(Not to be confused with the lazy learning regime, see Neural tangent kernel). In machine learning, lazy learning is a learning method in which generalization of the training data is, in theory, delayed until a query is made to the system, as opposed to eager learning, where the system tries to generalize the training data before receiving queries. The primary motivation for employing lazy learning, as in the K-nearest neighbors algorithm, used by online recommendation systems ("people who viewed/purchased/listened to this movie/item/tune also ...") is that the data set is continuously updated with new entries (e.g., new items for sale at Amazon, new movies to view at Netflix, new clips at YouTube, new music at Spotify or Pandora). Because of the continuous update, the "training data" would be rendered obsolete in a relatively short time especially in areas like books and movies, where new best-sellers or hit movies/music are published/released continuously. Therefore, one cannot really talk of a "training phase". Lazy classifiers are most useful for large, continuously changing datasets with few attributes that are commonly queried. Specifically, even if a large set of attributes exist - for example, books have a year of publication, author/s, publisher, title, edition, ISBN, selling price, etc. - recommendation queries rely on far fewer attributes - e.g., purchase or viewing co-occurrence data, and user ratings of items purchased/viewed. == Advantages == The main advantage gained in employing a lazy learning method is that the target function will be approximated locally, such as in the k-nearest neighbor algorithm. Because the target function is approximated locally for each query to the system, lazy learning systems can simultaneously solve multiple problems and deal successfully with changes in the problem domain. At the same time they can reuse a lot of theoretical and applied results from linear regression modelling (notably PRESS statistic) and control. It is said that the advantage of this system is achieved if the predictions using a single training set are only developed for few objects. This can be demonstrated in the case of the k-NN technique, which is instance-based and function is only estimated locally. == Disadvantages == Theoretical disadvantages with lazy learning include: The large space requirement to store the entire training dataset. In practice, this is not an issue because of advances in hardware and the relatively small number of attributes (e.g., as co-occurrence frequency) that need to be stored. Particularly noisy training data increases the case base unnecessarily, because no abstraction is made during the training phase. In practice, as stated earlier, lazy learning is applied to situations where any learning performed in advance soon becomes obsolete because of changes in the data. Also, for the problems for which lazy learning is optimal, "noisy" data does not really occur - the purchaser of a book has either bought another book or hasn't. Lazy learning methods are usually slower to evaluate. In practice, for very large databases with high concurrency loads, the queries are not postponed until actual query time, but recomputed in advance on a periodic basis - e.g., nightly, in anticipation of future queries, and the answers stored. This way, the next time new queries are asked about existing entries in the database, the answers are merely looked up rapidly instead of having to be computed on the fly, which would almost certainly bring a high-concurrency multi-user system to its knees. Larger training data also entail increased cost. Particularly, there is the fixed amount of computational cost, where a processor can only process a limited amount of training data points. There are standard techniques to improve re-computation efficiency so that a particular answer is not recomputed unless the data that impact this answer has changed (e.g., new items, new purchases, new views). In other words, the stored answers are updated incrementally. This approach, used by large e-commerce or media sites, has long been used in the Entrez portal of the National Center for Biotechnology Information (NCBI) to precompute similarities between the different items in its large datasets: biological sequences, 3-D protein structures, published-article abstracts, etc. Because "find similar" queries are asked so frequently, the NCBI uses highly parallel hardware to perform nightly recomputation. The recomputation is performed only for new entries in the datasets against each other and against existing entries: the similarity between two existing entries need not be recomputed. == Examples of Lazy Learning Methods == K-nearest neighbors, which is a special case of instance-based learning. Local regression. Lazy naive Bayes rules, which are extensively used in commercial spam detection software. Here, the spammers keep getting smarter and revising their spamming strategies, and therefore the learning rules must also be continually updated.
