RTX Video Super Resolution (RTX VSR) is a video scaling feature by Nvidia. It was released on February 28, 2023. == History == The feature was first unveiled during CES 2023 as RTX Video Super Resolution. It uses the on-board Tensor Cores to upscale browser video content in real time. Video Super Resolution was initially only available on RTX 30 and 40 series GPUs, while support for 20 series GPUs was added afterwards; it is now available on all Nvidia RTX-branded GPUs. The feature supports input resolutions from 360p to 1440p and a max output of 4K and comes without support for HDR content although that could be likely added in the future. Nvidia released RTX Video Super Resolution 1.5 with improved video quality and RTX 20 series support on October 17, 2023. == Reception == According to ComputerBase, although "the algorithm is not yet working flawlessly", the feature is "overall recommendable".
Zero-knowledge service
In cloud computing, the term zero-knowledge (or occasionally no-knowledge or zero-access) is a commonly used term for online services that store, transfer or manipulate data with a high level of confidentiality, where the data is only accessible to the data's owner (the client), and not to the service provider. However, unlike "end-to-end encryption", the term "zero-knowledge" does not imply any specific threat model or security notion, and its use is commonly frowned-upon by the security community. The term "zero-knowledge" was popularized by backup service SpiderOak, which later switched to using the term "no knowledge", acknowledging that the previous terminology was not technically accurate. == Disadvantages == Most cloud storage services keep a copy of the client's password on their servers, allowing clients who have lost their passwords to retrieve and decrypt their data using alternative means of authentication; but since zero-knowledge services do not store copies of clients' passwords, if a client loses their password then their data cannot be decrypted, making it practically unrecoverable. Most of the most used cloud storage services, such as Google Drive, Dropbox, OneDrive or iCloud, are also able to furnish access requests from law enforcement agencies for similar reasons; zero-knowledge services, however, are unable to do so, since their systems are designed to make clients' data inaccessible without the client's explicit cooperation.
Enterprise Objects Framework
The Enterprise Objects Framework, or simply EOF, was introduced by NeXT in 1994 as a pioneering object-relational mapping product for its NeXTSTEP and OpenStep development platforms. EOF abstracts the process of interacting with a relational database by mapping database rows to Java or Objective-C objects. This largely relieves developers from writing low-level SQL code. EOF enjoyed some niche success in the mid-1990s among financial institutions who were attracted to the rapid application development advantages of NeXT's object-oriented platform. Since Apple Inc's merger with NeXT in 1996, EOF has evolved into a fully integrated part of WebObjects, an application server also originally from NeXT. Many of the core concepts of EOF re-emerged as part of Core Data, which further abstracts the underlying data formats to allow it to be based on non-SQL stores. == History == In the early 1990s NeXT Computer recognized that connecting to databases was essential to most businesses and yet also potentially complex. Every data source has a different data-access language (or API), driving up the costs to learn and use each vendor's product. The NeXT engineers wanted to apply the advantages of object-oriented programming, by getting objects to "talk" to relational databases. As the two technologies are very different, the solution was to create an abstraction layer, insulating developers from writing the low-level procedural code (SQL) specific to each data source. The first attempt came in 1992 with the release of Database Kit (DBKit), which wrapped an object-oriented framework around any database. Unfortunately, NEXTSTEP at the time was not powerful enough and DBKit had serious design flaws. NeXT's second attempt came in 1994 with the Enterprise Objects Framework (EOF) version 1, a complete rewrite that was far more modular and OpenStep compatible. EOF 1.0 was the first product released by NeXT using the Foundation Kit and introduced autoreleased objects to the developer community. The development team at the time was only four people: Jack Greenfield, Rich Williamson, Linus Upson and Dan Willhite. EOF 2.0, released in late 1995, further refined the architecture, introducing the editing context. At that point, the development team consisted of Dan Willhite, Craig Federighi, Eric Noyau and Charly Kleissner. EOF achieved a modest level of popularity in the financial programming community in the mid-1990s, but it would come into its own with the emergence of the World Wide Web and the concept of web applications. It was clear that EOF could help companies plug their legacy databases into the Web without any rewriting of that data. With the addition of frameworks to do state management, load balancing and dynamic HTML generation, NeXT was able to launch the first object-oriented Web application server, WebObjects, in 1996, with EOF at its core. In 2000, Apple Inc. (which had merged with NeXT) officially dropped EOF as a standalone product, meaning that developers would be unable to use it to create desktop applications for the forthcoming Mac OS X. It would, however, continue to be an integral part of a major new release of WebObjects. WebObjects 5, released in 2001, was significant for the fact that its frameworks