Artisto

Artisto is a video processing application with art and movie effects filters based on neural network algorithms created in 2016 by Mail.ru Group machine learning specialists. At the moment the application can process videos up to 10 seconds long and offers users 21 filters, including those based on the works of famous artists (e.g. Blue Dream — Pablo Picasso), theme-based (Rio-2016 — related to the 2016 Summer Olympics in Rio de Janeiro) and others. The app works with both pre-recorded videos and videos recorded with the application. == History == Information on the application first appeared on Mail.ru Group Vice President Anna Artamonova's FB page on July 29, 2016. At the moment of posting there was only an Android version available. According to Anna, the application's first version only took eight days to develop. On July 31, the application was added to the AppStore for free download. From this moment and continuing into the present, Artisto has been the world's first app that uses neural networks for editing short videos, processing them in the style of famous artworks or any other source image. Prisma (app) application developers promise to deliver similar functionality at any moment. The application soon won recognition and started to attract the attention of both international brands (e.g. Korean auto manufacturer Kia Motors) and popular singers and musicians. According to the independent App Annie analysis system, within the first two weeks on the market the application made it onto the TOP download lists in nine countries. == Technology == The idea of transferring styles from works of famous artists to images was first mentioned in September 2015 after the publication of Leon Gatys's article "A Neural Algorithm of Artistic Style", where he described the algorithm in detail. The major shortcoming of this algorithm is its slow performance, which is up to dozens of seconds depending on the algorithm's settings. In March 2016, Russian researcher Dmitry Ulyanov's article was published, where he invented a way to improve the generation of stylized pictures using additional neuron generator network training. With this approach, stylized images can be generated within just dozens of milliseconds. Seventeen days after Ulyanov's article, Justin Johnson published an article containing an identical idea, the only difference being the structure of the generator network. The Artisto application was developed using these open-source technologies, which Mail.ru Group's machine learning specialists improved for faster video processing and better quality.