Instance (computer science)
In computer science, an instance or token (from metalogic and metamathematics) is a specific occurrence of a software element that is based on a type definition. When created, an occurrence is said to have been instantiated, and both the creation process and the result of creation are called instantiation. == Examples == Chat AI instance In chat-based AI systems, an assistant can be invoked across many independent conversation sessions (often called a thread), each with its own message history. A specific execution of the assistant over that session may be represented as a run (an execution on a thread). Class instance In object-oriented programming, an object created from a class type. Each instance of a class shares the class-defined structure and behavior but has its own identity and state. Procedural instance In some contexts (including Simula), each procedure call can be viewed as an instance of that procedure—an activation with its own parameters and local variables. Computer instance In cloud computing and virtualization, an instance commonly refers to a provisioned virtual machine or virtual server with an allocated combination of compute, memory, network, and storage resources. Polygonal model In computer graphics, a model may be instanced so it can be drawn multiple times with different transforms and parameters, improving performance by reusing shared geometry data. Program instance In a POSIX-oriented operating system, a running process is an instance of a program. It can be instantiated via system calls such as fork() and exec(). Each executing process is an instance of a program it has been instantiated from.
Research software engineering
Research software engineering is the application of software engineering practices, methods and techniques for research software, i.e. software that was made for and is mainly used within research projects. As usual for software engineering, this also includes knowledge of other (and in this case varying) research fields as well as open science that need to be incorporated into a software development process. The term was proposed in a research paper in 2010 in response to an empirical survey on tools used for software development in research projects. It started to be used in United Kingdom in 2012, when it was needed to define the type of software development needed in research. This focuses on reproducibility, reusability, and accuracy of data analysis and applications created for research. == Support == Various type of associations and organisations have been created around this role to support the creation of posts in universities and research institutes. In 2014 a Research Software Engineer Association was created in UK, which attracted 160 members in the first three months and which lead to the creation of the Society of Research Software Engineering in 2019. Other countries like the Netherlands, Germany, and the USA followed creating similar communities and there are similar efforts being pursued in Asia, Australia, Canada, New Zealand, the Nordic countries, and Belgium. In January 2021 the International Council of RSE Associations was introduced. UK counts over 40 universities and institutes with groups that provide access to software expertise to different areas of research. Additionally, the Engineering and Physical Sciences Research Council created a Research Software Engineer fellowship to promote this role and help the creation of RSE groups across UK, with calls in 2015, 2017, and 2020. The world first RSE conference took place in UK in September 2016 and it has been repeated annually (except for a gap in 2020) since. In 2019 the first national RSE conferences in Germany and the Netherlands were held, next editions were planned for 2020 and then cancelled. US-RSE held its first national conference in 2023. The Research Software Alliance was formed in 2019 to advance the global research software ecosystem by collaborating with decision makers and key influencers. The SORSE (A Series of Online Research Software Events) community was established in late‑2020 in response to the COVID-19 pandemic and ran its first online event in September 2020.
ZeroPC
ZeroPC was a commercial webtop developed by ZeroDesktop, Inc. located in San Mateo, California. ZeroPC has been called a personal cloud OS. It mimicked the look, feel and functionality of the desktop environment of a real operating system. The software was launched in September 2011 through Disrupt SF 2011 event and recently selected to the finalist of SXSW 2012 in Innovative Web Technology category. ZeroPC is web-based and required a Java applet to operate bundled productivity tool Thinkfree. The web applications found on ZeroPC are built on Java in the back end. Features included drag-and-drop functionality, cloud dashboard and personal cloud storage meta services. ZeroPC belonged to a category of services that intended to turn the Web into a full-fledged platform by using Web services as a foundation along with presentation technologies that replicated the experience of desktop applications for users. ZeroPC