Tech Guides

686 cybersecurity breaches were reported in the first three months of 2018 alone, with unauthorized intrusion accounting for 38.9% of incidents. And with high-profile data breaches dominating headlines, it’s clear that while modern, complex software architecture might be more adaptable and data-intensive than ever, securing that software is proving a real challenge. Penetration testing (or pentesting) is a vital component within the cybersecurity toolkit. In theory, it should be at the forefront of any robust security strategy. But it isn’t as simple as just rolling something out with a few emails and new software - it demands people with great skills, as well a culture where stress testing and hacking your own system is viewed as a necessity, not an optional extra. This is where artificial intelligence comes in - the automation that you can achieve through artificial intelligence could well help make pentesting much easier to do consistently and at scale. In turn, this would help organizations tackle both issues of skills and culture, and get serious about their cybersecurity strategies. But before we dive deeper into artificial intelligence and pentesting, let’s take a look at where we are now, and the shortcomings of established pentesting methods. The shortcomings of established methods of pentesting Typically, pentesting is carried out in 5 stages: Source: Incapsula Every one of these stages, when carried out by humans, opens up the chance of error. Yes, software is important, but contextual awareness and decisions are required.. This process, then, provides plenty of opportunities for error. From misinterpreting data - like thinking a system is secure, when actually it isn’t - to taking care of evidence and thoroughly and clearly recording the results of pentests, even the most experienced pentester will get things wrong. But even if you don’t make any mistakes, this whole process is hard to do well at scale. It requires a significant amount of time and energy to test a piece of software, which, given the pace of change created by modern processes, makes it much harder to maintain the levels of rigor you ultimately want from pentesting. This is where artificial intelligence comes in. The pentesting areas that artificial intelligence can impact Let’s dive into the different stages of pentesting that AI can impact. #1 Reconnaissance Stage The most important stage in pentesting is the Reconnaissance or information gathering stage. As rightly said by many in cybersecurity, "The more information gathered, the higher the likelihood of success." Therefore, a significant amount of time should be spent obtaining as much information as possible about the target. Using AI to automate this stage would provide accurate results as well as save a lot of time invested. Using a combination of Natural Language Processing, Computer Vision, and Artificial Intelligence, experts can identify a wide variety of details that can be used to build a profile of the company, its employees, the security posture, and even the software/hardware components of the network and computers. #2 Scanning Stage Comprehensive coverage is needed In the scanning phase. Manually scanning through thousands if systems in an organization is not ideal. NNor is it ideal to interpret the results returned by scanning tools. AI can be used to tweak the code of the scanning tools to scan systems as well as interpret the results of the scan. It can help save pentesters time and help in the overall efficiency of the pentesting process. AI can focus on test management and the creation of test cases automatically that will check if a particular program can be tagged having security flaw. They can also be used to check how a target system responds to an intrusion. #3 Gaining and Maintaining access stage Gaining access phase involves taking control of one or more network devices in order to either extract data from the target, or to use that device to then launch attacks on other targets. Once a system is scanned for vulnerabilities, the pentesters need to ensure that the system does not have any loopholes that attackers can exploit to get into the network devices. They need to check that the network devices are safely protected with strong passwords and other necessary credentials. AI-based algorithms can try out different combinations of passwords to check if the system is susceptible for a break-in. The algorithms can be trained to observe user data, look for trends or patterns to make inferences about possible passwords used. Maintaining access focuses on establishing other entry points to the target. This phase is expected to trigger mechanisms, to ensure that the penetration tester’s security when accessing the network. AI-based algorithms should be run at equal intervals to time to guarantee that the primary path to the device is closed. The algorithms should be able to discover backdoors, new administrator accounts, encrypted channels, new network access channels, and so on. #4 Covering Tracks And Reporting The last stage tests whether an attacker can actually remove all traces of his attack on the system. Evidence is most often stored in user logs, existing access channels, and in error messages caused by the infiltration process. AI-powered tools can assist in the discovery of hidden backdoors and multiple access points that haven't been left open on the target network; All of these findings should be automatically stored in a report with a proper timeline associated with every attack done. A great example of a tool that efficiently performs all these stages of pentesting is CloudSEK’s X-Vigil. This tool leverages AI to extract data, derive analysis and discover vulnerabilities in time to protect an organization from data breach. Manual vs automated vs AI-enabled pentesting Now that you have gone through the shortcomings of manual pen testing and the advantages of AI-based pentesting, let’s do a quick side-by-side comparison to understand the difference between the two.   Manual Testing Automated Testing AI enabled pentesting Manual testing is not accurate at all times due to human error This is more likely to return false positives AI enabled pentesting is accurate as compared to automated testing Manual testing is time-consuming and takes up human resources.   Automated testing is executed by software tools, so it is significantly faster than a manual approach.   AI enabled testing does not consume much time. The algorithms can be deployed for thousands of systems at a single instance. Investment is required for human resources.   Investment is required for testing tools. AI will save the investment for human resources in pentesting. Rather, the same employees can be used to perform less repetitive and more efficient tasks Manual testing is only practical when the test cases are run once or twice, and frequent repetition is not required..   Automated testing is practical when tools find test vulnerabilities out of programmable bounds AI-based pentesting is practical in organizations with thousands of systems that need to be tested at once to save time and resources.   AI-based pentesting tools Pentoma is an AI-powered penetration testing solution that allows software developers to conduct smart hacking attacks and efficiently pinpoint security vulnerabilities in web apps and servers. It identifies holes in web application security before hackers do, helping prevent any potential security damages. Pentoma analyzes web-based applications and servers to find unknown security risks.In Pentoma, with each hacking attempt, machine learning algorithms incorporate new vulnerability discoveries, thus continuously improving and expanding threat detection capability. Wallarm Security Testing is another AI based testing tool that discovers network assets, scans for common vulnerabilities, and monitors application responses for abnormal patterns. It discovers application-specific vulnerabilities via Automated Threat Verification. The content of a blocked malicious request is used to create a sanitized test with the same attack vector to see how the application or its copy in a sandbox would respond. With such AI based pentesting tools, pentesters can focus on the development process itself, confident that applications are secured against the latest hacking and reverse engineering attempts, thereby helping to streamline a product’s time to market. Perhaps it is the increase in the number of costly data breaches or the continually expanding attack and proliferation of sensitive data and the attempt to secure them with increasingly complex security technologies that businesses lack in-house expertise to properly manage. Whatever be the reason, more organizations are waking up to the fact that if vulnerabilities are not caught in time can be catastrophic for the business. These weaknesses, which can range from poorly coded web applications, to unpatched databases to exploitable passwords to an uneducated user population, can enable sophisticated adversaries to run amok across your business.  It would be interesting to see the growth of AI in this field to overcome all the aforementioned shortcomings. 5 ways artificial intelligence is upgrading software engineering Intelligent Edge Analytics: 7 ways machine learning is driving edge computing adoption in 2018 8 ways Artificial Intelligence can improve DevOps

Virtual Reality is evolving at a staggering rate. Some of the humankind’s most exciting tools and technologies are coming to the Virtual reality Space. One such technology which is taking over the VR world and making it more powerful is the VR haptics technology. VR Haptics technology offers an extra dimension to the VR world by letting users feel the virtual environment via the sense of touch, in addition to visual and aural perception. It makes you feel truly immersive in the artificial world. Imagine yourself in a desert seeing the sand and feeling it glide under your feet as you walk. It uses external devices like Gloves, Shoes, Joysticks, etc, via which users can receive feedback in the form of vibrations from these computer applications. This feedback provides physical sensations in the hand or other parts of the body. It also provides a realistic simulation of the movements and behaviors, similar to those realized in the real world. VR Haptics: a growing domain The VR haptics technology is growing beyond creating vibrations in game controllers. Now, in the near future, you might able to cuddle a dog and feel it licking your face in the VR world. This speaks volumes about the pace at which the haptic technology is growing. One famous example which discusses modern VR is the popular sci-fi novel “Ready Player One”. It illustrates the possibilities of haptic technology in the future. The novel explores the journey of a guy as he sets foot into a virtual reality simulator (OASIS). He uses a headset and a pair of gloves to maneuver around the virtual world. Apart from the gloves, a lot of future concept products are also covered in the novel which makes the illusion of immersion easier to picture, such as towers emitting smells in the VR world and Wind/Temperature generators that mimic real-life. Haptics came about just as head mounted displays (HMD) came to light in the 2010s. HMDs allowed people to see the virtual reality while haptic feedback gave people the opportunity to experience the virtual world and to act within it. Texture, temperature, pressure, taste, smell and other non-visual sensory inputs became real in VR. Apart from virtual reality games and apps, Haptics feedback is used widely in personal computers, mobile devices, robots, and more. But, in this article, we’ll stick to the use of haptic technology or haptic feedback in the VR space. Usually, most VR users use Touch Controllers for haptic feedback. But, recently, a lot of third-party companies are coming out with products such as gloves for systems like the Oculus Rift & HTC Vive. Here is a list of recent developments in the haptic technology for the VR world. Super affordable VR Haptic gloves by Plexus Most of the currently available options in the VR haptics field are somewhat pricey but earlier this month, Plexus announced their new product, a VR haptic and sensor glove. https://vimeo.com/276517370 Source: Plexus Key features Plexus VR haptics gloves offer a fully modular tracking solution which is capable of tracking up to 0.01 degrees of precision. These gloves are capable of individual finger tracking as well as tracking each joint on the finger, thereby, offering higher precision in the VR world. It is compatible with the HTC Vive, Oculus Rift as well as Windows Mixed Reality devices. The VR haptic gloves also come with additional adapter plates. The development kit version of the Plexus haptic gloves, priced at $249 per glove pair, can be pre-ordered on the official Plexus Website. The company will begin shipping in August 2018 but at the moment, shipping is only available to USA, Europe, Canada and Australia. Kaaya Tech’s full body tracking HoloSuit Kaaya came out with a motion capture (MoCap) suit called HoloSuit, last month, which offers motion capture as well as haptic feedback. HoloSuit is the world’s first affordable, wireless, easy to use, bi-directional, full body motion capture suit. User’s entire body movement data is captured by Holosuit and it uses haptic feedback to send information back to the user. https://www.youtube.com/watch?v=SEQsDR32gII&t=122s  Source: HoloSuit It can be used in various areas such as sports, healthcare, education, entertainment or industrial operations. Key Features The HoloSuit consists of 36 embedded sensors in the pro version and 26 embedded sensors in the less complex version. Embedded sensors carry out all the work of capturing body motion which is necessary for world-scale tracking. It also consists of 9 haptic feedback devices, and 6 embedded firing buttons ( buttons that govern specific tasks such as saving the game, pausing, etc ) which are dispersed across both arms, legs, and all the ten fingers. It delivers data wirelessly either through Wifi or Bluetooth LE to a VR setup by using Unity or a Wi-Fi SDK. The HoloSuit doesn’t come with an external camera tracking option. It supports all the major platforms such as Windows, macOS, iOS, and Android devices. A complete HoloSuit is quite expensive and starts at a regular price of $999. Jacket and Jersey are priced at $499, jersey or track pants for $399, and a pair of gloves are available for $799. HoloSuit Pro is priced at $1,599. Shipping for the full body VR haptic HoloSuit will start this November. Disney’s VR Haptic “Force Jacket” Disney came out with their VR haptic jacket, namely, “Force Jacket” back in April. It provides users with precisely directed force along with a high-frequency vibration which is felt against the user’s upper body in sync with the visual medium. The prototype is made out of a converted life jacket and is provided with 26 airbags. https://www.youtube.com/watch?v=5BOFHEow608   Source: DisneyResearchHub The Force Jacket is created by engineers at Disney Research, MIT and Carnegie Mellon University. Key Features The Haptic Jacket uses an air compressor and a vacuum pump. These air compartments in the jacket can be inflated to exert a force on the user’s body relative to force sensitive resistors. 26 air compartments are activated using microcontrollers for either pressure or vibrotactile feedback or both. Controllers are used to activating the solenoid valves which are connected to the vacuum. There are certain Jacket inflation parameters like speed, force, and duration which are specified using the haptic effects editor. The jacket makes use of the motion interface to sequentially inflate the compartments for simulating motion across the body. Each airbag within the haptic jacket can be influenced to mimic sensations such as being hit in the chest by a snowball, getting tapped on the shoulder, lime dripping on their back, getting punched in the side, and a snake coiling its body around the user. The jacket is mainly to be used in the entertainment and gaming industry and is not available for the consumer market. But, it seems to have great potential in the future for other applications as well. VR gloves by Haptx Haptx announced a pair of VR gloves back in November of last year. The gloves use micro-pneumatics technology for detailed haptics and force feedback (the ability to restrict your fingers’ movement to simulate holding objects) in the fingers. https://www.youtube.com/watch?v=2C2_kbjtjRU Source: HaptX Key Features It features technology that enables it to provide 100 points of tactile displacement feedback. It offers up to five pounds of resistance per finger. It also comes with sub-millimeter precision motion tracking The glove uses SDK of HaptX’s design, which is created by using Unreal Engine’s physics system. This tells the glove when and where it needs to apply haptic effects as well as when and how to engage the force feedback. No information on pricing or worldwide availability has been released by the company yet. But, it is rumored to launch the VR gloves for the consumer market sometime later this year. Apart from these products, there are other minor advancements that keep happening in the VR haptics space. For example, Heather Culbertson, Assistant Professor of USC's computer department, recently created a haptic armband which is capable of mimicking the sensation of a human touch. VR aims to provide you with an environment where you feel truly immersive and where you can feel the objects as in the real world. These products are bringing the VR world a step closer to achieve richer levels of immersive experiences. Gone are the days when haptic feedback was limited to just vibrating controllers and joysticks. As the technology advances, the whole new world of VR haptic devices is here to make your VR experience as seamlessly immersive as possible. In fact, some people even believe that without Haptics, VR is nothing but a picture and a sound. Game developers say Virtual Reality is here to stay CTA announces its first AR/VR Standard terminology Top 7 modern Virtual Reality hardware systems  