Cloud computing

Cloud computing is defined by the International Organization for Standardization (ISO) as "a paradigm for enabling network access to a scalable and elastic pool of shareable physical or virtual resources with self-service provisioning and administration on demand". It is commonly referred to as "the cloud". == Characteristics == In 2011, the National Institute of Standards and Technology (NIST) identified five "essential characteristics" for cloud systems. Below are the exact definitions according to NIST: On-demand self-service: "A consumer can unilaterally provision computing capabilities, such as server time and network storage, as needed automatically without requiring human interaction with each service provider." Broad network access: "Capabilities are available over the network and accessed through standard mechanisms that promote use by heterogeneous thin or thick client platforms (e.g., mobile phones, tablets, laptops, and workstations)." Resource pooling: " The provider's computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to consumer demand." Rapid elasticity: "Capabilities can be elastically provisioned and released, in some cases automatically, to scale rapidly outward and inward commensurate with demand. To the consumer, the capabilities available for provisioning often appear unlimited and can be appropriated in any quantity at any time." Measured service: "Cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported, providing transparency for both the provider and consumer of the utilized service. By 2023, the International Organization for Standardization (ISO) had expanded and refined the list. == History == The history of cloud computing extends to the 1960s, with the initial concepts of time-sharing becoming popularized via remote job entry (RJE). The "data center" model, where users submitted jobs to operators to run on mainframes, was predominantly used during this era. This period saw broad experimentation with making large-scale computing power more accessible through time-sharing, while optimizing infrastructure, platforms, and applications to improve efficiency for end users. The "cloud" metaphor for virtualized services dates to 1994, when it was used by General Magic for the universe of "places" that mobile agents in the Telescript environment could "go". The metaphor is credited to David Hoffman, a General Magic communications specialist, based on its long-standing use in networking and telecom. The expression cloud computing became more widely known in 1996 when Compaq Computer Corporation drew up a business plan for future computing and the Internet. The company's ambition was to supercharge sales with "cloud computing-enabled applications". The business plan foresaw that online consumer file storage would likely be commercially successful. As a result, Compaq decided to sell server hardware to internet service providers. In the 2000s, the application of cloud computing began to take shape with the establishment of Amazon Web Services (AWS) in 2002, which allowed developers to build applications independently. In 2006 Amazon Simple Storage Service, known as Amazon S3, and the Amazon Elastic Compute Cloud (EC2) were released. In 2008 NASA's development of the first open-source software for deploying private and hybrid clouds. The following decade saw the launch of various cloud services. In 2010, Microsoft launched Microsoft Azure, and Rackspace Hosting and NASA initiated an open-source cloud-software project, OpenStack. IBM introduced the IBM SmartCloud framework in 2011, and Oracle announced the Oracle Cloud in 2012. In December 2019, Amazon launched AWS Outposts, a service that extends AWS infrastructure, services, APIs, and tools to customer data centers, co-location spaces, or on-premises facilities. == Value proposition == Cloud computing can shorten time to market by offering pre-configured tools, scalable resources, and managed services, allowing users to focus on core business value rather than maintaining infrastructure. Cloud platforms can enable organizations and individuals to reduce upfront capital expenditures on physical infrastructure by shifting to an operational expenditure model, where costs scale with usage. Cloud platforms also offer managed services and tools, such as artificial intelligence, data analytics, and machine learning, which might otherwise require significant in-house expertise and infrastructure investment. While cloud computing can offer cost advantages through effective resource optimization, organizations often face challenges such as unused resources, inefficient configurations, and hidden costs without proper oversight and governance. Many cloud platforms provide cost management tools, such as AWS Cost Explorer and Azure Cost Management, and frameworks like FinOps have emerged to standardize financial operations in the cloud. Cloud computing also facilitates collaboration, remote work, and global service delivery by enabling secure access to data and applications from any location with an internet connection. Cloud providers offer various redundancy options for core services, such as managed storage and managed databases, though redundancy configurations often vary by service tier. Advanced redundancy strategies, such as cross-region replication or failover systems, typically require explicit configuration and may incur additional costs or licensing fees. Cloud environments operate under a shared responsibility model, where providers are typically responsible for infrastructure security, physical hardware, and software updates, while customers are accountable for data encryption, identity and access management (IAM), and application-level security. These responsibilities vary depending on the cloud service model—Infrastructure as a Service (IaaS), Platform as a Service (PaaS), or Software as a Service (SaaS)—with customers typically having more control and responsibility in IaaS environments and progressively less in PaaS and SaaS models, often trading control for convenience and managed services. == Adoption and suitability == The decision to adopt cloud computing or maintain on-premises infrastructure depends on factors such as scalability, cost structure, latency requirements, regulatory constraints, and infrastructure customization. Organizations with variable or unpredictable workloads, limited capital for upfront investments, or a focus on rapid scalability benefit from cloud adoption. Startups, SaaS companies, and e-commerce platforms often prefer the pay-as-you-go operational expenditure (OpEx) model of cloud infrastructure. Additionally, companies prioritizing global accessibility, remote workforce enablement, disaster recovery, and leveraging advanced services such as AI/ML and analytics are well-suited for the cloud. In recent years, some cloud providers have started offering specialized services for high-performance computing and low-latency applications, addressing some use cases previously exclusive to on-premises setups. On the other hand, organizations with strict regulatory requirements, highly predictable workloads, or reliance on deeply integrated legacy systems may find cloud infrastructure less suitable. Businesses in industries like defense, government, or those handling highly sensitive data often favor on-premises setups for greater control and data sovereignty. Additionally, companies with ultra-low latency requirements, such as high-frequency trading (HFT) firms, rely on custom hardware (e.g., FPGAs) and physical proximity to exchanges, which most cloud providers cannot fully replicate despite recent advancements. Similarly, tech giants like Google, Meta, and Amazon build their own data centers due to economies of scale, predictable workloads, and the ability to customize hardware and network infrastructure for optimal efficiency. However, these companies also use cloud services selectively for certain workloads and applications where it aligns with their operational needs. In practice, many organizations are increasingly adopting hybrid cloud architectures, combining on-premises infrastructure with cloud services. This approach allows businesses to balance scalability, cost-effectiveness, and control, offering the benefits of both deployment models while mitigating their respective limitations. == Challenges and limitations == One of the primary challenges of cloud computing, compared with traditional on-premises systems, is maintaining data security and privacy. Cloud users entrust their sensitive data to third-party providers, who may not have adequate measures to protect it from unau

