Managing Java Application Performance

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Java-based applications are powering many business critical IT services today. Uses of Java technologies span several domains including healthcare, logistics, finance, insurance, education and many more. In all of these instances, Java technology is the middleware in which the business logic resides. Many production installations also include home-grown application components, running on standard Java application servers such as Oracle WebLogic, IBM WebSphere, SAP NetWeaver, Apache Tomcat and JBoss, to name a few.

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Aberdeen Group

Aberdeen provides intent-based marketing and sales solutions that deliver performance improvements in advertising click through rates and sales pipeline resulting in a measurable ROI. Our intent data is the largest scale, most accurate and highly targeted in the market. We don’t force clients to buy more software; our products plug into clients existing marketing and sales tech stack via data feeds, embedded analytics and apps.

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AI's Impact on Improving Customer Experience

Article | July 14, 2022

To enhance the consumer experience, businesses all over the world are experimenting with artificial intelligenace (AI), machine learning, and advanced analytics. Artificial intelligence (AI) is becoming increasingly popular among marketers and salespeople, and it has become a vital tool for businesses that want to offer their customers a hyper-personalized, outstanding experience. Customer relationship management (CRM) and customer data platform (CDP) software that has been upgraded with AI has made AI accessible to businesses without the exorbitant expenses previously associated with the technology. When AI and machine learning are used in conjunction for collecting and analyzing social, historical, and behavioral data, brands may develop a much more thorough understanding of their customers. In addition, AI can predict client behavior because it continuously learns from the data it analyzes, in contrast to traditional data analytics tools. As a result, businesses may deliver highly pertinent content, boost sales, and enhance the customer experience. Predictive Behavior Analysis and Real-time Decision Making Real-time decisioning is the capacity to act quickly and based on the most up-to-date information available, such as information from a customer's most recent encounter with a company. For instance, Precognitive's Decision-AI uses a combination of AI and machine learning to assess any event in real-time with a response time of less than 200 milliseconds. Precognitive's fraud prevention product includes Decision-AI, which can be implemented using an API on a website. Marketing to customers can be done more successfully by using real-time decisioning. For example, brands may display highly tailored, pertinent content and offer to clients by utilizing AI and real-time decisioning to discover and comprehend a customer's purpose from the data they produce in real-time. By providing deeper insights into what has already happened and what can be done to facilitate a sale through suggestions for related products and accessories, AI and predictive analytics are able to go further than historical data alone. This increases the relevance of the customer experience, increases the likelihood that a sale will be made, and increases the emotional connection that the customer has with a brand.

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SOFTWARE

The Evolution of Quantum Computing and What its Future Beholds

Article | July 13, 2022

The mechanism of quantum computers will be entirely different from anything we humans have ever created or constructed in the past. Quantum computers, like classical computers, are designed to address problems in the real world. They process data in a unique way, though, which makes them a much more effective machine than any computer in use today. Superposition and entanglement, two fundamental ideas in quantum mechanics, could be used to explain what makes quantum computers unique. The goal of quantum computing research is to find a technique to accelerate the execution of lengthy chains of computer instructions. This method of execution would take advantage of a quantum physics event that is frequently observed but does not appear to make much sense when written out. When this fundamental objective of quantum computing is accomplished, and all theorists are confident works in practice, computing will undoubtedly undergo a revolution. Quantum computing promises that it will enable us to address specific issues that current classical computers cannot resolve in a timely manner. While not a cure-all for all computer issues, quantum computing is adequate for most "needle in a haystack" search and optimization issues. Quantum Computing and Its Deployment Only the big hyperscalers and a few hardware vendors offer quantum computer emulators and limited-sized quantum computers as a cloud service. Quantum computers are used for compute-intensive, non-latency-sensitive issues. Quantum computer architectures can't handle massive data sizes yet. In many circumstances, a hybrid quantum-classical computer is used. Quantum computers don't use much electricity to compute but need cryogenic refrigerators to sustain superconducting temperatures. Networking and Quantum Software Stacks Many quantum computing software stacks virtualize the hardware and build a virtual layer of logical qubits. Software stacks provide compilers that transform high-level programming structures into low-level assembly commands that operate on logical qubits. In addition, software stack suppliers are designing domain-specific application-level templates for quantum computing. The software layer hides complexity without affecting quantum computing hardware performance or mobility.