aggregates content so users can easily access, transfer and share whatever content they want, using a web browser from any device. Its meta-cloud layer supports Dropbox, Box, SugarSync, OneDrive, 4Shared, Google Drive, Evernote, Picasa, Flickr, Instagram, Facebook, Twitter, and Photobucket. ZeroPC Cloud OS platform also provides extensive APIs for iOS and Android App developers. Some of the features found on ZeroPC are: File sharing, Webmail, Cloud Content Navigator, Instant messenger, Sticky Note, Audio/Video Player and Office productivity applications. ZeroPC 2.0 platform ran on AWS for free and paid users. Its platform is licensable to Telco and ISV for commercial purpose. Their clients are SFR, SK Telecom, Hancom and others. As of June 1, 2017, ZeroPC's servers were switched off completely, and ZeroPC is no longer in service since its parent company, NComputing, had launched Virtual Desktop Service in the cloud (AWS) to public. == Browser and Platform Compatibility == The ZeroPC web desktop was compatible with Mac OS X and Microsoft Windows platforms. It is certified to operate on Safari 6.0, Firefox 15.0.1, Google Chrome 22.0.1229.79 m and Internet Explorer 8 and 9. The ZeroPC front end user interface executes entirely within a web browser (see above) and uses HTML, some features of HTML5, JavaScript, AJAX and an optional Java plug-in. == Security == All communication between the ZeroPC front end user interface and the ZeroPC back end servers is encrypted using SSL (HTTPS) protocol. Furthermore, any content stored in the ZeroPC server-side repository is also encrypted using 256-bit Advanced Encryption Standard (AES-256) by Amazon S3 on AWS. ZeroPC users could connect their ZeroPC profile to other storage services such as Dropbox and Box. This connection allows the ZeroPC user to fully manage their content stored in these other storage services. To establish the connection ZeroPC rigorously adhered to the Oauth implementation provided by the target storage service. Upon completion of the Oauth process, ZeroPC stores the relevant access token in the user's profile. This token, along with all other sensitive password related data was encrypted using AES 256-bit key size. == Implementations == As noted above, the ZeroPC platform was hosted on Amazon Web Services infrastructure and is available to the general consumer. A user was allowed to sign up by selecting one of three account plans including a no-cost option. The ZeroPC could also be white-labeled for organizations wishing to provide this functionality to their own users. The white-label options include managed hosting on Amazon Web Services infrastructure and also installation within the organization's IT infrastructure. == User Access Points == The ZeroPC infrastructure provided user access to content and features in several different ways. As described in this article the user can access their information by signing into the ZeroPC web desktop. Additionally, ZeroPC offers native applications designed to run on popular mobile devices including smartphones and tablets. == Leadership == ZeroPC was founded by Chief Executive Officer, Young Song, an entrepreneur who previously founded NComputing, a $60 million venture-backed company. He also co-founded eMachines, Inc., a low-cost computer brand (later acquired by Gateway).
Carrier cloud
In cloud computing, a carrier cloud is a class of cloud that integrates wide area networks (WAN) and other attributes of communications service providers’ carrier-grade networks to enable the deployment of highly-complex applications in the cloud. In contrast, classic cloud computing focuses on the data center and does not address the network connecting data centers and cloud users. This may result in unpredictable response times and security issues when business-critical data are transferred over the Internet. == History == The advent of virtualization technology, cost-effective computing hardware, and ubiquitous Internet connectivity have enabled the first wave of cloud services starting in the early years of the 21st century. But many businesses and other organizations hesitated to move to more demanding applications, from on-premises dedicated hardware to private or public clouds. As a response, communications service providers started in the 2010/2011 time frame to develop carrier clouds that address perceived weaknesses in existing cloud services. Cited weaknesses vary but often include possible downtime, security issues, high cost of custom software and data transfer, inflexibility of some cloud apps, poor customer and nonfulfillment of service level agreements (SLAs). == Characteristics == To enable the deployment of time-sensitive and business critical applications in the cloud, the carrier cloud is designed to match or even exceed the characteristics of on-premises deployments. Therefore, the carrier cloud is characterized by some or all of the following items: Configurable, elastic network performance: Typical cloud computing solutions use the best effort of the public Internet to connect cloud users and data centers. This approach