsure of what threats we’ll face from malicious hackers and software tomorrow, we can rest assured that expanding user’s access to security features and educating the public on common security-pitfalls is a step in the right direction.
As Wind River’s paper highlights, security cannot be thought of as an after-measure. Rather, producers of IoT gadgets and apps must commit the necessary funds – no matter how expensive – to ensure security as early in the development process as possible.
The IoT is far too wonderful to be sacrificed to malevolent attackers who seek to exploit gaps in its security. Companies and users alike must rely on their own determination and commitment to a new, ever-expanding set of security norms if they hope to benefit from the IoT well into the future.
Source: Networkworld

Friday, August 18, 2017

What Makes An Internet Of Things (IoT) Platform Enterprise-Ready?

Answering the question of what makes an IoT platform scalable enough to excel in an enterprise needs to take into account application enablement, connectivity management, and data aggregation and storage. These and other insights are from a recent article by the Boston Consulting Group, Who Will Win The IoT Platform Wars? The report provides a useful framework for evaluating the enterprise readiness of the proliferating number of  IoT platforms available today.
The following key takeaways offer a glimpse into the IoT platform landscape and how enterprises can determine if IoT platforms of interest will be able to scale to their needs:
  • Enterprise software & services (22%) and IoT start-ups (32%) offer the largest amount of IoT platforms today. 18% of IoT platforms are being developed and sold by industrial technology providers who are looking to shift away from their hardware-centric business models. Superior design, extensibility, intuitive user experience and incentives for developers to support their IoT platforms are what’s need to excel in the enterprise. BCG found that 65% of enterprise buyers have a strong preference for purchasing their IoT platforms from enterprise software and services providers.
  • The real test of any vendor’s IoT platform is to see if it supports application enablement, data aggregation and storage, and connectivity management. Ideally, IoT platforms orchestrate applications, data, devices, systems, and processes providing business strategists with a scalable framework they can rely on to accomplish long-term goals. In reality the majority of IoT platforms today can’t provide this level of support to business strategies. BCG found that a scalable enterprise-ready IoT platform provides application enablement, data aggregation and storage, and connectivity management. The following graphic provides an overview of core IoT platform requirements.
Boston Consulting Group, Who Will Win The IoT Platform Wars?

  • Just 14% of IoT platforms are enterprise-ready. IoT pure-play companies/startups lead all providers of enterprise-wide IoT platforms today. Enterprise software & service providers are next at 36% of all platform providers meeting all three criterion. In the next three years, the pace of IoT pure-play companies/startup acquisitions is going to accelerate as enterprise software & service providers look to gain needed new technologies quickly.
Boston Consulting Group, Who Will Win The IoT Platform Wars?
  • IoT platform providers who earn developer loyalty have the best chance of dominating the market for years to come. Creating an open source IoT platform supported by a vibrant, growing community that excels at knowledge sharing is the most effective strategy for attracting developers. Additional criterion developers evaluate IoT platforms on are the potential to gain platform knowledge and improve their career prospects, and availability of high-quality APIs and available tools. The following graphic provides an analysis of what developers consider the most important criterion they consider when choosing an IoT platform to develop on. Enterprise software & service providers are using a variety of incentives including free training, certification, and the opportunity to earn cash bonuses for accelerating development and delivery of new applications on their platforms.






Source: https://www.forbes.com/

Thursday, June 16, 2016

How To Be a Superhero Software Developer, Agile or Not

If you've seen any trailers for movies lately, you know that superheroes are the new hotness. In a blatant rip-off of the genre, our Agile Architect describes how to have your own secret origin as a super software developer.


Everyone who knows me knows that I collect comic books. I've long passed the days when I was somewhat ashamed to admit that a grown man's favorite super-hero is The Green Lantern. Now I'm taking the next step and writing about them in my purportedly technical column. For those of you waiting for an agile angle to this, trust me. I'll get there. For those of you happy to read about comic books. Cool! So let's dive in.
There are two main aspects to comic book collecting: collecting as a financial investment and collecting for enjoyment. While there are many people like me who purchase and read comics for enjoyment, I am always amazed at the people who collect comics only for investment purposes. They don't even read the comics they buy. In fact, there is a relatively recent initiative in comic book collecting to "slab" comics. That is, to have their condition professionally graded, then entomb the comic book in a sealed, archive quality plastic sleeve with a certificate indicating the condition. At this point, the comic book becomes, to me at least, a worthless commodity to be sold or traded purely for its perceived value. In other words, its valuable because somebody else is willing to buy something that they also cannot read or enjoy.