Harold Borko

Harold Borko (1922-2012) was an American psychologist and researcher working primarily in the field of information science. == Biography == Borko was born in 1922 in New York City, New York. After serving in the US Army from 1942 to 1946 he obtained a BA in Psychology from the University of California, Los Angeles in 1948 and both his MA and PhD from the University of Southern California in Psychology in 1952. He returned to the army as a psychologist until 1956 after which he began a career working in and teaching information science. He died in California in 2012. == Information Science Career == After leaving the military Borko began working at the RAND Corporation as a Systems Training Specialist in 1956 and moved to the Systems Development Corporation a year later working in the Language Processing and Retrieval department. Alongside this work he taught Psychology at USC from 1957-65 and then moved into teaching Library Science at UCLA from 1965. In 1967 Borko left his role at the Systems Development Corporation and continued as a full-time professor at UCLA until his retirement in 1993.. From 1961 to 1995 Borko authored and co-authored over 100 articles on new developments in the field as well as the historiography of information science. He served as an editor of the Journal of Educational Data Processing from 1963-1975 and as President of the American Society for Information Science in 1966 == Partial list of works == Borko, H. (1962, May). The construction of an empirically based mathematically derived classification system. In Proceedings of the May 1-3, 1962, spring joint computer conference (pp. 279-289). Borko, H., & Bernick, M. (1963). Automatic document classification. Journal of the ACM (JACM), 10(2), 151-162. Borko, H. (1964). The Storage and Retrieval of Educational Information. Journal of Teacher Education, 15(4), 449-452. Borko, H. (1964). Measuring the reliability of subject classification by men and machines. American Documentation, 15(4), 268-273. Borko, H. (1965). The conceptual foundations of information systems. Borko, H. (1968), Information science: What is it?†. Amer. Doc., 19: 3-5. https://doi.org/10.1002/asi.5090190103 Borko, H. (1970). Experiments in book indexing by computer. Information storage and retrieval, 6(1), 5-16. Borko, H. (1985). An introduction to computer-based library systems (Lucy A. Tedd). Education for Information, 3(1), 61.

Reservoir sampling

Reservoir sampling is a family of randomized algorithms for choosing a simple random sample, without replacement, of k items from a population of unknown size n in a single pass over the items. The size of the population n is not known to the algorithm and is typically too large for all n items to fit into main memory. The population is revealed to the algorithm over time, and the algorithm cannot look back at previous items. At any point, the current state of the algorithm must permit extraction of a simple random sample without replacement of size k over the part of the population seen so far. == Motivation == Suppose we see a sequence of items, one at a time. We want to keep 10 items in memory, and we want them to be selected at random from the sequence. If we know the total number of items n and can access the items arbitrarily, then the solution is easy: select 10 distinct indices i between 1 and n with equal probability, and keep the i-th elements. The problem is that we do not always know the exact n in advance. == Simple: Algorithm R == A simple and popular but slow algorithm, Algorithm R, was created by Jeffrey Vitter. Initialize an array R {\displaystyle R} indexed from 1 {\displaystyle 1} to k {\displaystyle k} , containing the first k items of the input x 1 , . . . , x k {\displaystyle x_{1},...,x_{k}} . This is the reservoir. For each new input x i {\displaystyle x_{i}} , generate a random number j uniformly in { 1 , . . . , i } {\displaystyle \{1,...,i\}} . If j ∈ { 1 , . . . , k } {\displaystyle j\in \{1,...,k\}} , then set R [ j ] := x i . {\displaystyle R[j]:=x_{i}.} Otherwise, discard x i {\displaystyle x_{i}} . Return R {\displaystyle R} after all inputs are processed. This algorithm works by induction on i ≥ k {\displaystyle i\geq k} . While conceptually simple and easy to understand, this algorithm needs to generate a random number for each item of the input, including the items that are discarded. The algorithm's asymptotic running time is thus O ( n ) {\displaystyle O(n)} . Generating this amount of randomness and the linear run time causes the algorithm to be unnecessarily slow if the input population is large. This is Algorithm R, implemented as follows: == Optimal: Algorithm L == If we generate n {\displaystyle n} random numbers u 1 , . . . , u n ∼ U [ 0 , 1 ] {\displaystyle u_{1},...,u_{n}\sim U[0,1]} independently, then the indices of the smallest k {\displaystyle k} of them is a uniform sample of the k {\displaystyle k} -subsets of { 1 , . . . , n } {\displaystyle \{1,...,n\}} . The process can be done without knowing n {\displaystyle n} : Keep the smallest k {\displaystyle k} of u 1 , . . . , u i {\displaystyle u_{1},...,u_{i}} that has been seen so far, as well as w i {\displaystyle w_{i}} , the index of the largest among them. For each new u i + 1 {\displaystyle u_{i+1}} , compare it with u w i {\displaystyle u_{w_{i}}} . If u i + 1 < u w i {\displaystyle u_{i+1}