provides instant connectivity but does not offer control over network capacities, latencies, and jitter. Carrier clouds address these gaps with content delivery networks and/or dedicated virtual private networks (VPN) at OSI layers 1 (optical wavelengths), 2 (data link layer), and 3 (network layer). These VPNs can be configured to offer the desired performance parameters and exhibit the same type of elasticity for the network that regular clouds provide for servers and storage. To achieve the requested performance parameters, such as low latency, cloud applications can be (automatically) allocated to distributed data centers that are close enough to the cloud users. Automatic resource placement: For a cloud with multiple data centers, information about both the data center and the connecting network is relevant for a decision of where to place cloud images and storage volumes. For this decision, carrier clouds can obtain relevant information about the network, e.g., using the Application-Layer Traffic Optimization (ALTO) protocol. High level of security and governance: Cloud application providers are subject to general and domain specific security, privacy, and governance requirements and regulations, such as the European Data Protection Directive and the U.S. Health Insurance Portability and Accountability Act. For added security, the wide area network of the carrier cloud can provide segregated encrypted or unencrypted network links that are not accessible from the general Internet. At the data center, the carrier cloud provides e.g. virtual private servers, management processes, logs, and documentation to fulfill security and governance rules. Location control: Fundamentally, cloud users should not be concerned with the geographic location of their cloud resources. However, privacy and other regulations may mandate that certain types of data must not be sent outside a national jurisdiction or other geographical region. Open APIs: Carrier clouds provide graphical user interfaces and Web application programming interfaces that allow cloud application providers to set up, manage, and monitor both, the data center and the WAN, of their cloud services. == Architecture == Carrier clouds encompass data centers at different network tiers and wide area networks that connect multiple data centers to each other as well as to the cloud users. Links between data centers are used for failover, overflow, backup, and geographic diversity. Carrier clouds can be set up as public, private, or hybrid clouds. The carrier cloud federates these cloud entities by using a single management system to orchestrate, manage, and monitor data center and network resources as a single system.
Reference Software International
Reference Software International, Inc. (RSI), was an American software developer active from 1985 to 1993 and based in Albuquerque, New Mexico, and San Francisco, California. The company released several productivity and reference software packages, including the Grammatik grammar checker, for MS-DOS. The company was acquired by WordPerfect Corporation in 1993. == History == === Background (1980–1985) === Reference Software International, Inc., was founded by Donald "Don" Emery and Bruce Wampler in 1985 in San Francisco, California. Both Wampler and Emery were college professors when they founded RSI: Wampler at the University of New Mexico as a professor of computer science and Emery a professor of marketing at San Francisco State University. After graduating from the University of Utah in around 1978, Wampler founded his first software company, Aspen Software, in Tijeras, New Mexico, in 1979. Wampler founded Aspen to develop an early spell checker software package, called Proofreader, for the TRS-80, licensing Random House's Webster's Unabridged Dictionary for the package's lexicon. In 1980, he began development on a grammar checker inspired by Writer's Workbench, a pioneering grammar checker for Unix systems. Wampler used Writer's Workbench heavily during the writer of his doctoral dissertation but disliked having to jump between the Apple II on which he composed the dissertation and the mainframe on which Writer's Workbench ran, and so wanted to develop a version of the latter for microcomputers. Wampler's work came to fruition as Grammatik in 1981, eventually ported to several other microcomputer platforms in the early 1980s. In 1983, by which point the company had 12 employees and sold a combined 80,000 units of Grammatik and Proofreader, Wampler sold Aspen to Dictronics, a software company best known for developing the Electronic Thesaurus, an early thesaurus program for microcomputers. Dictronics was in turn purchased by Wang Laboratories; according to Wampler, "Wang bought [Aspen] and sat on it. They did nothing with it". Wampler moved on to teach for the University of New Mexico, but, frustrated by Wang's inaction, got the urge to resurrect his work. In 1985, he was able to license back Grammatik and Proofreader from a small California-based software firm that had grandfathered rights to a forked version of both. In the same year, he met