Many of these investors spend a boat-load of money buying the new hot book on the shelf. I've seen people purchase hundreds of copies of a single book, thinking that each copy would someday bring them a fortune. It doesn't.
In truth, if you are truly interested in making money on comic books, you should gather up as much money as you possibly can and invest it in a few (or even one) near-perfect copy of an early issue of a well known character like Superman or Batman. If you can afford to purchase and hold onto a copy of Action Comics #1 from June 1938, the first appearance of Superman, you are bound to make money. If you buy 200 copies of anything on the shelf today at $3 to $5 dollars apiece, they will probably bring you 25 cents apiece in a few years, maybe $1 if you are lucky.
But if you collect comic books because you like to read them, and you buy the things you like to read, every once in a while there's a gem that makes it into your collection before it becomes valuable.



So how does this relate to software and agile? I've been working with a group of recent Ph.D. physicists who are interested in moving from academia into commercial software development. While they are extremely bright and have some advanced algorithm programming skills, it's gotten me thinking about how to succeed in the commercial software industry. And as you may have guessed, there's a parallel to comic book collecting and it boils down to this:
You can spend a lot of time chasing after the latest hotness, but in the end, your ability to craft professional-grade software depends on the investment you've made in understanding the fundamentals and your enjoyment of the current state-of-the-art technology.
That's it. Pretty simple.
And now I'm going to run the risk of complicating it by trying to explain it.
First of all, I did not say not to learn the new shiny hotness. Just like with comic book collecting, there will be new technologies that catch your interest, either because they are directly applicable to your work or just because. Go for it. Learn it. Learning new things expands your horizons and gives you new insights into your craft.
But, just as there isn't enough time to read every comic book published each month,  we are long past the days when we can learn every new technology that comes out. You can certainly read about them and play with them at a superficial level, but there isn't enough time in the day to be an expert at everything. And if you try, you won't have the time to learn any of them well. Better to pick the ones that you need for your current projects or that you truly enjoy and learn them thoroughly.
And it's a tough choice, because there are all kinds of cool things out there, ranging from the Internet of Things (IoT) on the edge of our networks to Big Data distributed through our data centers, from iOS and Android native apps to highly available Web services fronted by Angular, React or any number of other web frameworks, from noSQL databases to Raspberry Pi's, and so much more. Oh, and let's not forget the drones!
And while you are learning the cool things, make sure you understand the fundamentals, the things that don't change based on which technology you are using at the moment.
Here's a list of some things I think every commercial software developer should know, agile or not, in no particular order.
  • Conversational UML: The Unified Modeling Language is a standard way to express object-oriented ideas. I've been in too many design discussions with developers who don't know UML who struggle to express their ideas to others. And even if they do, the artifacts they leave behind, e.g. whiteboard drawings, are indecipherable to anyone that comes late to the discussion. With UML, anyone can walk up to a diagram and understand its intent. Now, I'm not suggesting spending huge amounts of time learning all the nuances of UML. That's why I call this conversational UML. Just like any language, you need to know enough to express yourself. The UML is there to facilitate the conversations, not to replace it.

  • Design Patterns: The book "Design Patterns: Elements of Reusable Object-Oriented Software" by Gamma, Helm, Johnson, and Vlissides introduced the concept of design patterns to the world. It is still required reading today. Perhaps one of the single most productive learning experiences I have had as a software professional was absorbing this book over a weekend. If you've written object oriented software, you will recognize these patterns in your own work. Understanding them and being able to name them allows you to write better, more expressive software that can concisely convey the intent of your design through the use of these patterns and their names.

  • The Open-Closed Principle, Dependency Inversion Principle and Dependency injection: OK. This isn't just one thing but they are all related. The Open-Closed principal, which says that you should design things that are open to extension but closed to modification, sounds impossible to achieve but is quite simple if you understand dependency inversion, that the outside system should define the dependencies of an entity not the other way around, which can be implemented with dependency injection. In other words, when you build a class, function, etc., it should not have knowledge of the outside world. Anything external that it needs to function properly should be given to it. I acknowledge that this is very much an object-oriented concept. As I am learning more about functional languages, I'm curious to see if there is an analogous concept.

  • Version Control with Branching and Merging Strategies: Every developer that works in collaboration with others needs a basic understanding of how to use a version control system to manage their code base. Modern tools like Git provide great power in terms of the ability to easily create diverging branches of code and subsequently merge them back together. But the tool by itself doesn't make managing code easier. In fact, it can help you to quickly make quite a mess of things. Understanding concepts like a stable master branch strategy, an unstable master branch strategy and Branch By Abstraction and how these are different from branching as code promotion strategies can make your life much easier.