The use of AI (Artificial Intelligence) technology in commercial aviation has brought some significant changes in the ways flights are being operated today. World’s leading airliner service providers are now using AI tools and technologies to deliver a more personalized traveling experience to their customers. From building AI-powered airport kiosks to using it for automating airline operations and security checking, AI will play even more critical roles in the aviation industry. Engineers have found AI can help the aviation industry with machine vision, machine learning, robotics, and natural language processing. Artificial intelligence has been found to be highly potent and various researches have shown how the use of artificial intelligence can bring significant changes in aviation. Few airlines now use artificial intelligence for predictive analytics, pattern recognition, auto scheduling, targeted advertising, and customer feedback analysis showing promising results for better flight experience. A recent report shows that aviation professionals are thinking to use artificial intelligence to monitor pilot voices for a hassle-free flying experience of the passengers. This technology is to bring huge changes in the world of aviation. Identification of the Passengers There’s no need to explain how modern inventions are contributing towards the betterment of mankind and AI can help in air transportation in numerous ways. Check-in before boarding is a vital task for an airline and they can simply take the help of artificial intelligence to do it easily, the same technology can be also used for identifying the passengers as well. American airline company Delta Airlines took the initiative in 2017. Their online check-in via Delta mobile app and ticketing kiosks have shown promising results and nowadays you can see many airlines taking similar features to the whole new level. The Transportation Security Administration of the United States has introduced new AI technology to identify potential threats at the John F. Kennedy, Los Angeles International Airport and Phoenix airports. Likewise, Hartsfield-Jackson Airport is planning to launch America’s first biometric terminal. Once installed, “the AI technology will make the process of passenger identification fast and easy for officials. Security scanners, biometric identification”, and machine learning are some of the AI technologies that will make a number of jobs easy for us. In this way, AI helps us predict disruption in airline services. Baggage Screening Baggage screening is another tedious but important task that needs to be done at the airport. However, AI has simplified the process of baggage screening. The American airlines once conducted a competition on app development on artificial intelligence and Team Avatar became the winner of the competition for making an app that would allow the users to determine the size of their baggage at the airport. Osaka Airport in Japan is planning to install the Syntech ONE 200, which is an AI technology developed to screen baggage for multiple passenger lanes. Such tools will not only automate the process of baggage screening but also help authorities detect illegal items effectively. Syntech ONE 200is compatible with the X-ray security system and it increases the probability of identification of potential threats. Assisting Customers AI can be used to assist customers in the airport and it can help a company reduce its operational costs and labor costs at the same time. Airlines companies are now using AI technologies to help their customers to resolve issues quickly by getting accurate information on future flights trips on their internet-enabled devices. More than 52% of airlines companies across the world have planned to install AI-based tools to improve their customer service functions in the next five years. Artificial Intelligence can answer various common questions of the customers, assisting them for check-in requests, the status of the flight and more. Nowadays artificial intelligence is also used in air cargo for different purposes such as revenue management, safety, and maintenance and it has shown impressive results till date. Maintenance Prediction Airlines companies are planning to implement AI technology to predict potential failures of maintenance on aircraft. Leading aircraft manufacturer Airbus is taking measures to improve the reliability of aircraft maintenance. They are using Skywise, a cloud-based data storing system. It helps the fleet to collect and record a huge amount of real-time data. The use of AI in the predictive maintenance analytics will pave the way for a systematic approach on how and when the aircraft maintenance should be done.  Nowadays you can see how top-rated airlines use artificial intelligence to make the process of maintenance easy and improve the user experience at the same time. Pitfalls of using AI in Aviation Despite being considered as a future of the aviation industry,  AI has some pitfalls. For instance, it takes time for implementation and it cannot be used as an ideal tool for customer service. The recent incident of Ethiopian Airlines Boeing 737 was an eye-opener for us and it clearly represents the drawback of AI technology in the aviation sector. The Boeing 737 crashed a few minutes after it took off from the capital of Ethiopia. The failure of the MCAC system was the key reasons behind the fatal accident. Also, AI is quite expensive; for example, if an airline company is planning to deploy a chatbot, it will have to invest more than $15,000. Thus, it would be a hard thing for small companies to invest for the same and this could create a barrier between small and big airlines in the future. As the market is becoming highly competitive, big airlines will conquer the market and the small airlines might face an existential threat due to this reason.   Conclusion The use of artificial intelligence in aviation has made many tasks easy for airlines and airport authorities across the world. From identifying passengers to screening the bags and providing fast and efficient customer care solutions. Unlike the software industry, the risks of real life harms are exponentially higher in the aviation industry. While other industries have started using this technology long back, the adoption of AI in aviation has been one of caution, and rightly so. As the aviation industry embraces the benefits of artificial intelligence and machine learning, it must also invest in putting in place checks and balances to identify, reduce and eliminate harmful consequences of AI, whether intended or otherwise.  As Silicon Valley reels in ethical dilemmas, the aviation industry will do well to learn from Silicon Valley while making a transition to a smart future. The aviation industry known for its rigorous safety measures and processes may, in fact, have a thing or two to teach Silicon Valley when it comes to designing, adopting and deploying AI systems into live systems that have high-risk profiles. Author Bio Maria Brown is Content Writer, Blogger and maintaining Social Media Optimization for 21Twelve Interactive. She believes in sharing her solid knowledge base with a focus on entrepreneurship and business. You can find her on Twitter.