VMDS

VMDS abbreviates the relational database technology called Version Managed Data Store provided by GE Energy as part of its Smallworld technology platform and was designed from the outset to store and analyse the highly complex spatial and topological networks typically used by enterprise utilities such as power distribution and telecommunications. VMDS was originally introduced in 1990 as has been improved and updated over the years. Its current version is 6.0. VMDS has been designed as a spatial database. This gives VMDS a number of distinctive characteristics when compared to conventional attribute only relational databases. == Distributed server processing == VMDS is composed of two parts: a simple, highly scalable data block server called SWMFS (Smallworld Master File Server) and an intelligent client API written in C and Magik. Spatial and attribute data are stored in data blocks that reside in special files called data store files on the server. When the client application requests data it has sufficient intelligence to work out the optimum set of data blocks that are required. This request is then made to SWMFS which returns the data to the client via the network for processing. This approach is particularly efficient and scalable when dealing with spatial and topological data which tends to flow in larger volumes and require more processing then plain attribute data (for example during a map redraw operation). This approach makes VMDS well suited to enterprise deployment that might involve hundreds or even thousands of concurrent clients. == Support for long transactions == Relational databases support short transactions in which changes to data are relatively small and are brief in terms in duration (the maximum period between the start and the end of a transaction is typically a few seconds or less). VMDS supports long transactions in which the volume of data involved in the transaction can be substantial and the duration of the transaction can be significant (days, weeks or even months). These types of transaction are common in advanced network applications used by, for example, power distribution utilities. Due to the time span of a long transaction in this context the amount of change can be significant (not only within the scope of the transaction, but also within the context of the database as a whole). Accordingly, it is likely that the same record might be changed more than once. To cope with this scenario VMDS has inbuilt support for automatically managing such conflicts and allows applications to review changes and accept only those edits that are correct. == Spatial and topological capabilities == As well as conventional relational database features such as attribute querying, join fields, triggers and calculated fields, VMDS has numerous spatial and topological capabilities. This allows spatial data such as points, texts, polylines, polygons and raster data to be stored and analysed. Spatial functions include: find all features within a polygon, calculate the Voronoi polygons of a set of sites and perform a cluster analysis on a set of points. Vector spatial data such as points, polylines and polygons can be given topological attributes that allow complex networks to be modelled. Network analysis engines are provided to answer questions such as find the shortest path between two nodes or how to optimize a delivery route (the travelling salesman problem). A topology engine can be configured with a set of rules that define how topological entities interact with each other when new data is added or existing data edited. == Data abstraction == In VMDS all data is presented to the application as objects. This is different from many relational databases that present the data as rows from a table or query result using say JDBC. VMDS provides a data modelling tool and underlying infrastructure as part of the Smallworld technology platform that allows administrators to associate a table in the database with a Magik exemplar (or class). Magik get and set methods for the Magik exemplar can be automatically generated that expose a table's field (or column). Each VMDS row manifests itself to the application as an instance of a Magik object and is known as an RWO (or real world object). Tables are known as collections in Smallworld parlance. # all_rwos hold all the rwos in the database and is heterogeneous all_rwos << my_application.rwo_set() # valve_collection holds the valve collection valves << all_rwos.select(:collection, {:valve}) number_of_valves << valves.size Queries are built up using predicate objects: # find 'open' valves. open_valves << valves.select(predicate.eq(:operating_status, "open")) number_of_open_valves << open_valves.size _for valve _over open_valves.elements() _loop write(valve.id) _endloop Joins are implemented as methods on the parent RWO. For example, a manager might have several employees who report to him: # get the employee collection. employees << my_application.database.collection(:gis, :employees) # find a manager called 'Steve' and get the first matching element steve << employees.select(predicate.eq(:name, "Steve").and(predicate.eq(:role, "manager")).an_element() # display the names of his direct reports. name is a field (or column) # on the employee collection (or table) _for employee _over steve.direct_reports.elements() _loop write(employee.name) _endloop Performing a transaction: # each key in the hash table corresponds to the name of the field (or column) in # the collection (or table) valve_data << hash_table.new_with( :asset_id, 57648576, :material, "Iron") # get the valve collection directly valve_collection << my_application.database.collection(:gis, :valve) # create an insert transaction to insert a new valve record into the collection a # comment can be provide that describes the transaction transaction << record_transaction.new_insert(valve_collection, valve_data, "Inserted a new valve") transaction.run()