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AI TECH

Language Models: Emerging Types and Why They Matter

Article | July 20, 2022

Language model systems, often known as text understanding and generation systems, are the newest trend in business. However, not every language model is made equal. A few are starting to take center stage, including massive general-purpose models like OpenAI's GPT-3 and models tailored for specific jobs. There is a third type of model at the edge that is intended to run on Internet of Things devices and workstations but is typically very compressed in size and has few functionalities. Large Language Models Large language models, which can reach tens of petabytes in size, are trained on vast volumes of text data. As a result, they rank among the models with the highest number of parameters, where a "parameter" is a value the model can alter on its own as it gains knowledge. The model's parameters, which are made of components learned from prior training data, fundamentally describe the model's aptitude for solving a particular task, like producing text. Fine-tuned Language Models Compared to their massive language model siblings, fine-tuned models are typically smaller. Examples include OpenAI's Codex, a version of GPT-3 that is specifically tailored for programming jobs. Codex is both smaller than OpenAI and more effective at creating and completing strings of computer code, although it still has billions of parameters. The performance of a model, like its capacity to generate protein sequences or respond to queries, can be improved through fine-tuning. Edge Language Models Edge models, which are intentionally small in size, occasionally take the shape of finely tuned models. To work within certain hardware limits, they are occasionally trained from scratch on modest data sets. In any event, edge models provide several advantages that massive language models simply cannot match, notwithstanding their limitations in some areas. The main factor is cost. There are no cloud usage fees with an edge approach that operates locally and offline. As significant, fine-tuned, and edge language models grow in response to new research, they are likely to encounter hurdles on their way to wider use. For example, compared to training a model from the start, fine-tuning requires less data, but fine-tuning still requires a dataset.

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SOFTWARE

Low-code and No-code: A Business' New Best Friend

Article | July 5, 2022

Businesses are starting to integrate artificial intelligence (AI) into their workflow in greater numbers as a result of the growth of digital transformation and developments in machine learning (ML). As a result, platforms that need no coding, as well as their low-code counterparts, are becoming more popular. This development is a step toward computer science's long-term objective of automating manual coding. Low-code/no-code AI platforms will be beneficial to businesses in more data-driven industries like marketing, sales, and finance. AI can assist in a variety of ways, including automating invoicing, evaluating reports, making intelligent suggestions, and anticipating churn rates. How Does an Organization Look at Low-code/No-code as the Future? Developers and other tech-related positions are in high demand, particularly in the fields of AI and data science. Organizations have the chance to close the gap with the aid of citizen data scientists who don't require an AI professional to design unique AI solutions for many scenarios, thanks to low-code and no-code AI technologies. The demand for technological solutions and AI technologies is rising significantly as the technological landscape rapidly changes. AI systems, for example, require complex software that uses a lot of code, a variety of frameworks, and the Internet of Things (IoT). One person's capacity to comprehend every technical detail is strained by the array of complicated technology. Software delivery must be timely, effective, and secure while maintaining high standards. Conclusion Low-code AI solutions offer the speed, ease of use, and adaptability of ready-made software solutions while also drastically reducing the time to market for AI solutions and the cost of recruiting software and computer vision engineers. Organizations are free to construct the architecture, functionality, or pipeline that best suits their project, the sky being the limit. However, creating such unique models may be both costly and time-consuming. Therefore, employing low-code/no-code platforms would apply to particular pipeline actions that would streamline and accelerate the processes.

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Spotlight

Aberdeen Group

Aberdeen provides intent-based marketing and sales solutions that deliver performance improvements in advertising click through rates and sales pipeline resulting in a measurable ROI. Our intent data is the largest scale, most accurate and highly targeted in the market. We don’t force clients to buy more software; our products plug into clients existing marketing and sales tech stack via data feeds, embedded analytics and apps.