Emery, who, impressed by Wampler's, founded Reference Software International to market his software. RSI's research and development headquarters were based in Albuquerque, while the company's sales and marketing department was based in Walnut Creek, California. === Success (1985–1992) === In August 1985, RSI released their first product: the Random House Reference Set, a new version of Proofreader for the IBM Personal Computer and compatibles, revised to be a terminate-and-stay-resident program that ran atop other word processors such as WordStar or WordPerfect. At the time, Reference Set was the only such program on the market that functioned like this. RSI netted $114,000 from sales of Reference Set by the end of 1985. In June 1986, they released version 2.0 of Grammatik as Grammatik II for the PC. The latter was a breakout hit for RSI, receiving praise in the press (including technology journals such as PC Magazine) and RSI selling 1,000 units a month. In spring 1987, they released Reference Set II, which allowed users to import their own words into the built-in dictionary and added a thesaurus of 300,000 words. In November 1987, they released version 3.0 of Reference Set, which comprised two new field-specific dictionaries for the medical and legal professions. As well as the general Random House dictionary and thesaurus, it included Stedman's Medical Dictionary and Black's Law Dictionary. Emery consulted Paul Brest and Bob Jackson—professors of law at Stanford Law School and San Francisco State respectively—for the curation of the law dictionary; and Burton Grebin—at the time the executive director of Mount Saint Mary's Hospital—for the curation of the medical dictionary. In fall 1988, the company released Grammatik III, a total rewrite that made use of artificial intelligence to more accurately judge the grammar of sentences by breaking them down into a syntactic hierarchy. Grammatik III received universal acclaim, with Gloria Morris of InfoWorld calling it the apparent leader in the grammar checking field and Sandra Anderson of Mac Home Journal calling it "hands down ... the best of the industry" six years after its release. By 1989, the product had competitors in Correct Grammar by Lifetree Software and RightWriter by Rightsoft, Inc. By 1990, RSI achieved annual sales of $9.7 million. In the same year they released Grammatik IV, which was the first to offer direct integration with WordPerfect on both MS-DOS and Windows. In March 1992—by which point RSI had sold 1.5 million copies of Grammatik across all versions—the company released version 5 of the program, another rewrite that updated the lexicon further and added new functions such as word redundancy detection. Around the same time, the company introduced Easy Proof, a pared-down version of Grammatik intended for novice writers, students, and family computers. In 1991, the company was engaged in a trademark dispute with Systems Compatibility Corporation (SCC) of Chicago, Illinois, over the rights to the Software Toolkit title. Both companies had published software bundles bearing the name in the turn of the 1990s; SCC had published theirs first in 1988 and registered the trademark with the USPTO. SCC was granted a restraining order against RSI in January 1991. The following month, RSI agreed to rename their product, preventing a protracted legal battle. === Decline and acquisition (1992–1993) === By early 1992, RSI achieved annual sales of more than $13 million, employed 120 people, and had opened international offices in London, Belgium, and Antwerp to sell foreign versions of Reference Set and Grammatik. The company reached peak employment in the middle of 1992, with 140 employees. However, RSI's launch of six disparate titles in the year proved problematic for the company when they failed to sell as well as they had projected, and the company laid off employees by the dozens. By December 1992, only 71 employees were left, 32 from their San Francisco office. On the last day of 1992, RSI received an acquisition offer from WordPerfect Corporation, makers of the namesake word processor based in Orem, Utah. The deal was inked in January 1993, RSI's stakeholders receiving $19 million. The company's remaining employees were absorbed into WordPerfect in Orem. WordPerfect continued selling Grammatik as a standalone product for several years.
Curve (tonality)
In image editing, a curve is a remapping of image tonality, specified as a function from input level to output level, used as a way to emphasize colours or other elements in a picture. Curves can usually be applied to all channels together in an image, or to each channel individually. Applying a curve to all channels typically changes the brightness in part of the spectrum. Light parts of a picture can be easily made lighter and dark parts darker to increase contrast. Applying a curve to individual channels can be used to stress a colour. This is particularly efficient in the Lab colour space due to the separation of luminance and chromaticity, but it can also be used in RGB, CMYK or whatever other colour models the software supports.