  • Automated Testing: Automated testing comes in many flavors from low-level unit testing up to large scalability testing, from functional testing to user acceptance testing, from performance testing to integration testing. And these can be done in a variety of ways from after-the-fact testing to the more agile test-driven and behavior-driven development. Knowing how to build robust tests, and knowing which tests not to write, can mean the difference between stable, well constructed software and a real mess of missed deadlines and broken promises.

  • Build Systems: Having the ability to build your software in a single, well-defined, well-controlled environment avoids the "it works on my computer" syndrome. Build systems like Jenkins and TFS not only compile your code but can also deploy and run all of your automated tests. Having a reliable build system is an important step in creating a successful devOps strategy.

  • Deployment Strategies: Having a well thought out deployment strategy for your software is important to ensure its stability in production. I've seen too many companies build their software in place on their production systems. Since they've bootstrapped their way, they have no repeatable process to stand up another system. When their product is successful and they need to replicate it, e.g. for scaling to support more users, they have no mechanism to do this. Understanding how to deploy your software means you can set up multiple identical, or nearly-identical, development, QA and production environments at any time.

  • Memory Management: While many modern languages don't explicitly require you to manage memory directly, it's still important to understand how your software allocates and frees memory. If you haven't learned a lower-level language like C that requires you to allocate and deallocate memory explicitly, then you run the risk of being too cavalier in your trust of garbage collectors and other automated memory management solutions. Any system can have memory leaks if you don't understand what they are doing under the hood.

  • Performance Tuning and Testing: Every developer needs to understand when and how to tune performance. Prematurely optimize and you run the risk of creating unnecessarily poor designs. Don't think about optimization at all and your software may die by a death of a thousand cuts. That is, too many slow implementations scattered throughout your code making it impossible to optimize. Of course, agile practices can help alleviate that issue.

  • Debugging: I'm not just talking about how to use a debugger. I am talking about general techniques to track down defects in your software. Knowing how to debug your code is important for multiple reasons. First, it means that you can track down defects quickly. Second, it informs you on how to best instrument your code to help track down problems while supporting the production application.

  • User-Centered Design: This is a slightly different beast from the topics above. Everyone on a software team, not just the UX person or the developers, should understand the importance, benefits and practices of user-centered design.  I once worked on a large multi-tiered application where one of the developers said, totally seriously, "Let's build the server first and then see what it can do." Umm, no.
Final Thoughts...
So there you have it. Superman, science and software all wrapped up with a neat little bow. I didn't really tell you how to be a software superhero nor did I provide you with an exhaustive list of skills that modern software developers need. But I hope I at least got you thinking about your own skills and how they line up to the list above.

About the Author
Dr. Mark Balbes serves as Vice President, Architecture at WWT Asynchrony Labs, and leads multiple Agile projects for Government and Fortune 500 companies. He received his Ph.D. in Nuclear Physics from Duke University in 1992, then continued his research in nuclear astrophysics at Ohio State University. Dr. Balbes has worked in the industrial sector since 1995 applying his scientific expertise to the disciplines of software development. He has led teams as small as a few software developers to as large as a multi-national Engineering department with development centers in the U.S., Canada, and India. Whether serving as product manager, chief scientist, or chief architect, he provides both technical and thought leadership around Agile development, Agile architecture, and Agile project management principles.
Source: https://adtmag.com/articles/2016/06/14/software-superhero.aspx

Wednesday, January 6, 2016

Vietnam ICT market

A report about the Vietnam ICT market, published by the Canadian Trade Commissioner Service in March 2012, describes Vietnam as an attractive software outsourcing destination for Canadian companies.  TMA Solutions is listed in the report as one of the largest Vietnam IT outsourcing providers.
Many indexes have shown the Vietnam ICT industry’s dramatic development over recent years.  The improvement is a result of better infrastructure, availability of devices and telecommunications, as well as human resources. While the low-cost advantage is well understood by Canadian companies,  the report also notes that the quality of human resources is high among Vietnam offshore software company. Vietnam software developers are intelligent, good at math, and hard-working.
Two other main reasons discussed in the report that make Vietnam IT outsourcing companies attractive are the strategic location of Vietnam in Southeast Asia and its population size.  These factors have helped attract global ICT and electronics manufacturing giants such as Samsung, Foxconn, Intel, NEC, Canon, and Hitachi, and many others.
One recommendation for Canadian companies who are setting up outsourcing projects with Vietnam IT outsourcing companies is to plan on sending a few talented staff to go overseas for training.  This is a common practice and helps create a more successful project.  Upon returning home, the individuals work with staff to develop their skills and pass on the knowledge 
In such a fast-growing ICT market, TMA Solutions is listed as one of largest Vietnam offshore software companies, with competencies being built around three pillars: telecom, business applications and mobile applications development. With over 18 years of experience in R&D software outsourcing, TMA has been able to win contracts from international customers such as Avaya, IBM, Nortel, Alcatel-Lucent, NTT, Toshiba, Telus, and NEC. The company has offices in Canada, Europe, US, Japan, and Australia, and has been witnessing an impressive growth rate of over 50% through years of operation.




