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Category: Computing & data (Page 71 of 91)

Computing and data is a broad category. Our coverage of computing is largely limited to software, and we are mostly focused on unstructured data, semi-structured data, or mixed data that includes structured data.

Topics include computing platforms, analytics, data science, data modeling, database technologies, machine learning / AI, Internet of Things (IoT), blockchain, augmented reality, bots, programming languages, natural language processing applications such as machine translation, and knowledge graphs.

Related categories: Semantic technologies, Web technologies & information standards, and Internet and platforms.

Arria unveils new natural language technology

Arria NLG, provider of Natural Language Generation (NLG) technology, has announced the release of Arria Connect, which allows companies to embed Arria’s natural language technology into their dashboards and product offerings. Arria Connect puts the integration capabilities into the hands of developers who want to build NLG into their own Business Intelligence (BI) tool, analytics application, website, or process-automation workflow. Arria’s API-based architecture allows for a connection to other technology platforms in two ways:

  1. make a direct API call from any application that can communicate via HTTP; data is ingested and narrative is automatically returned; or,
  2. use the Arria Connect Integration Accelerator Kit to embed NLG inside software platforms for incorporating narrative. Users also have the ability to work with Arria’s prebuilt NLG solutions and configure them.

Developers with no NLG experience can leverage Arria Connect’s prebuilt libraries for integration rather than building from scratch. In addition to Arria’s prebuilt NLG solutions, Arria Connect also comes with a sample NLG implementation, sample code, and a complete documentation set.

https://www.arria.com/connect/

Adobe announces end of PhoneGap development

Adobe announced the end of development for PhoneGap and PhoneGap Build and the end of their investment in Apache Cordova. The PhoneGap Build service will be discontinued on October 1, 2020. PhoneGap’s goal has been to bring the full power of the web to mobile applications and enable mobile developers to create performant apps with a single codebase. Since the project’s beginning in 2008, the market has evolved and Progressive Web Apps (PWAs) now bring the power of native apps to web applications. PWAs are increasingly bridging the gap between web and native mobile apps through capabilities such as offline support, push notifications, home-screen icons and full-screen view control without the need for containers. In the context of these developments and declining PhoneGap usage, Adobe is focusing on providing a platform that enables developers to build, extend, customize and integrate with Adobe products.

Apache Cordova, the open source fork of the PhoneGap project will continue to exist and offers a great pathway for most developers. Refer to the Cordova getting started guide for building locally as an alternative to PhoneGap Build and using Cordova-based tools as an alternative to the PhoneGap-specific workflows. Adobe has worked with Ionic to help customers with the transition experience and to enable them to continue to build their applications in the cloud. Refer to this documentation for best practices on moving your application to Ionic Appflow. There are also a number of alternative products to which you may want to consider migrating your PhoneGap app including:

https://blog.phonegap.com/update-for-customers-using-phonegap-and-phonegap-build-cc701c77502c

Hyland announces Brainware Foundation

Hyland announced Brainware Foundation, the latest release of its intelligent data extraction and text analytics software. Brainware Foundation EP1 includes enhancements to functionality, usability and security – most notably the addition of a new handwriting recognition engine. Brainware users can now opt to leverage Microsoft’s cloud OCR engine through Azure Computer Vision, an intelligent content analysis tool within the portfolio of Microsoft Azure Cognitive Services. The Microsoft engine includes advanced OCR capabilities for extracting difficult handwritten inputs, in addition to machine-printed text. Extraction can be performed in a single pass on free-form printed or scripted writing without anchors, constraint boxes, color dropout, or additional OCR/ICR engines. Other features within the Brainware Foundation EP1 release include:

  • Increased license control in hosted environments: The latest version of Brainware automatically moves runtime license files to a database. This improves supportability and scalability for solutions hosted in multi-server environments. Additionally, users can configure the location, size and storage time of log files.
  • Usability and security enhancements: Additional usability and security enhancements provide the ability to reclassify documents earlier in the document separation process, upgraded security and user access controls with TLS1.2, and password masking within solution configuration.

https://www.hyland.com/en/platform/product-suite/brainware

Zignal Labs adds Lexalytics to provide natural language processing to platform

Lexalytics announced that Zignal Labs, creator of the Impact Intelligence platform for measuring the evolution of opinion in real time, has added Lexalytics Salience engine to extend its platform’s natural language processing (NLP) and text analytics capabilities to help marketers, communicators and analysts gain a greater understanding of perceptions across traditional and social media. With Lexalytics, Zignal’s customers across industries can understand what people are saying about products, services or current events, categorize discussions into separate groupings and themes, and evaluate the sentiment of media coverage across multiple languages.

http://www.lexalytics.com, http://www.zignallabs.com

Unscrambl integrates with Microsoft Teams to let users converse with enterprise data