As we all know, patterns are a kind of simplified and smarter solution for a repetitive concern or recurring challenge in any field of importance. In the field of software engineering, there are primarily many designs, integration, and architecture patterns. In this article, we will cover the need for software patterns and describe the most prominent and dominant software architecture patterns. This article is an excerpt from Architectural Patterns by Pethuru Raj, Anupama Raman, and Harihara Subramanian. Why software patterns? There is a bevy of noteworthy transformations happening in the IT space, especially in software engineering. The complexity of recent software solutions is continuously going up due to the continued evolution of the business expectations. With complex software, not only does the software development activity become very difficult, but also the software maintenance and enhancement tasks become tedious and time-consuming. Software patterns come as a soothing factor for software architects, developers, and operators. Types of software patterns Several newer types of patterns are emerging in order to cater to different demands. This section throws some light on these. An architecture pattern expresses a fundamental structural organization or schema for complex systems. It provides a set of predefined subsystems, specifies their unique responsibilities, and includes the decision-enabling rules and guidelines for organizing the relationships between them. The architecture pattern for a software system illustrates the macro-level structure for the whole software solution. A design pattern provides a scheme for refining the subsystems or components of a system, or the relationships between them. It describes a commonly recurring structure of communicating components that solves a general design problem within a particular context. The design pattern for a software system prescribes the ways and means of building the software components. There are other patterns, too. The dawn of the big data era mandates for distributed computing. The monolithic and massive nature of enterprise-scale applications demands microservices-centric applications. Here, application services need to be found and integrated in order to give an integrated result and view. Thus, there are integration-enabled patterns. Similarly, there are patterns for simplifying software deployment and delivery. Other complex actions are being addressed through the smart leverage of simple as well as composite patterns. Software architecture patterns Let's look at some of the prominent and dominant software architecture patterns. Object-oriented architecture (OOA) Objects are the fundamental and foundational building blocks for all kinds of software applications. Therefore, the object-oriented architectural style has become the dominant one for producing object-oriented software applications. Ultimately, a software system is viewed as a dynamic collection of cooperating objects, instead of a set of routines or procedural instructions. We know that there are proven object-oriented programming methods and enabling languages, such as C++, Java, and so on. The properties of inheritance, polymorphism, encapsulation, and composition being provided by OOA come in handy in producing highly modular (highly cohesive and loosely coupled), usable and reusable software applications. The object-oriented style is suitable if we want to encapsulate logic and data together in reusable components. Also, the complex business logic that requires abstraction and dynamic behavior can effectively use this OOA. Component-based assembly (CBD) architecture Monolithic and massive applications can be partitioned into multiple interactive and smaller components. When components are found, bound, and composed, we get the full-fledged software applications.  CBA does not focus on issues such as communication protocols and shared states. Components are reusable, replaceable, substitutable, extensible, independent, and so on. Design patterns such as the dependency injection (DI) pattern or the service locator pattern can be used to manage dependencies between components and promote loose coupling and reuse. Such patterns are often used to build composite applications that combine and reuse components across multiple applications. Aspect-oriented programming (AOP) aspects are another popular application building block. By deft maneuvering of this unit of development, different applications can be built and deployed. The AOP style aims to increase modularity by allowing the separation of cross-cutting concerns. AOP includes programming methods and tools that support the modularization of concerns at the level of the source code. Agent-oriented software engineering (AOSE) is a programming paradigm where the construction of the software is centered on the concept of software agents. In contrast to the proven object-oriented programming, which has objects (providing methods with variable parameters) at its core, agent-oriented programming has externally specified agents with interfaces and messaging capabilities at its core. They can be thought of as abstractions of objects. Exchanged messages are interpreted by receiving agents, in a way specific to its class of agents. Domain-driven design (DDD) architecture Domain-driven design is an object-oriented approach to designing software based on the business domain, its elements and behaviors, and the relationships between them. It aims to enable software systems that are a correct realization of the underlying business domain by defining a domain model expressed in the language of business domain experts. The domain model can be viewed as a framework from which solutions can then be readied and rationalized. DDD is good if we have a complex domain and we wish to improve communication and understanding within the development team. DDD can also be an ideal approach if we have large and complex enterprise data scenarios that are difficult to manage using the existing techniques. Client/server architecture This pattern segregates the system into two main applications, where the client makes requests to the server. In many cases, the server is a database with application logic represented as stored procedures. This pattern helps to design distributed systems that involve a client system and a server system and a connecting network. The main benefits of the client/server architecture pattern are: Higher security: All data gets stored on the server, which generally offers a greater control of security than client machines. Centralized data access: Because data is stored only on the server, access and updates to the data are far easier to administer than in other architectural styles. Ease of maintenance: The server system can be a single machine or a cluster of multiple machines. The server application and the database can be made to run on a single machine or replicated across multiple machines to ensure easy scalability and high availability. However, the traditional two-tier client/server architecture pattern has numerous disadvantages. Firstly, the tendency of keeping both application and data on a server can negatively impact system extensibility and scalability. The server can be a single point of failure. The reliability is the main worry here. To address these issues, the client-server architecture has evolved into the more general three-tier (or N-tier) architecture. This multi-tier architecture not only surmounts the issues just mentioned but also brings forth a set of new benefits. Multi-tier distributed computing architecture The two-tier architecture is neither flexible nor extensible. Hence, multi-tier distributed computing architecture has attracted a lot of attention. The application components can be deployed in multiple machines (these can be co-located and geographically distributed). Application components can be integrated through messages or remote procedure calls (RPCs), remote method invocations (RMIs), common object request broker architecture (CORBA), enterprise Java beans (EJBs), and so on. The distributed deployment of application services ensures high availability, scalability, manageability, and so on. Web, cloud, mobile, and other customer-facing applications are deployed using this architecture. Thus, based on the business requirements and the application complexity, IT teams can choose the simple two-tier client/server architecture or the advanced N-tier distributed architecture to deploy their applications. These patterns are for simplifying the deployment and delivery of software applications to their subscribers and users. Layered/tiered architecture This pattern is an improvement over the client/server architecture pattern. This is the most commonly used architectural pattern. Typically, an enterprise software application comprises three or more layers: presentation/user interface layer, business logic layer, and data persistence layer. The presentation layer is primarily usded for user interface applications (thick clients) or web browsers (thin clients). With the fast proliferation of mobile devices, mobile browsers are also being attached to the presentation layer. Such tiered segregation comes in handy in managing and maintaining each layer accordingly. The power of plug-in and play gets realized with this approach. Additional layers can be fit in as needed. There are model view controller (MVC) pattern-compliant frameworks hugely simplifying enterprise-grade and web-scale applications. MVC is a web application architecture pattern. The main advantage of the layered architecture is the separation of concerns. That is, each layer can focus solely on its role and responsibility. The layered and tiered pattern makes the application: Maintainable Testable Easy to assign specific and separate roles Easy to update and enhance layers separately This architecture pattern is good for developing web-scale, production-grade, and cloud-hosted applications quickly and in a risk-free fashion. When there are business and technology changes, this layered architecture comes in handy in embedding newer things in order to meet varying business requirements. Event-driven architecture (EDA) The world is eventually becoming event-driven. That is, applications have to be sensitive and responsive proactively, pre-emptively, and precisely. Whenever there is an event happening, applications have to receive the event information and plunge into the necessary activities immediately. The request and reply notion paves the way for the fire and forgets tenet. The communication becomes asynchronous. There is no need for the participating applications to be available online all the time. EDA is typically based on an asynchronous message-driven communication model to propagate information throughout an enterprise. It supports a more natural alignment with an organization's operational model by describing business activities as series of events. EDA does not bind functionally disparate systems and teams into the same centralized management model. EDA ultimately leads to highly decoupled systems. The common issues being introduced by system dependencies are getting eliminated through the adoption of the proven and potential EDA. We have seen various forms of events used in different areas. There are business and technical events. Systems update their status and condition emitting events to be captured and subjected to a variety of investigations in order to precisely understand the prevailing situations. The submission of web forms and clicking on some hypertexts generate events to be captured. Incremental database synchronization mechanisms, RFID readings, email messages, short message service (SMS), instant messaging, and so on are events not to be taken lightly. There are event processing engines, message-oriented middleware (MoM) solutions such as message queues and brokers to collect and stock event data and messages. Millions of events can be collected, parsed, and delivered through multiple topics through these MoM solutions. As event sources/producers publish notifications, event receivers can choose to listen to or filter out specific events and make proactive decisions in real-time on what to do next. EDA style is built on the fundamental aspects of event notifications to facilitate immediate information dissemination and reactive business process execution. In an EDA environment, information can be propagated to all the services and applications in real-time. The EDA pattern enables highly reactive enterprise applications. Real-time analytics is the new normal with the surging popularity of the EDA pattern. Service-oriented architecture (SOA) With the arrival of service paradigms, software packages and libraries are being developed as a collection of services. Services are capable of running independently of the underlying technology. Also, services can be implemented using any programming and script languages. Services are self-defined, autonomous, and interoperable, publicly discoverable, assessable, accessible, reusable, and compostable. Services interact with one another through messaging. There are service providers/developers and consumers/clients. Every service has two parts: the interface and the implementation. The interface is the single point of contact for requesting services. Interfaces give the required separation between services. All kinds of deficiencies and differences of service implementation get hidden by the service interface. Precisely speaking, SOA enables application functionality to be provided as a set of services, and the creation of personal as well as professional applications that make use of software services. In short, SOA is for service-enablement and service-based integration of monolithic and massive applications. The complexity of enterprise process/application integration gets moderated through the smart leverage of the service paradigm. To summarize, we detailed the prominent and dominant software architecture patterns and how they are used for producing and running any kind of enterprise-class and production-grade software applications. To know more about patterns associated with object-oriented, component-based, client-server, and cloud architectures, grab the book Architectural Patterns. Why we need Design Patterns? Implementing 5 Common Design Patterns in JavaScript (ES8) An Introduction to Node.js Design Patterns

Machine learning is a useful and effective tool to have when it comes to building prediction models or to build a useful data structure from an avalanche of data. Many ML algorithms are in use today for a variety of real-world use cases. Given a sample dataset, a machine learning model can give predictions with only certain accuracy, which largely depends on the quality of the training data fed to it. Is there a way to increase the prediction accuracy by somehow involving humans in the process? The answer is yes, and the solution is called as ‘Interactive Machine Learning’. Why we need interactive machine learning As we already discussed above, a model can give predictions only as good as the quality of the training data fed to it. If the quality of the training data is not good enough, the model might: Take more time to learn and then give accurate predictions Quality of predictions will be very poor This challenge can be overcome by involving humans in the machine learning process. By incorporating human feedback in the model training process, it can be trained faster and more efficiently to give more accurate predictions. In the widely adopted machine learning approaches, including supervised and unsupervised learning or even active learning for that matter, there is no way to include human feedback in the training process to improve the accuracy of predictions. In case of supervised learning, for example, the data is already pre-labelled and is used without any actual inputs from the human during the training process. For this reason alone, the concept of interactive machine learning is seen by many machine learning and AI experts as a breakthrough. How interactive machine learning works Machine Learning Researchers Teng Lee, James Johnson and Steve Cheng have suggested a novel way to include human inputs to improve the performance and predictions of the machine learning model. It has been called as the ‘Transparent Boosting Tree’ algorithm, which is a very interesting approach to combine the advantages of machine learning and human inputs in the final decision making process. The Transparent Boosting Tree, or TBT in short, is an algorithm that would visualize the model and the prediction details of each step in the machine learning process to the user, take his/her feedback, and incorporate it into the learning process. The ML model is in charge of updating the assigned weights to the inputs, and filtering the information shown to the user for his/her feedback. Once the feedback is received, it can be incorporated by the ML model as a part of the learning process, thus improving it. A basic flowchart of the interactive machine learning process is as shown: Interactive Machine Learning More in-depth information on how interactive machine learning works can be found in their paper. What can Interactive machine learning do for businesses With the rising popularity and applications of AI across all industry verticals, humans may have a key role to play in the learning process of an algorithm, apart from just coding it. While observing the algorithm’s own outputs or evaluations in the form of visualizations or plain predictions, humans can suggest way to to improve that prediction by giving feedback in the form of inputs such as labels, corrections or rankings. This helps the models in two ways: Increases the prediction accuracy Time taken for the algorithm to learn is shortened considerably Both the advantages can be invaluable to businesses, as they look to incorporate AI and machine learning in their processes, and look for faster and more accurate predictions. Interactive Machine Learning is still in its nascent stage and we can expect more developments in the domain to surface in the coming days. Once production-ready, it will undoubtedly be a game-changer. Read more Active Learning: An approach to training machine learning models efficiently Anatomy of an automated machine learning algorithm (AutoML) How machine learning as a service is transforming cloud