had been ported from their native Objective-C programming language to the Java language. Critics of this change argue that most of the power of EOF was a side effect of its Objective-C roots, and that EOF lost the beauty or simplicity it once had. Third-party tools, such as EOGenerator, help fill the deficiencies introduced by Java (mainly due to the loss of categories). The Objective-C code base was re-introduced with some modifications to desktop application developers as Core Data, part of Apple's Cocoa API, with the release of Mac OS X Tiger in April 2005. == How EOF works == Enterprise Objects provides tools and frameworks for object-relational mapping. The technology specializes in providing mechanisms to retrieve data from various data sources, such as relational databases via JDBC and JNDI directories, and mechanisms to commit data back to those data sources. These mechanisms are designed in a layered, abstract approach that allows developers to think about data retrieval and commitment at a higher level than a specific data source or data source vendor. Central to this mapping is a model file (an "EOModel") that you build with a visual tool — either EOModeler, or the EOModeler plug-in to Xcode. The mapping works as follows: Database tables are mapped to classes. Database columns are mapped to class attributes. Database rows are mapped to objects (or class instances). You can build data models based on existing data sources or you can build data models from scratch, which you then use to create data structures (tables, columns, joins) in a data source. The result is that database records can be transposed into Java objects. The advantage of using data models is that applications are isolated from the idiosyncrasies of the data sources they access. This separation of an application's business logic from database logic allows developers to change the database an application accesses without needing to change the application. EOF provides a level of database transparency not seen in other tools and allows the same model to be used to access different vendor databases and even allows relationships across different vendor databases without changing source code. Its power comes from exposing the underlying data sources as managed graphs of persistent objects. In simple terms, this means that it organizes the application's model layer into a set of defined in-memory data objects. It then tracks changes to these objects and can reverse those changes on demand, such as when a user performs an undo command. Then, when it is time to save changes to the application's data, it archives the objects to the underlying data sources. === Using Inheritance === In designing Enterprise Objects developers can leverage the object-oriented feature known as inheritance. A Customer object and an Employee object, for example, might both inherit certain characteristics from a more generic Person object, such as name, address, and phone number. While this kind of thinking is inherent in object-oriented design, relational databases have no explicit support for inheritance. However, using Enterprise Objects, you can build data models that reflect object hierarchies. That is, you can design database tables to support inheritance by also designing enterprise objects that map to multiple tables or particular views of a database table. == Enterprise Objects (EOs) == An Enterprise Object is analogous to what is often known in object-oriented programming as a business object — a class which models a physical or conceptual object in the business domain (e.g. a customer, an order, an item, etc.). What makes an EO different from other objects is that its instance data maps to a data store. Typically, an enterprise object contains key-value pairs that represent a row in a relational database. The key is basically the column name, and the value is what was in that row in the database. So it can be said that an EO's properties persist beyond the life of any particular running application. More precisely, an Enterprise Object is an instance of a class that implements the com.webobjects.eocontrol.EOEnterpriseObject interface. An Enterprise Object has a corresponding model (called an EOModel) that defines the mapping between the class's object model and the database schema. However, an enterprise object doesn't explicitly know about its model. This level of abstraction means that database vendors can be switched without it affecting the developer's code. This gives Enterprise Objects a high degree of reusability. == EOF and Core Data == Despite their common origins, the two technologies diverged, with each technology retaining a subset of the features of the original Objective-C code base, while adding some new features. === Features Supported Only by EOF === EOF supports custom SQL; shared editing contexts; nested editing contexts; and pre-fetching and batch faulting of relationships, all features of the original Objective-C implementation not supported by Core Data. Core Data also does not provide the equivalent of an EOModelGroup—the NSManagedObjectModel class provides methods for merging models from existing models, and for retrieving merged models from bundles. === Features Supported Only by Core Data === Core Data supports fetched properties; multiple configurations within a managed object model; local stores; and store aggregation (the data for a given entity may be spread across multiple stores); customization and localization of property names and validation warnings; and the use of predicates for property validation. These features of the original Objective-C implementation are not supported by the Java implementation.