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AI TECH

Diffblue's First AI-Powered Automated Java Unit Testing Solution is Now Available for Free to Commercial and Open Source Software Developers

Diffblue | March 23, 2021

Diffblue, the developers of the world's first AI for code solution that automates the writing of unit tests for Java, announced today that its free IntelliJ extension, Diffblue Cover: Community Edition, is now available to use to create unit tests for any of an organization's Java code – both open source and commercial. For business clients that need additional service, indemnification, and the opportunity to write tests for packages, Diffblue also offers a technical edition. Diffblue also has a CLI variant of Diffblue Cover, which is suitable for team collaboration. Diffblue's ground-breaking technology, developed by University of Oxford researchers, is based on reinforcement learning, the same machine learning strategy that fuelled AlphaGo, Alphabet subsidiary DeepMind's software program that dominated the world champion player GO. Diffblue Cover automates the time-consuming process of writing Java unit tests, which can consume up to 20% of a Java developer's time. Diffblue Cover produces Java tests 10X-100X faster than humans, and are also easy to grasp for users, and manages them automatically as the code evolves, even on applications with tens of millions of lines of code. Java, the most common business programming language in the Global 2000, is now supported by Diffblue Cover. Diffblue Cover's technology can be extended to support other popular programming languages like Python, Javascript, and C#. About Diffblue Diffblue is a pioneer in the use of artificial intelligence to automate software development. Diffblue Cover, which was established by University of Oxford academics, uses artificial intelligence to write unit tests that help development teams and companies enhance their code coverage and accuracy while shipping software quicker, more regularly, and with fewer defects. Diffblue is backed by Goldman Sachs and Oxford Sciences Innovation and has clients such as AWS and Goldman Sachs.

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JavaScript Library Introduced XSS Flaw in Google Search

SecurityWeek | April 01, 2019

A change made several months ago in an open-source JavaScript library introduced a cross-site scripting (XSS) vulnerability in Google Search and likely other Google products. Japanese security researcher Masato Kinugawa discovered what appeared to be an XSS vulnerability in Google Search. Such a security hole can pose a serious risk and it could be highly useful to malicious actors for phishing and other types of attacks. According to an analysis conducted by LiveOverflow, the XSS vulnerability was introduced by the use of a library named Closure and its failure to properly sanitize user input. Closure is a broad JavaScript library designed by Google for complex and scalable web applications. The tech giant has made the library open source and still uses it for many of its applications, including Search, Gmail, Maps and Docs. The vulnerability was apparently introduced on September 26, 2018, when someone removed a sanitization mechanism reportedly due to some user interface design issues. It was addressed on February 22, 2019, when the change made in September 2018 was reverted. Google is said to have patched the vulnerability shortly after learning of its existence. Comments posted by developers when the rollback was done confirmed that the issue was related to an HTML sanitizer and that it introduced an XSS flaw in the Google Web Server (GWS) software.

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Oracle launches Java Card 3.1 to boost security for IoT devices at the edge

IoT Tech News | January 18, 2019

Oracle has launched the latest version of Java Card, its open application platform that secures some of the world’s most sensitive devices. The Java Card 3.1 is an extensive update that aims to offer more flexibility in order to meet the special hardware and security requirements of both existing secure chips and emerging IoT technologies. The Java Card 3.1 has features that addresses use cases across markets ranging from telecom and payments to cars and wearables. There are nearly six billion Java Card-based devices deployed every year. But the software platform, which is known to run security services on smart cards and secure elements, is already a leader in the market since many years. This platform has introduced some new features that make applications more portable across security hardware critical to IoT, which allows new uses for hardware-based security, such as multi-cloud IoT security models, and makes Java Card a suitable solution for billions of IoT devices that require security at the edge of the network. Security remains a key benchmark for the industry as 2019 begins to develop. Earlier this month BCC Research put together a report which predicted that the IoT security market, valued at £1.31bn, will reach £4bn by 2023 at a CAGR of 25.1%.