For those of us that manage others, effectiveness is largely driven by the skills and motivation of those that report to us. So whether you are a CIO, IT division leader, or a front-line manager, you need to spend the time to assess the currents skills, abilities and career aspirations of your staff and help them put in place the plans that can support their development. And yet, you need to do this in such a way that still supports the overall near-term objectives of the organization, and properly balances the need for professional development against the day-to-day needs of the organization. There are certifications for competence in many different products. Having such certifications is very valuable and gives one a sense of the skill-set of an individual. But how do you assess someone as a journeyman programmer, tester or systems engineer, or perhaps as a master in one’s chosen discipline? This evaluation is overly subjective and places too much emphasis on “book knowledge” rather than practical application of that knowledge to develop new, innovative solutions or approaches that the organization truly needs. In other words, how do you assess the knowledge, skills and abilities (KSAs) of a person to perform their job role? This assessment problem is two-fold: For a specific IT discipline, you need a comprehensive framework by which to understand the types of skills and knowledge you should have each level — from novice to expert. For each discipline, you also need a way to accurately assess the current level ability of your technical staff members to create the baseline by which you can develop their skills to move to higher levels of proficiency. This not only helps the individual develop a realistic and achievable plan, but also gives you insights into where you have significant skills gaps in your organization. Skills Framework for the Information Age (SFIA) In 2003, a non-profit organization was founded called the Skills Framework for the Information Age (SFIA), which provides a comprehensive framework of skills in IT technologies and disciplines based on a broad industry “body of knowledge.” SFIA currently covers 97 professional skills required by professionals in roles involving information and communications technology. These skills are organized into six categories, as follows: Strategy and Architecture Change and Transformation Development and Implementation Delivery and Operation Skills and Quality Relationships and Engagement Each of the skills are described at one or more of SFIA’s seven levels of attainment — from a novice to expert. Find out more about this framework here. Although the framework helps define your needed competencies, it doesn’t tell you if your workers have the skills that match them. Building your own effective framework In order to accurately assess the current ability level of your technical staff members is to create the baseline from which you can develop their skills to higher levels of proficiency. So, the best way to progress would be by identifying the goals of the team or org and then building your own framework. So, how do we proceed? List the roles within your team To start with you need a list of the role types within your team. This isn’t the same thing as having a listing of every position on your org chart. You want to simplify the process by grouping together like roles. List the skills needed for each role Now that you’ve created a list of role types, the next step is to list the skills needed for each of these roles. What do the skills look like? They could be behavioral like “Listens to customer needs carefully to determine requirements” or they could be more technical like this sample list of engineering skills: Writing quality code Design skills Writing optimal code Programming patterns Once you have this list, it’s a valuable resource in itself. Create a survey It’s ideal if you can find out all of the relevant skills a person has, not just those for their current role. To do this, create a survey that makes it easy for your people to respond. This essentially means you need to keep it short and not ask the same question twice. To achieve this, the survey should group together each of the major role types. Use the list you created in step 2 as your starting point for this. Let’s say you have an engineering group within your organization. It may have a number of different role types within it, but there’s probably common skills across many of them. For example, many of the role types may require people to be skilled at “Programming.” Rather than listing skills more than once under each relevant role type, list them once under a common group heading. Survey your workforce With the survey designed, you are now ready to ask your workforce to respond to it. The size of your team and the number of roles will determine how you go about doing this. It’s a good practice to communicate to survey participants to explain why you are asking for their response and what will happen with the information. Analyze the data You can now reap the rewards of your skills audit process. You can analyze: The skill gaps in specific roles Skill gaps within teams or organization groups Potential successors for certain roles The number of people who have critical skills Future skill requirements This assessment not only helps employees create realistic and achievable individual development plans, but also gives you insight into where you have significant skills gaps in your team or in your organization. Hari Vignesh Jayapalan is a Google Certified Android app developer, IDF Certified UI & UX Professional, street magician, fitness freak, technology enthusiast, and wannabe entrepreneur. He can be found on Twitter @HariofSpades.

Machine Learning introduces a huge potential to reduce costs and generate new revenue in an enterprise. Application of machine learning effectively helps in solving practical problems smartly within an organization. Machine learning automates tasks that would otherwise need to be performed by a live agent. It has made drastic improvements in the past few years, but many a time, a machine needs the assistance of a human to complete its task. This is why it is necessary for organizations to learn best practices in machine learning which you will learn in this article today. This article is an excerpt from a book written by Chiheb Chebbi titled Mastering Machine Learning for Penetration Testing Feature engineering in machine learning Feature engineering and feature selection are essential to every modern data science product, especially machine learning based projects. According to research, over 50% of the time spent building the model is occupied by cleaning, processing, and selecting the data required to train the model. It is your responsibility to design, represent, and select the features. Most machine learning algorithms cannot work on raw data. They are not smart enough to do so. Thus, feature engineering is needed, to transform data in its raw status into data that can be understood and consumed by algorithms. Professor Andrew Ng once said: "Coming up with features is difficult, time-consuming, requires expert knowledge. 'Applied machine learning' is basically feature engineering." Feature engineering is a process in the data preparation phase, according to the cross-industry standard process for data mining: The term Feature Engineering itself is not a formally defined term. It groups together all of the tasks for designing features to build intelligent systems. It plays an important role in the system. If you check data science competitions, I bet you have noticed that the competitors all use the same algorithms, but the winners perform the best feature engineering. If you want to enhance your data science and machine learning skills, I highly recommend that you visit and compete at www.kaggle.com: When searching for machine learning resources, you will face many different terminologies. To avoid any confusion, we need to distinguish between feature selection and feature engineering. Feature engineering transforms raw data into suitable features, while feature selection extracts necessary features from the engineered data. Featuring engineering is selecting the subset of all features, without including redundant or irrelevant features. Machine learning best practices Feature engineering enhances the performance of our machine learning system. We discuss some tips and best practices to build robust intelligent systems. Let's explore some of the best practices in the different aspects of machine learning projects. Information security datasets Data is a vital part of every machine learning model. To train models, we need to feed them datasets. While reading the earlier chapters, you will have noticed that to build an accurate and efficient machine learning model, you need a huge volume of data, even after cleaning data. Big companies with great amounts of available data use their internal datasets to build models, but small organizations, like startups, often struggle to acquire such a volume of data. International rules and regulations are making the mission harder because data privacy is an important aspect of information security. Every modern business must protect its users' data. To solve this problem, many institutions and organizations are delivering publicly available datasets, so that others can download them and build their models for educational or commercial use. Some information security datasets are as follows: The Controller Area Network (CAN) dataset for intrusion detection (OTIDS): http://ocslab.hksecurity.net/Dataset/CAN-intrusion-dataset The car-hacking dataset for intrusion detection: http://ocslab.hksecurity.net/Datasets/CAN-intrusion-dataset The web-hacking dataset for cyber criminal profiling: http://ocslab.hksecurity.net/Datasets/web-hacking-profiling The API-based malware detection system (APIMDS) dataset: http://ocslab.hksecurity.net/apimds-dataset The intrusion detection evaluation dataset (CICIDS2017): http://www.unb.ca/cic/datasets/ids-2017.html The Tor-nonTor dataset: http://www.unb.ca/cic/datasets/tor.html The Android adware and general malware dataset: http://www.unb.ca/cic/datasets/android-adware.html Use Project Jupyter The Jupyter Notebook is an open source web application used to create and share coding documents. I highly recommend it, especially for novice data scientists, for many reasons. It will give you the ability to code and visualize output directly. It is great for discovering and playing with data; exploring data is an important step to building machine learning models. Jupyter's official website is http://jupyter.org/: To install it using pip, simply type the following: python -m pip install --upgrade pip python -m pip install jupyter Speed up training with GPUs As you know, even with good feature engineering, training in machine learning is computationally expensive. The quickest way to train learning algorithms is to use graphics processing units (GPUs). Generally, though not in all cases, using GPUs is a wise decision for training models. In order to overcome CPU performance bottlenecks, the gather/scatter GPU architecture is best, performing parallel operations to speed up computing. TensorFlow supports the use of GPUs to train machine learning models. Hence, the devices are represented as strings; following is an example: "/device:GPU:0" : Your device GPU "/device:GPU:1" : 2nd GPU device on your Machine To use a GPU device in TensorFlow, you can add the following line: with tf.device('/device:GPU:0'): <What to Do Here> You can use a single GPU or multiple GPUs. Don't forget to install the CUDA toolkit, by using the following commands: Wget "http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/cuda-repo-ubuntu1604_8.0.44-1_amd64.deb" sudo dpkg -i cuda-repo-ubuntu1604_8.0.44-1_amd64.deb sudo apt-get update sudo apt-get install cuda Install cuDNN as follows: sudo tar -xvf cudnn-8.0-linux-x64-v5.1.tgz -C /usr/local export PATH=/usr/local/cuda/bin:$PATH export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64" export CUDA_HOME=/usr/local/cuda Selecting models and learning curves To improve the performance of machine learning models, there are many hyper parameters to adjust. The more data that is used, the more errors that can happen. To work on these parameters, there is a method called GridSearchCV. It performs searches on predefined parameter values, through iterations. GridSearchCV uses the score() function, by default. To use it in scikit-learn, import it by using this line: from sklearn.grid_search import GridSearchCV Learning curves are used to understand the performance of a machine learning model. To use a learning curve in scikit-learn, import it to your Python project, as follows: from sklearn.learning_curve import learning_curve Machine learning architecture In the real world, data scientists do not find data to be as clean as the publicly available datasets. Real world data is stored by different means, and the data itself is shaped in different categories. Thus, machine learning practitioners need to build their own systems and pipelines to achieve their goals and train the models. A typical machine learning project respects the following architecture: Coding Good coding skills are very important to data science and machine learning. In addition to using effective linear algebra, statistics, and mathematics, data scientists should learn how to code properly. As a data scientist, you can choose from many programming languages, like Python, R, Java, and so on. Respecting coding's best practices is very helpful and highly recommended. Writing elegant, clean, and understandable code can be done through these tips: Comments are very important to understandable code. So, don't forget to comment your code, all of the time. Choose the right names for variables, functions, methods, packages, and modules. Use four spaces per indentation level. Structure your repository properly. Follow common style guidelines. If you use Python, you can follow this great aphorism, called the The Zen of Python, written by the legend, Tim Peters: "Beautiful is better than ugly. Explicit is better than implicit. Simple is better than complex. Complex is better than complicated. Flat is better than nested. Sparse is better than dense. Readability counts. Special cases aren't special enough to break the rules. Although practicality beats purity. Errors should never pass silently. Unless explicitly silenced. In the face of ambiguity, refuse the temptation to guess. There should be one-- and preferably only one --obvious way to do it. Although that way may not be obvious at first unless you're Dutch. Now is better than never. Although never is often better than *right* now. If the implementation is hard to explain, it's a bad idea. If the implementation is easy to explain, it may be a good idea. Namespaces are one honking great idea -- let's do more of those!" Data handling Good data handling leads to successfully building machine learning projects. After loading a dataset, please make sure that all of the data has loaded properly, and that the reading process is performing correctly. After performing any operation on the dataset, check over the resulting dataset. Business contexts An intelligent system is highly connected to business aspects because, after all, you are using data science and machine learning to solve a business issue or to build a commercial product, or for getting useful insights from the data that is acquired, to make good decisions. Identifying the right problems and asking the right questions are important when building your machine learning model, in order to solve business issues. In this tutorial, we had a look at somes tips and best practices to build intelligent systems using Machine Learning. To become a master at penetration testing using machine learning with Python,  check out this book  Mastering Machine Learning for Penetration Testing Why TensorFlow always tops machine learning and artificial intelligence tool surveys Intelligent Edge Analytics: 7 ways machine learning is driving edge computing adoption in 2018 Tackle trolls with Machine Learning bots: Filtering out inappropriate content just got easy