Metadata controller
Metadata controller (or MDC) is a storage area network (SAN) technology for managing file locking, space allocation and data access authorization. This is needed when several clients are given block level access to the same disk volume, data storage sharing. MDCs are only used on high-end servers. These are never found on user computers. In the absence of MDC over a SAN there is no possible way of ensuring privacy of the stored data. This controller can also play its role as a sharing device in case the administrators allow other servers to access certain blocks in a particular SAN. The access granted to the servers is of different levels. Some times it may happen that the server is not able to see a block or make changes in it in case of a locked file. This is caused by grant of low level access. If different clients on SAN happen to know each other, access may be granted to shift a certain block from one server to another. This allows the recipient server to use the block and make changes in it. MDCs work as enzymes. They require certain types of SANs and networks to work properly. If a controller is connected to the right network it will boost its output. In case of wrong connection i.e. with the incorrect network, it will decrease its performance.
AI notetaker
An AI notetaker is a tool using artificial intelligence to take notes during meetings. They are created by tech companies such as Microsoft and Google; by AI transcription services such Otter.ai, and by smaller firms such as Cluely and Krisp. Some business executives send AI notetakers to attend meetings not only to take notes, but also to answer questions on their behalf. The use of AI notetakers raises ethical questions, including recording meetings without the consent of all participants and the possibility that the notetaker will hallucinate and misrepresent what was said during meetings. There are also concerns when it comes to the privacy and security of meeting data and the sensitive information that lives inside meetings. Further controversies have developed from the use of AI notetakers such as Cluely to cheat in technical job interviews. == Technology == Large technology companies have integrated transcription capabilities into broader productivity and accessibility tools, including real-time captioning, dictation, and meeting documentation features embedded in operating systems and office platforms. Standalone transcription platforms, such as Transkriptor, focus specifically on automated transcription workflows and apply AI-based speech recognition to convert audio and video recordings into text. The software supports transcription in multiple languages and processes recordings uploaded via a web interface as well as through mobile and browser extensions. Tools of this type typically provide editable, time-aligned transcripts and export options for text and subtitle formats, cloud-based processing, multilingual support, and automation in transcription technology.
Google Messages
Google Messages (formerly known as Messenger, Android Messages, and Messages by Google) is a text messaging software application developed by Google for its Android and Wear OS mobile operating systems. It is also available as a web app. Google's official universal messaging platform for the Android ecosystem, Messages employs SMS, MMS, and Rich Communication Services (RCS). Starting in 2023, Google has RCS activated by default on participating Android devices, similar to the implementation of iMessage on Apple devices. Samsung Messages will be discontinued on July 6th 2026, with Samsung transitioning users to Google Messages as the default messaging application. == History == The original code for Android SMS messaging was released in 2009 integrated into the operating system. It was released as a standalone application independent of Android with the release of Android 5.0 Lollipop in 2014, replacing Google Hangouts as the default SMS app on Google's Nexus line of phones. In 2018, Messages adopted RCS messages and evolved to send larger data files, sync with other apps, and even create mass messages. This was in preparation for when Google launched Messages for web. In December 2019, Google began to introduce support for Rich Communication Services (RCS) messaging via an RCS service hosted by Google, referred to in the user interface as "chat features". This was followed by a wider global rollout throughout 2020. The app surpassed 1 billion installs in April 2020, doubling its number of installs in less than a year. Initially, RCS did not support end-to-end encryption. In June 2021, Google introduced end-to-end encryption in Messages by default using the Signal Protocol, for all one-to-one RCS-based conversations, for all RCS group chats in December 2022 for beta users, and for all RCS users by August 2023, as well as enabling RCS for all users by default to encourage encryption. In July 2023, Google announced it would build the Message Layer Security (MLS) end-to-end encryption protocol into Google Messages. Beginning with the Samsung Galaxy S21, Messages replaces Samsung's in-house Messages app as the default text messaging app for One UI for some regions and carriers. In April 2021, the app began to receive UI modifications on Samsung devices to follow aspects of One UI, including pushing the top of the message list towards the middle of the screen to improve ergonomics. In February 2023, Google