Source: http://www.enterprisecanadanetwork.ca/_uploads/resources/The-Information-and-Communications-Technology-Market-in-Vietnam.pdf

Monday, September 22, 2014

3 Reasons to Use HTML5 for Mobile App Development

While Android and iOS top the charts in the mobile app-building business, other native application-building operating systems like Blackberry, Windows and others are also making stringent efforts get their markets up. On the other side, there are constant innovations and predictions regarding new methodologies that come into the market. HTML5 is one of the most powerful and feature-rich tools to develop mobile as well as web applications. Along with the existence of native mobile application tools that enable mobile app developers to inculcate all the amendments in it, there are cross-platform tools as well that can prove advantageous in many other ways for development.



HTML5 is the main domain for most of the cross platform application development tools such as Apache Cordova, Rhodes and many others. The key to success of the HTML5 web application tool is the amount of flexibility that it gives the developer in all cases including UI, development, using scripts, and others. Some important reasons behind the adaptability of using HTML5 in mobile applications development are mentioned below.

1. Inclusion of Robust Features
With various domains of applications available in the market, it is important for developers to stick to a single utility with multiple features. HTML5 depicts the perfect platform to launch something that could amend multiple things in rapid duration, keeping the center of attraction intact. Also there have been effective apps with enhanced utility launched in the Chrome store that has given an extensive hike to HTML5 development among the mobile apps development companies. The root cause behind this is the migration of web apps and cross-linking user interfaces that can be produced in spite of giving add-on features within the application.

2. Deployment Made Much Easier
Although there are more extensive scripts and codes that are utilized in native methods of making mobile applications, there are several practices regarding the deployment on the respective app store. But for HTML5 apps, the process goes much smoother since there are fractional parts to be installed which can be easily deployed using open-source JavaScript tools while launching the app. Hosting an application is far more profitable if your app gets renowned popularity on an app store. On the other hand, native apps have various policies and terms-conditions to be followed before going live. The Chrome store has many mobile and web-based applications that are HTML5-based.

3. Easily Available Manpower
For any kind of app development, you need a front-line team of developers who can lead you through successful completion of the project. HTML5 is among the most popular tools that is used by many programmers. So, your company should be able to find HTML5 programmers easily. On the other hand, Android and iPhone developers are hard to find, and even if they are available, there is constant demand of high remunerations. So HTML5 also becomes cost-effective from a hiring point of view.

As you can see, HTML5 mobile application development is a great way to monetize your app and maximize profits. Therefore, mobile apps development companies need to focus on this new tool as a part of their business and get into the groove of HTML5 development to make a mark in a highly competitive market.

Source: http://tech.co/3-reasons-use-html5-mobile-app-development-2014-05

Tuesday, July 29, 2014

Android Application Development TMA Competencies

If you need to develop an Android application, finding an offshore Android application development company can be a great idea; you can save money while also having an expert team with a lot of experience creating apps develop your Android application. There are many great Android application development companies that firms can look to for high quality custom Android application development.  With all of the available options, it can be hard to determine which Android application development company would be ideal for your particular needs.  Here is a list of questions that you should ask to help narrow down your list of potential Android application development companies.
1.    Does this Android application development company offer the services I am looking for?
TMA Solutions offers high quality Android application development services at a competitive price.  Our services include custom application development, porting apps to new platforms, testing, and consulting so we can help you no matter which stage of the process you are in.
2.    Does this Android application development company have the skills and tools necessary to do the job?
TMA’s technical skills in this area include Java, Android SDK, Android NDK, Facebook API, Twitter API, City Search API, Google Search Location API, JSON, XML, kSOAP2, Restful Web services, and data encryption and decryption.
3.    Does this Android application development company have proven success with similar projects?
TMA’s Android development team is comprised of 120 developers who have experience working on hundreds of successful Android apps in industries including finance, retail, healthcare, hospitality, and more.  Some of our successful projects include Visual Yellow Pages, Visual Menu, Mobile Media, Mobile Sales, Hotel Application, SMS Remote Control, Mobile Dictionary, Mobile Collaboration, AutoSMS, SMS SecretBox, Epub3 reader, Visual Shop, and Mobile Furniture.
Contact us today to learn more about our successful Android application development experiences and how we can help you.
Source: TMA solutions

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