In the process of rebuilding its Big Data platform, Uber created an open-source Spark library named Hadoop Upserts anD Incremental (Hudi). This library permits users to perform operations such as update, insert, and delete on existing Parquet data in Hadoop. It also allows data users to incrementally pull only the changed data, which significantly improves query efficiency. It is horizontally scalable, can be used from any Spark job, and the best part is that it only relies on HDFS to operate. Why is Hudi introduced? Uber studied its current data content, data access patterns, and user-specific requirements to identify problem areas. This research revealed the following four limitations: Scalability limitation in HDFS Many companies who use HDFS to scale their Big Data infrastructure face this issue. Storing large numbers of small files can affect the performance significantly as HDFS is bottlenecked by its NameNode capacity. This becomes a major issue when the data size grows above 50-100 petabytes. Need for faster data delivery in Hadoop Since Uber operates in real time, there was a need for providing services the latest data. It was important to make the data delivery much faster, as the 24-hour data latency was way too slow for many of their use cases. No direct support for updates and deletes for existing data Uber used snapshot-based ingestion of data, which means a fresh copy of source data was ingested every 24 hours. As Uber requires the latest data for its business, there was a need for a solution which supports update and delete operations for existing data. However, since their Big Data is stored in HDFS and Parquet, direct support for update operations on existing data is not available. Faster ETL and modeling ETL and modeling jobs were also snapshot-based, requiring their platform to rebuild derived tables in every run. ETL jobs also needed to become incremental to reduce data latency. How Hudi solves the aforementioned limitations? The following diagram shows Uber's Big Data platform after the incorporation of Hudi: Source: Uber Regardless of whether the data updates are new records added to recent date partitions or updates to older data, Hudi allows users to pass on their latest checkpoint timestamp and retrieve all the records that have been updated since. This data retrieval happens without running an expensive query that scans the entire source table. Using this library Uber has moved to an incremental ingestion model leaving behind the snapshot-based ingestion. As a result, the data latency was reduced from 24 hrs to less than one hour. To know about Hudi in detail, check out Uber’s official announcement. How can Artificial Intelligence support your Big Data architecture? Big data as a service (BDaaS) solutions: comparing IaaS, PaaS and SaaS Uber’s Marmaray, an Open Source Data Ingestion and Dispersal Framework for Apache Hadoop

Artificial Intelligence is making it easier for people to do things every day. You can schedule your day, search for photos of loved ones, type emails on the go, or get things done with the virtual assistant. AI also provides innovative ways of tackling existing problems, from healthcare to advancing scientific discovery. According to Gartner’s Top 10 Strategic Technology Trends for 2018, the next few years will see every app, application, and service incorporating AI at some level. With major companies like Google, Amazon, IBM investing in AI and incorporating AI in their products, this statement, instead of a prediction is becoming a fact. Apple’s IPhone X comes with a Facial Recognition System, Samsung’s Bixby, Amazon’s Alexa, Google’s Google Assistant, and the recently launched Android Pie. Android Pie learns your preferences based on your usage patterns and gets better over time. It even provides you a breakdown of the time you spend on your phone. AI comes with endless possibilities, things that we used to dream of are now becoming a part of our day to day life. So, I have listed here, in no particular order, some of those innovative applications: Microsoft’s Seeing AI - Eye for the visually impaired Source: Microsoft Seeing AI is a perfect example of how technology is improving our lives. It is an intelligent camera app that uses computer vision to audibly help blind and visually impaired people to know about their surroundings. It comes with functionalities like reading out short text and documents for you, giving you description about a person, identifies currencies, colour, handwriting, light and even images in other apps using the device's camera. A data scientist named Anirudh Koul started this project (called Deep Vision earlier) to help his grandfather who was gradually losing his vision. Two breakthroughs by the Microsoft researchers facilitated him to further his idea: vision-to-language and image classification. To make the app this advance and real-time, they used the idea of making servers communicate with Microsoft Cognitive Services. This app brings in four technologies together to provide users with an array of functionalities: OCR, barcode scanner, facial recognition, and scene recognition. Check out this YouTube tutorial to understand how it works. Download App Store Ada - Healthcare in your hand Source: Digital Health Ada, with a very simple and conversational UI, helps you understand what could be wrong if you or someone you care about is not feeling well. Just like any doctor’s appointment, it starts with your basic details, then does an assessment, in which it asks several personalized questions related to the symptoms, and then gives a report. The report consists of a summary, possible causes, and less-likely causes. It also allows you to share the report as a PDF. After training over several years using real world cases, Ada has become a handy health advisor. Its platform is powered by a sophisticated Artificial Intelligence engine combined with large medical knowledge base covering many thousands of conditions, symptoms and findings. In every medical assessment, Ada takes all of a patient’s information into account, including past medical history, symptoms, risk factors and more. Using machine learning and multiple closed feedback loops, Ada becomes more intelligent. Download App Store Google Play Store Plume Air Report - An air pollution monitor Source: Plume Labs Blog Industrialization and urbanization definitely comes with their side effects, the main being air pollution. It has become inevitable to keep yourself safe from the pollution, but now at least you can be aware of the air pollution levels in your area. Plume Air Report forecasts how air quality will evolve hour by hour over the next 24 hours similar to weather forecast. You can also easily compare the air quality between cities. It gives you insight on all pollutants (PM2.5, PM10, O3, NO2), with absolute concentration levels and your local air quality scale. It uses machine learning and atmospheric sciences to deliver real-time and hourly forecast air quality data. First, latest pollution levels is collected from over 12,000 monitoring stations and 80 public agencies around the world and then filtered for errors. Local atmospheric data (wind, temperature, atmosphere, etc.) is sourced to track their influence on pollution levels in your city. A team of data scientists analyzes local specifics such as geographical features and human activities. Finally, AI algorithms and atmospheric models are developed that turn this giant amount of data into hourly forecasts. Download App Store Google Play Store Aura - Mindfulness meets AI Source: Popular Science In this fast life, slow down a little and give yourself a time out with Aura. Aura is a new kind of mindfulness app that learns about you and simplifies your learning through guided meditations. It helps in reducing stress and increases positivity through 3-minute meditations, personalized by Artificial Intelligence. Aura is an intelligent app that leverages machine learning to give you a unique experience. After every exercise, you can rate your experience and Aura will learn how to provide more tailored meditations according to your needs. You can even track your mood and learn your mood patterns. Download App Store Google Play Store Replika - An emotive chatbot as a friend for life Source: Medium Want to be friends with someone who is always there to listen to you, talk to you, and never judges you? Then Replika is for you! It helps you make a real connection with an unreal friend. The idea of building Replika came from a very tragic background. The founder of the software company, Luka, Eugenia Kudya, lost her best friend in an accident in November 2015. She used to go through their messenger texts to bring back their memories. This is how she got this idea to develop a chatbot making it learn from the sample texts sent by her best friend. In her own words, “Most of the companies try to build an app that talks, but we tried to build an app that could listen well”. The chatbot uses neural network facilitating more natural one-on-one conversation with its user, and over time, learn how to speak like them. The source code is freely available for developers under the name CakeChat. It comes with a pre-trained model that you can use as is to run a chatbot that maintains a conversation in a certain emotional state. You can also build a variety of other conversational agents by using your own dataset, for example, persona-based model, emotional chatting machine, topic-centric model. To know more about the background and evolution of Replika, check out this amazing YouTube video. Download App Store Google Play Store Google Assistant - Your personal Google Source: Google Assistant When talking of AI-powered apps, voice assistants probably come first in your mind. Google Assistant makes your life easier and helps in organizing your day better. You can manage your little tasks, plan your day, enjoy entertainment, and get answers. It can also sync to your other devices including Google Home, smart TVs, laptops, and more. To give users smart assistance, Google Assistant relies on Artificial Intelligence technologies such as natural language processing, natural language understanding, and machine learning to understand what the user is saying, and to make suggestions or act on that language input. Download App Store Google Play Store Hound - Say it, Get it Source: Android Apps In an array of virtual assistants to choose from, Hound understands your voice commands better. You do not need to give “search query” like commands and can have a more natural conversation. Hound can be used for variety of tasks, some of them are: search, discover, and play music, set alarms, timers, and reminders, call, text, navigate hands-free, get the weather forecast. Hound’s speed and accuracy comes from their powerful Houndify platform. This platform combines Speech Recognition and Natural Language Understanding into a single step, which is called Speech-to-Meaning. Download App Store Google Play Store Picai - An app that picks filters for your pics, keeping you looking your best always Source: Google Play Store Picai with the help of Artificial Intelligence, recommends picture-perfect filters by analyzing the scene. It automatically analyzes the scene and with the help of object recognition detects the type of the object, for example, a plant, a girl, etc. It then uses a proprietary deep learning model to recommend two optimum filters from 100+ filters. What makes this app stand out is the split-screen filter selection, which makes the filter selection easier for the users. When using this app be warned of the picture quality and app size (76 MB), but it is definitely worth trying! Download Google Play Store Microsoft Pix - The pro photographer Source: MSPoweuser Named one of the 50 Best Apps of the Year by Time Magazine, Microsoft Pix helps you take better photos without the extra effort! It solves the problem of “not living in the moment”. It comes with some amazing features like, hyperlapse, live images Microsoft Pix Comix, artistic styles to transform your photos, smart settings that automatically checks scene and lighting between each shutter tap, and updates settings between each shot, and more. Microsoft Pix uses Artificial Intelligence to improve the image, such as cropping edges, enhancing color and tone, and sharpening focus. It includes enhanced deep-learning capabilities around image understanding. It captures a burst of 10 frames with each shutter click and uses AI to select three best shots. Before the remaining photos are deleted, it uses data from the entire burst to remove noise. These best, enhanced images are ready in about a second. The app also detects whether your eyes are open or not using the facial recognition technology. Download App Store ELSA - Your machine learning English teacher Source: TechCrunch ELSA (English Language Speech Assistant)  helps you in learning English and bettering your pronunciation every day. It provides you a curriculum tailored just, regular feedback, progress tracking, common phrases used in daily life. You can practice in a relaxed environment and improve your speaking skills to prepare for the TOEFL, IELTS, TOEIC ELSA coaches you in improving your English pronunciations by using speech recognition, deep learning, and Artificial Intelligence. Download App Store Google Play Store Socratic - Homework in a snap Source: Google Play Store Socratic is your new helper, apart from your parents, in completing those complex Math problems. You just need to take a photo of your homework and can get explanations, videos, step-by-step help, instantly. Also, these resources are jargon-free, helping you understand the concepts better. It supports all subjects including Math (Algebra, Calculus, Statistics, Graphing, etc), Science, Chemistry, History, English, Economics, and more. Socratic uses Artificial Intelligence to figure out the concepts you need to learn in order to answer it. For this it combines cutting-edge computer vision technologies, which read questions from images, with machine learning classifiers. These classifiers are built using millions of sample homework questions, to accurately predict which concepts will help you solve your question. Download App Store Google Play Store Recent News - Stay informed Source: Recent News Recent News is an app that will provide you customized news. Some of the features that it comes with to give you the daily dose of news include one-minute news summary with very quick load time, hot news, local news, and personalized recommendations, instantly share news on Facebook, Twitter, and other social networks, and many more. It uses Artificial Intelligence to learn about your interests, suggest relevant articles, and propose topics you might like to follow. So, the more you use it the better it becomes! The app is surely innovative and saves time, but I do wish the developers applied some innovation in the app’s name as well :P Download App Store Google Play Store And that’s the end of my list. People say, “Smartphones and apps are becoming smarter, and we are becoming dumber”. But I would like to say that these apps, with the right usage, empower us to become smarter. Agree? 7 Popular Applications of Artificial Intelligence in Healthcare 5 examples of Artificial Intelligence in Web apps What Should We Watch Tonight? Ask a Robot, says Matt Jones from OVO Mobile [Interview]