began to replace references to "chat features" in the Messages user interface with "RCS". In August 2023, Google announced that Messages will use RCS by default for all users unless they opt out, to allow them to benefit from secure messaging. In December 2023, with the arrival of several new features, the app was renamed "Google Messages". In July 2024, Samsung announced it would no longer pre-install Samsung Messages on its Galaxy devices in some regions, starting with the Galaxy Z Fold 6 and Flip, favoring Google Messages instead. In April 2026, Samsung announced that Samsung Messages would be discontinued in July 2026. It encouraged users to switch to Google Messages. == Features == Some of the most important features in Google Messages are: Send instant text and voice messages in 1:1 or group chat conversations over mobile data and Wi-Fi, via Android, Wear OS or the web. End-to-end encryption for RCS chats. Typing, sent, delivered and read status Reply and react to specific messages Share files and high-resolution photos Voice message transcriptions Schedule messages In-app reminders for birthdays and messages you didn't respond to after some time with Nudges Tight integration with the Google ecosystem, e.g. Google Calendar, Meet, Maps, YouTube, Photos, Contacts, Assistant, Search, Safe Browsing etc. Web interface: Users can visit https://messages.google.com/web and either sign in with their Google account or scan the QR code that is shown with their smartphone to access a limited web version of the app that allows them to send and receive messages, provided the smartphone remains connected. Phone number recognition: The app shows the country and province of the caller. Additionally, it can show the company's name or a warning for spam calls if the number is registered in a data base. Access to the Gemini chatbot on select Pixel, Galaxy and Android devices.
Technical data management system
A technical data management system (TDMS) is a document management system (DMS) pertaining to the management of technical and engineering drawings and documents. Often the data are contained in 'records' of various forms, such as on paper, microfilms or digital media. Hence technical data management is also concerned with record management involving technical data. Technical document management systems are used within large organisations with large scale projects involving engineering. For example, a TDMS can be used for integrated steel plants (ISP), automobile factories, aero-space facilities, infrastructure companies, city corporations, research organisations, etc. In such organisations, technical archives or technical documentation centres are created as central facilities for effective management of technical data and records. TDMS functions are similar to that of conventional archive functions in concepts, except that the archived materials in this case are essentially engineering drawings, survey maps, technical specifications, plant and equipment data sheets, feasibility reports, project reports, operation and maintenance manuals, standards, etc. Document registration, indexing, repository management, reprography, etc. are parts of TDMS. Various kinds of sophisticated technologies such as document scanners, microfilming and digitization camera units, wide format printers, digital plotters, software, etc. are available, making TDMS functions an easier process than previous times. == Constituents of a technical data management system == Technical data refers to both scientific and technical information recorded and presented in any form or manner (excluding financial and management information). A Technical Data Management System is created within an organisation for archiving and sharing information such as technical specifications, datasheets and drawings. Similar to other types of data management system, a Technical Data Management System consists of the 4 crucial constituents mentioned below. === Data planning === Data plans (long-term or short-term) are constructed as the first essential step of a proper and complete TDMS. It is created to ultimately help with the 3 other constituents, data acquisition, data management and data sharing. A proper data plan should not exceed 2 pages and should address the following basics: Types of data (samples, experiment results, reports, drawings, etc.) and metadata (data that summarizes and describes other data. In this case, it refers to details such as sample sizes, experiment conditions and procedures, dates of reports, explanations of drawings, etc.) Means of researches and collections of data (field works, experiments in production lines, etc.) Costs of researches Policies for access, sharing (re-use within the organisation and re-distribution to the public) Proposals for archiving data and maintaining access to it === Data acquisition === Raw data is collected from primary sites of the organisations through the use of modern technologies. Please reference the table below for examples. The data collected is then transferred to technical data centres for data management. === Data management === After data acquisition, data is sorted out, whilst useful data is archived, unwanted data is disposed. When managing and archiving data, the features below of the data are considered. Names, labels, values and descriptions for variables and records. (In the case of TDMS, one example is names of equipments on an equipment