Unscrambl’s conversational analytics software ‘qbo insights’ is now available as qbo app for Microsoft Teams. qbo, by enabling natural language access to your data, makes facts and insights take center stage in workspace collaborations. With this addition Teams users can make fact-based decisions simply by conversing with their data. Unscrambl’s qbo leverages Teams’ capabilities for an interactive and collaborative data exploration experience. A business user would start by asking a question in natural language, as one would ask a human data analyst. The response is an interactive visualization of the requested data, often with a brief explanation, and suggestions about follow-up questions. Users can converse with qbo one-on-one or collaboratively as a team, view charts, refine, drill-down, create boards and even present their findings without having to leave the Teams platform.

https://qbo.ai

Box adds automated classification to Box Shield

Box announced the addition of intelligent, automated classification to Box Shield, the company’s security solution for protecting content in the cloud. Leveraging machine learning, Shield can now automatically scan files and classify them based on their content, helping businesses detect and secure sensitive data without getting in the way of work. Box Shield helps prevent data leakage and proactively identifies potential insider threats or compromised accounts.

Using machine learning and data leakage prevention capabilities, this new feature scans files in real-time when they’re uploaded, updated, moved, or copied to specified folders, and automatically classifies them based on admin-defined policies. This enables customers to scale data classification and enforce policies across the enterprise, in order to reduce risk and meet compliance standards such as HIPAA, PCI DSS, and GDPR. Customers will be able to:

  • Automatically identify multiple personally identifiable information types within files, including social security numbers, driver’s licenses, International Bank Account Number (IBAN) codes, International Classification of Diseases (ICD-9/ICD-10) codes, and more
  • Automatically identify custom terms and phrases within files – for example: “Box Confidential”, “Internal use only”, and “NDA required”
  • Easily create policies that apply the appropriate classification label based on desired logic – including and/or conditions and unique counts

Once files are classified appropriately, Shield can help prevent data leakage through a combination of access controls already in use by Shield customers, such as shared link, external collaboration, and download restrictions. The new feature supports the most common unstructured file types in Box, including documents, spreadsheets, PDF, Box Notes, and more. The new Box Shield automated classification capabilities will begin to be available today and will roll out to eligible customers over the next month. 

https://www.box.com/

Neofonie announced TXTWerk – text mining for SAP solutions

Neofonie announced that TXTWerk – Text mining for SAP solutions, a framework application is now available for trial and online purchase on SAP App Center, the digital marketplace for SAP partner offerings. TXTWerk is delivered online as a subscription service and integrates with SAP and third-party software through the API management capabilities of SAP Cloud Platform Integration Suite. TXTWerk enables the extraction of metadata from texts, providing structured data from unstructured texts. By applying machine learning techniques in combination with rule-based approaches, TXTWerk can read and understand texts quickly. Whether 1,000 or 10 billion documents need to be processed, TXTWerk recognizes the most important keywords, people, places, organizations, events and key concepts and links them to sources such as knowledge graphs or internal company data. Also, part of the framework are artificial intelligence (AI) processes for classification in classes defined by the customer, a sentiment analysis of texts, phrase and role recognition as well as the automatic linking of entities according to specially defined relations. In addition to the AI processes, TXTWerk comes with a knowledge graph with over seven million entries.

https://www.neofonie.de/english, https://www.sapappcenter.com/en/product/display-0000059151_live_v1

Luminoso introduces deep learning model for evaluating sentiment at concept level

Luminoso’s new deep learning model understands documents using multiple layers of attention, a mechanism that identifies which words are relevant to get context around a specific concept as expressed by a word or phrase. This model is capable of identifying the author’s sentiment for each individual concept they’ve written about, as opposed to providing an analysis of the overall sentiment of the document.

Using Concept-Level Sentiment, users will be able to:

  • Effectively analyze mixed feedback — Concept-level sentiment analysis is critical for capturing and understanding the voice of the customer (VoC). For example, product reviews rarely contain just one type of feedback, and it’s important to tease apart the good from the bad. Getting a polarity for each of the topics in an open-ended survey response is critical for understanding what works and what doesn’t for your customers.
  • Quickly surface buried feedback — Uncovering negative comments in overwhelmingly positive open-ended survey responses is critical for better understanding customers and employees. For instance, in voice of the employee (VoE) surveys, employee feedback can be overwhelmingly positive and delivered in an upbeat way in an effort to soften criticisms. Concept-Level Sentiment in Luminoso enables users to quickly identify and understand “buried” feedback, such as negative points in an overwhelmingly positive HR survey.
  • Intuitively aggregate concept sentiment across an entire dataset — For instance, after responses to a mobile app market research survey are loaded into Luminoso Daylight, a user can get a distribution of positive, negative, and neutral opinions about every aspect of the mobile experience across all of its mentions in the dataset.
  • Analyze customer and employee feedback across multiple languages — Global organizations often receive customer and employee feedback in multiple languages. With Luminoso, users can analyze the sentiment of concepts, natively in 15 languages.

https://luminoso.com/solutions/concept-level-sentiment

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