Data Visualization is commonly used in the modern world, where most business decisions are taken into consideration by analyzing the data. One of the most significant benefits of data visualization is that it enables us to visually access huge amounts of data in easily understandable visuals. There are many areas where data visualization is being used. Some of the data visualization tools include Tableau, Alteryx, Infogram, ChartBlocks, Datawrapper, Plotly, Visual.ly, etc. Tableau and Alteryx are industry standard tools and have dominated the data analytics market for a few years now and still running strong without any strong competition. In this article, we will understand the core differences between Alteryx tool and Tableau. This will help us in deciding which tool to use for what purposes. Tableau is one of the top-rated tools which helps the analysts to carry out business intelligence and data visualization activities. Using Tableau, the users will be able to generate compelling dashboards and stunning data visualizations. Tableau’s interactive user interface helps users to quickly generate reports where they can drill down the information to a granular level. Alteryx is a powerful tool widely used in data analytics and also provides meaningful insights to the executive level personnel. With the user-friendly interface, the user will be able to extract the data, transform the data, and load the data within the Alteryx tool. Why use Alteryx with Tableau? The use of Alteryx with Tableau is a powerful combination when it comes to getting value-added data decisions. With Alteryx, businesses can manipulate their data and provide input to the Tableau platform, which in return will be able to showcase strong data visualizations. This will help the businesses to take appropriate actions which are backed up with data analysis. Alteryx and Tableau tools are widely used within organizations where the decisions can be taken into consideration based on the insights obtained from data analysis. Talking about data handling, Alteryx is a powerful ETL platform where data can be analyzed in different formats. When it comes to data representation, Tableau is a perfect match. Further, using Tableau the reports can be shared across team members. Nowadays, most of the businesses want to see real-time data and want to understand business trends. The combination of Alteryx and Tableau allows the data analysts to analyze the data, and generate meaningful insights to the users, on-the-fly. Here, data analysis can be executed within the Alteryx tool where the raw data is handled, and then the data representation or visualization is done in Tableau, so both of these tools go hand in hand. Tableau vs Alteryx The table below lists the differences between the tools. Alteryx Tableau This tool is known as a smart data analytics platform. This tool is known for its data visualization capabilities. 2. Can connect with different data sources and can synthesize the raw data. A standard ETL process is possible. 2. Can connect with different data sources and provide data visualization within minutes from the gathered data. 3. Helps in terms of the data analysis 3. Helps in terms of building appealing graphs. 4. The GUI is okay and widely accepted. 4. The GUI is one of the best features where graphs can be easily built by using drag and drop options. 5. Technical knowledge is necessary because it involves in data sources integrations, and also data blending activity. 5. Technical knowledge is not necessary, because all the data will be polished and only the user has to build graphs/visualization. 6.  Once the data blending activity is completed, the users will be able to share the file which can be consumed by Tableau. 6. Once the graphs are prepared, the reports can be easily shared among team members without any hassle. 7. A lot of flexibility while using this tool for data blending activity. 7. Flexibility while using the tool for data visualization. 8. Using this tool, the users will be able to do spatial and predictive analysis 8. Possible by representing the data in an appropriate format. 9.  One of the best tools when it comes to data preparations. 9. Not feasible to prepare the data in Tableau when it is compared to Alteryx. 10. Data representation cannot be done accurately. 10. It is a wonderful tool for data representation. 11. Has one time feeds- Annual fees 11. Has an option to pay monthly as well. 12. Has a drag and drop interface where the user can develop a workflow easily. 12. Has a drag and drop interface where the user will be able to build a visualization in no time. Alteryx and Tableau Integration As discussed earlier, these two tools have their own advantages and disadvantages, but when integrated together, they can do wonders with the data. This integration between Tableau and Alteryx makes the task of visualizing the Alteryx generated answers quite simple. The data is first loaded into the Alteryx tool and is then extracted in the form of .tde files (i.e. Tableau Data Extracted Files). These .tde files will be consumed by Tableau tool to do the data visualization part. On a regular basis, the data extracted file from Alteryx tool (i.e. .tde files) will be generated and will replace the old .tde files. Thus, by integrating Alteryx and Tableau, we can: Cleanse, combine, as well as collect all the data sources that are relevant and enrich them with the help of third-party data - everything in one workflow. Give analytical context to your data by providing predictive, location-based, and deep spatial analytics. Publish your analytic workflows’ results to Tableau for intuitive, rich visualizations that help you in making decisions more quickly. Tableau and Alteryx do not require any advanced skill-set as both tools have simple drag and drop interfaces. You can create a workflow in Alteryx that can process data in a sequential manner. In a similar way, Tableau enables you to build charts by dragging various fields to be utilized, to specified areas. The companies which have a lot of data to analyze, and can spend large amounts of money on analytics, can use these two tools. There doesn’t exist any significant challenges during Tableau, Alteryx integration. Conclusion When Tableau and Alteryx are used together, it is really useful for the businesses so that the senior management can take decisions based on the data insights provided by these tools. These two tools compliment each other and provide high-quality service to businesses. Author Bio Savaram Ravindra is a Senior Content Contributor at Mindmajix.com. His passion lies in writing articles on different niches, which include some of the most innovative and emerging software technologies, digital marketing, businesses, and so on. By being a guest blogger, he helps his company acquire quality traffic to its website and build its domain name and search engine authority. Before devoting his work full time to the writing profession, he was a programmer analyst at Cognizant Technology Solutions. Follow him on LinkedIn and Twitter. How to share insights using Alteryx Server How to do data storytelling well with Tableau [Video] A tale of two tools: Tableau and Power BI  

Okay, you’re probably here because you’ve got just a few months to graduate and the projects section of your resume is blank. Or you’re just an inquisitive little nerd scraping the WWW for ways to crack that dream job. Either way, you’re not alone and there are ten thousand others trying to build a great Data Science portfolio to land them a good job. Look no further, we’ll try our best to help you on how to make a portfolio that catches the recruiter’s eye! David “Trent” Salazar‘s portfolio is a great example of a wholesome one and Sajal Sharma’s, is a good example of how one can display their Data Science Portfolios on a platform like Github. Companies are on the lookout for employees who can add value to the business. To showcase this on your resume effectively, the first step is to understand the different ways in which you can add value. 4 things you need to show in a data science portfolio Data science can be broken down into 4 broad areas: Obtaining insights from data and presenting them to the business leaders Designing an application that directly benefits the customer Designing an application or system that directly benefits other teams in the organisation Sharing expertise on data science with other teams You’ll need to ensure that your portfolio portrays all or at least most of the above, in order to easily make it through a job selection. So let’s see what we can do to make a great portfolio. Demonstrate that you know what you're doing So the idea is to show the recruiter that you’re capable of performing the critical aspects of Data Science, i.e. import a data set, clean the data, extract useful information from the data using various techniques, and finally visualise the findings and communicate them. Apart from the technical skills, there are a few soft skills that are expected as well. For instance, the ability to communicate and collaborate with others, the ability to reason and take the initiative when required. If your project is actually able to communicate these things, you’re in! Stay focused and be specific You might know a lot, but rather than throwing all your skills, projects and knowledge in the employer’s face, it’s always better to be focused on doing something and doing it right. Just as you’d do in your resume, keeping things short and sweet, you can implement this while building your portfolio too. Always remember, the interviewer is looking for specific skills. Research the data science job market Find 5-6 jobs, probably from Linkedin or Indeed, that interest you and go through their descriptions thoroughly. Understand what kind of skills the employer is looking for. For example, it could be classification, machine learning, statistical modeling or regression. Pick up the tools that are required for the job - for example, Python, R, TensorFlow, Hadoop, or whatever might get the job done. If you don’t know how to use that tool, you’ll want to skill-up as you work your way through the projects. Also, identify the kind of data that they would like you to be working on, like text or numerical, etc. Now, once you have this information at hand, start building your project around these skills and tools. Be a problem solver Working on projects that are not actual ‘problems’ that you’re solving, won’t stand out in your portfolio. The closer your projects are to the real-world, the easier it will be for the recruiter to make their decision to choose you. This will also showcase your analytical skills and how you’ve applied data science to solve a prevailing problem. Put at least 3 diverse projects in your data science portfolio A nice way to create a portfolio is to list 3 good projects that are diverse in nature. Here are some interesting projects to get you started on your portfolio: Data Cleaning and wrangling Data Cleaning is one of the most critical tasks that a data scientist performs. By taking a group of diverse data sets, consolidating and making sense of them, you’re giving the recruiter confidence that you know how to prep them for analysis. For example, you can take Twitter or Whatsapp data and clean it for analysis. The process is pretty simple; you first find a “dirty” data set, then spot an interesting angle to approach the data from, clean it up and perform analysis on it, and finally present your findings. Data storytelling Storytelling showcases not only your ability to draw insight from raw data, but it also reveals how well you’re able to convey the insights to others and persuade them. For example, you can use data from the bus system in your country and gather insights to identify which stops incur the most delays. This could be fixed by changing their route. Make sure your analysis is descriptive and your code and logic can be followed. Here’s what you do; first you find a good dataset, then you explore the data and spot correlations in the data. Then you visualize it before you start writing up your narrative. Tackle the data from various angles and pick up the most interesting one. If it’s interesting to you, it will most probably be interesting to anyone else who’s reviewing it. Break down and explain each step in detail, each code snippet, as if you were describing it to a friend. The idea is to teach the reviewer something new as you run through the analysis. End to end data science If you’re more into Machine Learning, or algorithm writing, you should do an end-to-end data science project. The project should be capable of taking in data, processing it and finally learning from it, every step of the way. For example, you can pick up fuel pricing data for your city or maybe stock market data. The data needs to be dynamic and updated regularly. The trick for this one is to keep the code simple so that it’s easy to set up and run. You first need to identify a good topic. Understand here that we will not be working with a single dataset, rather you will need to import and parse all the data and bring it under a single dataset yourself. Next, get the training and test data ready to make predictions. Document your code and other findings and you’re good to go. Prove you have the data science skill set If you want to get that job, you’ve got to have the appropriate tools to get the job done. Here’s a list of some of the most popular tools with a link to the right material for you to skill-up: Data science languages There's a number of key languages in data science that are essential. It might seem obvious, but making sure they're on your resume and demonstrated in your portfolio is incredibly important. Include things like: Python R Java Scala SQL Big Data tools If you're applying for big data roles, demonstrating your experience with the key technologies is a must. It not only proves you have the skills, but also shows that you have an awareness of what tools can be used to build a big data solution or project. You'll need: Hadoop, Spark Hive Machine learning frameworks With machine learning so in demand, if you can prove you've used a number of machine learning frameworks, you've already done a lot to impress. Remember, many organizations won't actually know as much about machine learning as you think. In fact, they might even be hiring you with a view to building out this capability. Remember to include: TensorFlow Caffe2 Keras PyTorch Data visualisation tools Data visualization is a crucial component of any data science project. If you can visualize and communicate data effectively, you're immediately demonstrating you're able to collaborate with others and make your insights accessible and useful to the wider business. Include tools like these in your resume and portfolio:  D3.js Excel chart  Tableau  ggplot2 So there you have it. You know what to do to build a decent data science portfolio. It’s really worth attending competitions and challenges. It will not only help you keep up to data and well oiled with your skills, but also give you a broader picture of what people are actually working on and with what tools they’re able to solve problems.