datasheet) Derived data from the original data, with code, algorithm or command file used to create them. (In the case of TDMS, one example is an expectation report derived from the analysis of an equipment datasheet) Metadata associates with the data being archived === Data sharing === Archived and managed data are accessible to rightful entities. A proper and complete TDMS should share data to a suitable extent, under suitable security, in order to achieve optimal usage of data within the organisation. It aims for easy access when reused by other researchers and hence it enhances other research processes. Data is often referred in other tests and technical specifications, where new analysis is generated, managed and archived again. As a result, data is flowing within the organisation under effective management through the use of TDMS. == Advantages and disadvantages of usage of technical data management systems == There are strengths and weakness when using technical data management systems (TDMS) to archive data. Some of the advantages and disadvantages are listed below. === Advantages === ==== 1. Faster and easier data management ==== Since TDMS is integrated into the organisation's systems, whenever workers develop data files (SolidWorks, AutoCAD, Microsoft Word, etc.), they can also archive and manage data, linking what they need to their current work, at the same time they can also update the archives with useful data. This speeds up working processes and makes them more efficient. ==== 2. Increased security ==== All data files are centralized, hence internal and external data leakages are less likely to happen, and the data flow is more closely monitored. As a result, data in the organisation is more secured. ==== 3. Increased collaboration within the organisation ==== Since the data files are centralized and the data flow within the organisation increases, researchers and workers within the organisation are able to work on joint projects. More complex tasks can be performed for higher yields. ==== 4. Compatible to various formats of data ==== TDMS is compatible to many formats of data, from basic data like Microsoft Words to complex data like voice data. This enhances the quality of the management of data archived. === Disadvantages === ==== 1. Higher financial costs ==== Implementing TDMS into the organisation's systems involves monetary costs. Maintenance costs certain amount of human resources and money as well. These resources involve opportunity costs as they can be utilized in other aspects. ==== 2. Lower stability ==== Since TDMS manages and centralizes all the data the organisation processes, it links the working processes within the whole organisation together. It also increases the vulnerability of the organisation data network. If TDMS is not stable enough or when it is exposed to hacker and virus attacks, the organisation's data flow might shut down completely, affecting the work in an organisation-wide scale and leading to a lower stability as results. == Comparison between traditional data management approaches and technical data management systems == Test engineers and researchers are facing great challenges in turning complex test results and simulation data into usable information for higher yields of firms. These challenges are listed below. Increase in complication of designs Reduced in time and budgets available Higher quality is demanded === Traditional data management approaches === Many organisations are still applying the conventional file management systems, due to the difficulty in building a proper and complete archives for data management. The first approach is the simple file-folder system. This costs the problem of ineffectiveness as workers and researchers have to manually go through numerous layers of systems and files for the target data. Moreover, the target data may contain files with different formats and these files may not be stored in the same machine. These files are also easily lost if renamed or moved to another location. The second approach is conventional databases such as Oracle. These databases are capable of enabling easy search and access of data. However, a great drawback is that huge effort for preparing and modeling the data is required. For large-scale projects, huge monetary costs are induced, and extra IT human resources must be employed for constant handling, expanding and maintaining the inflexible system, which is custom for specific tasks, instead of all tasks. In the long-term, it is not cost-effective. === Technical data management systems (TDMS) === TDMS is developed based on 3 principles, flexible and organized file storage, self-scaling hybrid data index, and an interactive post-processing environment. The system in practical, mainly consists of 3 components, data files with essential and relevant Metadata, data finders for organizing and managing data regardless of files formats, and, a software of searching, analyzing and reporting. With metadata attached to original data files, the data finder can identify different related data files during searches, even if they are in different file formats. TDMS hence allows researchers to search for data like browsing the Internet. Last but not least, it can adapt to changes and update itself according to the changes, unlike databases. == Comparison between strong information systems and weak information systems == Complex organizations may need large amounts