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AI TECH

Diffblue's First AI-Powered Automated Java Unit Testing Solution is Now Available for Free to Commercial and Open Source Software Developers

Diffblue | March 23, 2021

Diffblue, the developers of the world's first AI for code solution that automates the writing of unit tests for Java, announced today that its free IntelliJ extension, Diffblue Cover: Community Edition, is now available to use to create unit tests for any of an organization's Java code – both open source and commercial. For business clients that need additional service, indemnification, and the opportunity to write tests for packages, Diffblue also offers a technical edition. Diffblue also has a CLI variant of Diffblue Cover, which is suitable for team collaboration. Diffblue's ground-breaking technology, developed by University of Oxford researchers, is based on reinforcement learning, the same machine learning strategy that fuelled AlphaGo, Alphabet subsidiary DeepMind's software program that dominated the world champion player GO. Diffblue Cover automates the time-consuming process of writing Java unit tests, which can consume up to 20% of a Java developer's time. Diffblue Cover produces Java tests 10X-100X faster than humans, and are also easy to grasp for users, and manages them automatically as the code evolves, even on applications with tens of millions of lines of code. Java, the most common business programming language in the Global 2000, is now supported by Diffblue Cover. Diffblue Cover's technology can be extended to support other popular programming languages like Python, Javascript, and C#. About Diffblue Diffblue is a pioneer in the use of artificial intelligence to automate software development. Diffblue Cover, which was established by University of Oxford academics, uses artificial intelligence to write unit tests that help development teams and companies enhance their code coverage and accuracy while shipping software quicker, more regularly, and with fewer defects. Diffblue is backed by Goldman Sachs and Oxford Sciences Innovation and has clients such as AWS and Goldman Sachs.

Read More

JavaScript Library Introduced XSS Flaw in Google Search

SecurityWeek | April 01, 2019

A change made several months ago in an open-source JavaScript library introduced a cross-site scripting (XSS) vulnerability in Google Search and likely other Google products. Japanese security researcher Masato Kinugawa discovered what appeared to be an XSS vulnerability in Google Search. Such a security hole can pose a serious risk and it could be highly useful to malicious actors for phishing and other types of attacks. According to an analysis conducted by LiveOverflow, the XSS vulnerability was introduced by the use of a library named Closure and its failure to properly sanitize user input. Closure is a broad JavaScript library designed by Google for complex and scalable web applications. The tech giant has made the library open source and still uses it for many of its applications, including Search, Gmail, Maps and Docs. The vulnerability was apparently introduced on September 26, 2018, when someone removed a sanitization mechanism reportedly due to some user interface design issues. It was addressed on February 22, 2019, when the change made in September 2018 was reverted. Google is said to have patched the vulnerability shortly after learning of its existence. Comments posted by developers when the rollback was done confirmed that the issue was related to an HTML sanitizer and that it introduced an XSS flaw in the Google Web Server (GWS) software.

Read More

Oracle launches Java Card 3.1 to boost security for IoT devices at the edge

IoT Tech News | January 18, 2019

Oracle has launched the latest version of Java Card, its open application platform that secures some of the world’s most sensitive devices. The Java Card 3.1 is an extensive update that aims to offer more flexibility in order to meet the special hardware and security requirements of both existing secure chips and emerging IoT technologies. The Java Card 3.1 has features that addresses use cases across markets ranging from telecom and payments to cars and wearables. There are nearly six billion Java Card-based devices deployed every year. But the software platform, which is known to run security services on smart cards and secure elements, is already a leader in the market since many years. This platform has introduced some new features that make applications more portable across security hardware critical to IoT, which allows new uses for hardware-based security, such as multi-cloud IoT security models, and makes Java Card a suitable solution for billions of IoT devices that require security at the edge of the network. Security remains a key benchmark for the industry as 2019 begins to develop. Earlier this month BCC Research put together a report which predicted that the IoT security market, valued at £1.31bn, will reach £4bn by 2023 at a CAGR of 25.1%.

Read More

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