Security is one of the major concerns while setting up data centers in the cloud. Although firewalls and managed networking components are deployed by most of the organizations for their data centers, they still fear being attacked by intruders. As such, organizations constantly seek tools that can assist them in gauging how vulnerable their network is and how they can secure their applications therein. Many confuse security assessment with penetration testing and also use it interchangeably. However, there is a notable difference between the two. Security assessment is a process of finding out the different vulnerabilities within a system and prioritize them based on severity and business criticality. On the other hand, penetration testing simulates a real-life attack and maps out paths that a real attacker would take to fulfill the attack. You can check out our article, Top 5 penetration testing tools for ethical hackers to know about some of the pentesting tools. Plethora of tools in the market exist and every tool claims to be the best. Here is our top 5 list of tools to secure your organization over the network. Wireshark Wireshark is one of the popular tools for packet analysis. It is open source under GNU General Public License. Wireshark has a user-friendly GUI  and supports Command Line Input (CLI). It is a great debugging tool for developers who wish to develop a network application. It runs on multiple platforms including Windows, Linux, Solaris, NetBSD, and so on. WireShark community also hosts SharkFest, launched in 2008, for WireShark developers and the user communities. The main aim of this conference is to support Wireshark development and to educate current and future generations of computer science and IT professionals on how to use this tool to manage, troubleshoot, diagnose, and secure traditional and modern networks. Some benefits of using this tool include: Wireshark features live real-time traffic analysis and also supports offline analysis. Depending on the platform, one can read live data from Ethernet, PPP/HDLC, USB, IEEE 802.11, Token Ring, and many others. Decryption support for several protocols such as IPsec, ISAKMP, Kerberos, SNMPv3, SSL/TLS, WEP, and WPA/WPA2 Network captured by this tool can be browsed via a GUI, or via the TTY-mode TShark utility. Wireshark also has the most powerful display filters in whole industry It also provides users with Tshark, a network protocol analyzer, used to analyze packets from the hosts without a UI. Nmap Network Mapper, popularly known as Nmap is an open source licensed tool for conducting network discovery and security auditing.  It is also utilized for tasks such as network inventory management, monitoring host or service uptime, and much more. How Nmap works is, it uses raw IP packets in order to find out the available hosts on the network, the services they offer, the OS on which they are operating, the firewall that they are currently using and much more. Nmap is a quick essential to scan large networks and can also be used to scan single hosts. It runs on all major operating system. It also provides official binary packages for Windows, Linux, and Mac OS X. It also includes Zenmap - An advanced security scanner GUI and a results viewer Ncat - This is a tool used for data transfer, redirection, and debugging. Ndiff - A utility tool for comparing scan results Nping - A packet generation and response analysis tool Nmap is traditionally a command-line tool run from a Unix shell or Windows Command prompt. This makes Nmap easy for scripting and allows easy sharing of useful commands within the user community. With this, experts do not have to move through different configuration panels and scattered option fields. Nessus Nessus, a product of the Tenable.io, is one of the popular vulnerability scanners specifically for UNIX systems. This tool remains constantly updated with 70k+ plugins. Nessus is available in both free and paid versions. The paid version costs around  $2,190 per year, whereas the free version, ‘Nessus Home’ offers limited usage and is licensed only for home network usage. Customers choose Nessus because It includes simple steps for policy creation and needs just a few clicks for scanning an entire corporate network. It offers vulnerability scanning at a low total cost of ownership (TCO) product One can carry out a quick and accurate scanning with lower false positives. It also has an embedded scripting language for users to write their own plugins and to understand the existing ones. QualysGuard QualysGuard is a famous SaaS (Software-as-a-Service) vulnerability management tool. It has a comprehensive vulnerability knowledge base, using which it is able to provide continuous protection against the latest worms and security threats. It proactively monitors all the network access points, due to which security managers can invest less time to research, scan, and fix network vulnerabilities. This helps organizations in avoiding network vulnerabilities before they could be exploited. It provides a detailed technical analysis of the threats via powerful and easy-to-read reports. The detailed report includes the security threat, the consequences faced if the vulnerability is exploited, and also a solution that recommends how the vulnerability can be fixed. One can get a summary of the overall security with QualysGuard’s executive dashboard. The dashboard displays a number of new, active, and re-opened vulnerabilities. It also displays a graph which showcases vulnerabilities based on severity level. Get to know more about QualysGuard on its official website. Core Impact Core Impact is widely used as a comprehensive tool to assess and test security vulnerability within any organization. It includes a large database of professional exploits and is regularly updated. It assists in cleanly exploiting one machine and later creating an encrypted tunnel through it to exploit other machines. Core Impact provides a controlled environment to mimic bad attacks. This helps one to secure their network before the occurrence of an actual attack. One interesting feature of Core Impact is that one can fully test their network, irrespective of the length, quickly and efficiently. These are five popular tools network security professionals use for assessing their networks. However, there are many other tools such as Netsparker, OpenVAS, Nikto, and many more for assessing the security of their network. Every security assessment tool is unique in its own way. However, it all boils down to one’s own expertise and the experience they have, and also the kind of project environment it is used in. Top 5 penetration testing tools for ethical hackers Intel’s Spectre variant 4 patch impacts CPU performance Pentest tool in focus: Metasploit

If you think C programming and Unix are unrelated, then you are making a big mistake. Back in the 1970s and 1980s, if the Unix engineers at Bell Labs had decided to use another programming language instead of C to develop a new version of Unix, then we would be talking about that language today. The relationship between the two is simple; Unix is the first operating system that is implemented with a high-level C programming language, got its fame and power from Unix. Of course, our statement about C being a high-level programming language is not true in today’s world. This article is an excerpt from the book Extreme C by Kamran Amini. Kamran teaches you to use C’s power. Apply object-oriented design principles to your procedural C code. You will gain new insight into algorithm design, functions, and structures. You’ll also understand how C works with UNIX, how to implement OO principles in C, and what multiprocessing is. In this article, we are going to look at the history of C programming and Unix. Multics OS and Unix Even before having Unix, we had the Multics OS. It was a joint project launched in 1964 as a cooperative project led by MIT, General Electric, and Bell Labs. Multics OS was a huge success because it could introduce the world to a real working and secure operating system. Multics was installed everywhere from universities to government sites. Fast-forward to 2019, and every operating system today is borrowing some ideas from Multics indirectly through Unix. In 1969, because of the various reasons that we will talk about shortly, some people at Bell Labs, especially the pioneers of Unix, such as Ken Thompson and Dennis Ritchie, gave up on Multics and, subsequently, Bell Labs quit the Multics project. But this was not the end for Bell Labs; they had designed their simpler and more efficient operating system, which was called Unix. It is worthwhile to compare the Multics and Unix operating systems. In the following list, you will see similarities and differences found while comparing Multics and Unix: Both follow the onion architecture as their internal structure. We mean that they both have the same rings in their onion architecture, especially kernel and shell rings. Therefore, programmers could write their own programs on top of the shell ring. Also, Unix and Multics expose a list of utility programs, and there are lots of utility programs such as ls and pwd. In the following sections, we will explain the various rings found in the Unix architecture. Multics needed expensive resources and machines to be able to work. It was not possible to install it on ordinary commodity machines, and that was one of the main drawbacks that let Unix thrive and finally made Multics obsolete after about 30 years. Multics was complex by design. This was the reason behind the frustration of Bell Labs employees and, as we said earlier, the reason why they left the project. But Unix tried to remain simple. In the first version, it was not even multitasking or multi-user! You can read more about Unix and Multics online, and follow the events that happened in that era. Both were successful projects, but Unix has been able to thrive and survive to this day. It is worth sharing that Bell Labs has been working on a new distributed operating system called Plan 9, which is based on the Unix project.   Figure 1-1: Plan 9 from Bell Labs Suffice to say that Unix was a simplification of the ideas and innovations that Multics presented; it was not something new, and so, I can quit talking about Unix and Multics history at this point. So far, there are no traces of C in the history because it has not been invented yet. The first versions of Unix were purely written using assembly language. Only in 1973 was Unix version 4 written using C. Now, we are getting close to discussing C itself, but before that, we must talk about BCPL and B because they have been the gateway to C. About BCPL and B BCPL was created by Martin Richards as a programming language invented for the purpose of writing compilers. The people from Bell Labs were introduced to the language when they were working as part of the Multics project. After quitting the Multics project, Bell Labs first started to write Unix using assembly programming language. That’s because, back then, it was an anti-pattern to develop an operating system using a programming language other than assembly. For instance, it was strange that the people at the Multics project were using PL/1 to develop Multics but, by doing that, they showed that operating systems could be successfully written using a higher-level programming language other than assembly. As a result, Multics became the main inspiration for using another language for developing Unix. The attempt to write operating system modules using a programming language other than assembly remained with Ken Thompson and Dennis Ritchie at Bell Labs. They tried to use BCPL, but it turned out that they needed to apply some modifications to the language to be able to use it in minicomputers such as the DEC PDP-7. These changes led to the B programming language. While we won’t go too deep into the properties of the B language here you can read more about it and the way it was developed at the following links: The B Programming Language  The Development of the C Language Dennis Ritchie authored the latter article himself, and it is a good way to explain the development of the C programming language while still sharing valuable information about B and its characteristics. B also had its shortcomings in terms of being a system programming language. B was typeless, which meant that it was only possible to work with a word (not a byte) in each operation. This made it hard to use the language on machines with a different word length. Therefore, over time, further modifications were made to the language until it led to developing the NB (New B) language, which later derived the structures from the B language. These structures were typeless in B, but they became typed in C. And finally, in 1973, the fourth version of Unix could be developed using C, which still had many assembly codes. In the next section, we talk about the differences between B and C, and why C is a top-notch modern system programming language for writing an operating system. The way to C programming and Unix I do not think we can find anyone better than Dennis Ritchie himself to explain why C was invented after the difficulties met with B. In this section, we’re going to list the causes that prompted Dennis Ritchie, Ken Thompson, and others create a new programming language instead of using B for writing Unix. Limitations of the B programming language: B could only work with words in memory: Every single operation should have been performed in terms of words. Back then, having a programming language that was able to work with bytes was a dream. This was because of the available hardware at the time, which addressed the memory in a word-based scheme. B was typeless: More accurately, B was a single-type language. All variables were from the same type: word. So, if you had a string with 20 characters (21 plus the null character at the end), you had to divide it up by words and store it in more than one variable. For example, if a word was 4 bytes, you would have 6 variables to store 21 characters of the string. Being typeless meant that multiple byte-oriented algorithms, such as string manipulation algorithms, were not efficiently written with B: This was because B was using the memory words not bytes, and they could not be used efficiently to manage multi-byte data types such as integers and characters. B didn’t support floating-point operations: At the time, these operations were becoming increasingly available on the new hardware, but there was no support for that in the B language. Through the availability of machines such as PDP-1, which could address memory on a byte basis, B showed that it could be inefficient in addressing bytes of memory: This became even clearer with B pointers, which could only address the words in the memory, and not the bytes. In other words, for a program wanting to access a specific byte or a byte range in the memory, more computations had to be done to calculate the corresponding word index. The difficulties with B, particularly its slow development and execution on machines that were available at the time, forced Dennis Ritchie to develop a new language. This new language was called NB, or New B at first, but it eventually turned out to be C. This newly developed language, C, tried to cover the difficulties and flaws of B and became a de facto programming language for system development, instead of the assembly language. In less than 10 years, newer versions of Unix were completely written in C, and all newer operating systems that were based on Unix got tied with C and its crucial presence in the system. As you can see, C was not born as an ordinary programming language, but instead, it was designed by having a complete set of requirements in mind. You may consider languages such as Java, Python, and Ruby to be higher-level languages, but they cannot be considered as direct competitors as they are different and serve different purposes. For instance, you cannot write a device driver or a kernel module with Java or Python, and they themselves have been built on top of a layer written in C. Unlike some programming languages, C is standardized by ISO, and if it is required to have a certain feature in the future, then the standard can be modified to support the new feature. To summarize In this article, we began with the relationship between Unix and C. Even in non-Unix operating systems, you see some traces of a similar design to Unix systems. We also looked at the history of C and explained how Unix appeared from Multics OS and how C was derived from the B programming language. The book Extreme C, written by Kamran Amini will help you make the most of C's low-level control, flexibility, and high performance. Is Dark an AWS Lambda challenger? Microsoft mulls replacing C and C++ code with Rust calling it a “modern safer system programming language” with great memory safety features Is Scala 3.0 a new language altogether? Martin Odersky, its designer, says “yes and no”

Facial recognition technology is not new. In fact, it has been around for more than a decade. However, with the recent rise in artificial intelligence and deep learning, facial technology has achieved new heights. In addition to facial detection, modern day facial recognition technology also recognizes faces with high accuracy and in unfavorable conditions. It can also recognize expressions and analyze faces to generate insights about an individual. Deep learning has enabled a power-packed face recognition system, all geared up to achieve widespread adoption. How has deep learning modernised facial recognition Traditional facial recognition algorithms would recognize images and people using distinct facial features (placement of eye, eye color, nose shape etc.) However, they failed in correct identification in cases of different lighting or slight change in the appearance ( beard growth, aging, or pose). In order to develop facial recognition techniques for a dynamic and ever-changing face, deep learning is proving to be a game changer. Deep Neural nets go beyond the approach of manual extraction. These AI based Neural Networks rely on image pixels to analyze features of a particular face. So they scan faces irrespective of the lighting, ageing, pose, or emotions. Deep learning algorithms remember each time they recognize or fail to recognize a problem. Thus, avoiding repeat mistakes and getting better at each attempt. Deep learning algorithms can also be helpful in converting 2D images to 3D. Facial recognition in practice: Facial Recognition Technology in Multimedia Deep learning enabled facial recognition technologies can be used to track audience reaction and measure different levels of emotions. Essentially it can predict how a member of the audience will react to the remaining film. Not only this, it also helps determine what percentage of users will be interested in a particular movie genre. For example, Microsoft’s Azure Emotion,  an emotion API detects emotions by analysing the facial expressions on an image or video content over time. Caltech and Disney have collaborated to develop a neural network which can track facial expressions. Their deep learning based Factorised Variational Autoencoders (FVAEs) analyze facial expressions of audience for about 10 minutes and then predict how their reaction will be for the rest of the film. These techniques help in estimating whether the viewers are giving the expected reactions at the right place. For example, the viewer is not expected to yawn on a comical scene. With this, Disney can also predict the earning potential of a particular movie. It can generate insights that may help producers create compelling movie trailers to maximize the number of footfalls. Smart TVs are also equipped with sophisticated cameras and deep learning algos for facial recognition ability. They can recognize the face of the person watching and automatically show channels and web applications programmed as their favorites. The British broadcasting corporation uses the facial recognition technology, built by CrowdEmotion. By tracking faces of almost 4,500 audience members watching show trailers, they gauge exact customer emotions about a particular programme. This in turn helps them generate insights to showcase successful commercials. Biometrics in Smartphones A large number of smartphones nowadays are instilled with biometric capabilities. Facial recognition in smartphones are not only used as a means of unlocking and authorizing, but also for making secure transactions and payments. In present times, there has been a rise in chips with built-in deep learning ability. These chips are embedded into smartphones. By having a neural net embedded inside the device, crucial face biometric data never leaves the device or sent to the cloud. This in turn improves privacy and reduces latency. Some of the real-world examples include Intel’s Nervana Neural Network Processor, Google’s TPU, Microsoft’s FPGA, and Nvidia’s Tesla V100. Deep learning models, embedded in a smartphone, can construct a mathematical model of the face which is then stored in the database. Using this mathematical face model, smartphones can easily recognize users even as their face ages or when it is obstructed by wearable accessories. Apple has recently launched the iPhone X facial recognition system termed as FaceID. It maps thousands of points on a user’s face using a projector and an infrared camera (which can operate under varied lighting conditions). This map is then passed to a bionic chip embedded in the smart phone. The chip has a neural network which constructs a mathematical model of the user’s face, used for biometric face verification and recognition. Windows Hello is also a facial recognition technology to unlock Windows smart devices equipped with infrared cameras. Qualcomm, a mobile technology organization, is working on a new depth-perception technology. It will include an image signal processor and high-resolution 3D depth-sensing cameras for facial recognition. Face recognition for Travel Facial recognition technologies can smoothen the departure process for a customer by eliminating the need for a boarding pass. A traveller is scanned by cameras installed at various check points, so they don’t have to produce a boarding pass at every step. Emirates is collaborating with Dubai Customs, Police and Airports to use a facial recognition technology solution integrated with the UAE Wallet app. The project is known as Together Initiative, it allows travellers to register and store their biometric facial data at several kiosks placed at the check-in area. This facility helps passengers to avoid presenting their physical documents at every touchpoint. Face recognition can also be used for determining illegal immigration. The technology compares the photos of passengers taken immediately before boarding, with the photos provided in their visa application. Biometric Exit, is an initiative by US government, which uses facial recognition to identify individuals leaving the country. Facial recognition technology can also be used at train stations to reduce the waiting time for  buying a train ticket or going through other security barriers. Bristol Robotics Laboratory has developed a software which uses infrared cameras to identify passengers as they walk onto the train platform. They do not need to carry tickets. Retail and shopping In the area of retail, smart facial recognition technologies can be helpful in fast checkout by keeping a track of each customer as they shop across a store. This smart technology, can also use machine learning and analytics to find trends in the shopper’s purchasing behavior over time and devise personalized recommendations. Facial video analytics and deep learning algorithms can also identify loyal and VIP shoppers from the moving crowd, giving them a privileged VIP experience. Thus, enabling them with more reasons to come back and make repeat purchases. Facial biometrics can also accumulate rich statistics about demographics(age, gender, shopping history) of an individual. Analyzing these statistics can generate insights, which helps organizations develop their products and marketing strategies. FindFace is one such platform that uses sophisticated deep learning technologies to generate meaningful data about the shopper. Its e-facial recognition system can verify faces with almost 99% accuracy. It can also help route the shopper data to a salesperson’s notice for personalized assistance. Facial recognition technology can also be used to make secure payment transactions simply by analysing a person’s face. AliBaba has set up a Smile to Pay face recognition system in KFC's. This system allows customers to make secure payments by merely scanning their face. Facial recognition has emerged as a hot topic of interest and is poised to grow. On the flip side, organizations deploying such technology should incorporate privacy policies as a standard measure. Data collected from such facial recognition software can also be used wrongly for targeting customers with ads, or for other illegal purposes. They should implement a methodical and systematic approach for using facial recognition for the benefit of their customers. This will not only help businesses generate a new source of revenue, but will also usher in a new era of judicial automation.  

Connected physical devices, home automation appliances, and wearable devices are all part of Internet of Things (IoT). All of these have two major things in common that is seamless connectivity and massive data transfer. This also brings with it, plenty of opportunities for massive data breaches and allied cyber security threats. The motive of digital forensics is to identify, collect, analyse, and present digital evidence collected from various mediums in a cybercrime incident. The multiplication of IoT devices and the increased number of cyber security incidents has given birth to IoT forensics. IoT forensics is a branch of digital forensics which deals with IoT-related cybercrimes and includes investigation of connected devices, sensors and the data stored on all possible platforms. If you look at the bigger picture, IoT forensics is a lot more complex, multifaceted and multidisciplinary in approach than traditional forensics. With versatile IoT devices, there is no specific method of IoT forensics that can be broadly used.So identifying valuable sources is a major challenge. The entire investigation will depend on the nature of the connected or smart device in place. For example, evidence could be collected from fixed home automation sensors, or moving automobile sensors, wearable devices or data store on Cloud. When compared to the standard digital forensic techniques, IoT forensics portrays multiple challenges depending on the versatility and complexity of the IoT devices. Following are some challenges that one may face in an investigation: Variance of the IoT devices Proprietary Hardware and Software Data present across multiple devices and platforms Data can be updated, modified, or lost Proprietary jurisdictions for data is stored on cloud or a different geography As such, IoT Forensics requires a multi-faceted approach where evidence can be collected from various sources. We can categorize sources of evidence into three broad groups: Smart devices and sensors; Gadgets present at the crime scene (Smartwatch, home automation appliances, weather control devices, and more) Hardware and Software; the communication link between smart devices and the external world (computers, mobile, IPS, and firewalls) External resources; areas outside the network unders investigation (Cloud, social networks, ISPs and mobile network providers) Once the evidence is successfully collected from an IoT device no matter the file system, operating system, or the platform it is based on, it should be logged and monitored. The main reason behind this is IoT devices data storage are majorly on Cloud due to its scalability and accessibility. There are high possibilities the data on Cloud can be altered which would result to an investigation failure. No doubt Cloud forensics can equally play an important role here but strengthening cyber security best practices should be the ideal motive. With ever evolving IoT devices there will always be a need for unique practice methods and techniques to break through the investigation. Cybercrime keeps evolving and getting bolder by the day. Forensics experts will have to develop skill sets to deal with the variety and complexity of IoT devices to keep up with this evolution. No matter the challenges one faces there is always a unique solution to complex problems. There will always be a need for unique, intelligent, and adaptable techniques to investigate IoT-related crimes and an even greater need for those displaying these capabilities. To learn more on IoT security, you can get you hands on a few of our books; IoT Penetration Testing Cookbook and Practical Internet of Things Security. Why Metadata is so important for IoT Why the Industrial Internet of Things (IIoT) needs Architects 5 reasons to choose AWS IoT Core for your next IoT project