Networked Help Desk

Networked Help Desk is an open standard initiative to provide a common API for sharing customer support tickets between separate instances of issue tracking, bug tracking, customer relationship management (CRM) and project management systems to improve customer service and reduce vendor lock-in. The initiative was created by Zendesk in June 2011 in collaboration with eight other founding member organizations including Atlassian, New Relic, OTRS, Pivotal Tracker, ServiceNow and SugarCRM. The first integration, between Zendesk and Atlassian's issue tracking product, Jira, was announced at the 2011 Atlassian Summit. By August 2011, 34 member companies had joined the initiative. A year after launching, over 50 organizations had joined. Within Zendesk instances this feature is branded as ticket sharing. == Basis == Support tools are generally built around a common paradigm that begins with a customer making a request or an incident report, these create a ticket. Each ticket has a progress status and is updated with annotations and attachments. These annotations and attachments may be visible to the customer (public), or only visible to analysts (private). Customers are notified of progress made on their ticket until it is complete. If the people necessary to complete a ticket are using separate support tools, additional overhead is introduced in maintaining the relevant information in the ticket in each tool while notifying the customer of progress made by each group in completing their ticket. For example, if a customer support issue is caused by a software bug and reported to a help desk using one system, and then the fix is documented by the developers in another, and analyzed in a customer relationship management tool, keeping the records in each system up-to-date and notifying the customer manually using a swivel chair approach is unnecessarily time-consuming and error-prone. If information is not transferred correctly, a customer may have to re-explain their problem each time their ticket is transferred. For systems with the Networked Help Desk API implemented, it is possible for several different applications related to a customer's support experience to synchronize data in one uniquely identified shared ticket. While many applications in these domains have implemented APIs that allow data to be imported, exported and modified, Network Help Desk provide a common standard for customer support information to automatically synchronize between several systems. Once implemented, two systems can quickly share tickets with just a configuration change as they both understand the same interface. Communication between two instances on a specific ticket occurs in three steps, an invitation agreement, sharing of ticket data and continued synchronization of tickets. The standard allows for "full delegation" (analysts in both systems each make public and private comments and synchronize status) as well as "partial delegation" where the instance receiving the ticket can only make private comments and status changes are not synchronized. Tickets may be shared with multiple instances. == Implementation list ==

Encyclopaedistics

Encyclopaedistics or encyclopaedics as a discipline, is the academic scholarship of encyclopedias as sources of encyclopedic knowledge and cultural objects as well; in this sense, this discipline is also known as "encyclopaedia studies" and can be termed as "theoretical encyclopaediography" by analogy with theoretical lexicography. Encyclopaedistics as a practical activity (profession or business) also called "encyclopaedic practice" or "encyclopedism" is the process of assembling encyclopaedias available to the public for sale or for free (encyclopaedia publishing or practical encyclopediography). In this sense, it is the art or craft of writing, compiling, and editing the paper or online encyclopedias. As a practical activity, encyclopaedistics originated in the Middle Ages in connection with the development of compendiums based on alphabetical structuring (e.g. first edition of Polyanthea by Dominicus Nanus Mirabellius). Encyclopaedistics is often defined as "the art and science of selecting and disseminating the information most significant to mankind". == Field of study == Encyclopaedistics is a specialized aspect of information science and communication science. At the same time, encyclopaedistics is also considered as one of scholarly disciplines which are seen as auxiliary for historical research (auxiliary sciences of history) . Third, encyclopaedics is a domain of philosophy (Romanticism). This term associated with German philosophers of the 18th century, such as Novalis, Friedrich Schlegel, who sought to create a "Scientific Bible" - both real and ideal book as the quintessence of human education (enlightenment). In any case, the most popular topics in encyclopaedia studies refferd the history of organization of encyclopaedic knowledge, encyclopaedic knowledge determination and selection, glossary composition, current state of development of encyclopaedic activity, features of making encyclopaedias and encyclopaedic articles, usage, role and significance of encyclopaedias, typology of encyclopaedic literature, encyclopaedists and encyclopaedic schools, opposition of classical encyclopaedias and Wikipedia as well as paper encyclopaedias and online encyclopaedias, case experience in building encyclopedias etc. In general, scholarly studies contribute to appearance of successful well-crafted encyclopaedias with high-quality articles. == Contemporary encyclopaedic practice == Today, academic institutions, universities, and publishing companies worldwide are engaged in encyclopaedic activity building national, multinational (universal), regional and subject-specific encyclopaedias, or doing studies related encyclopaedias. The development of national encyclopaedias is one of the prerogatives of the European Parliament in the policy of protection of accurate and verified information and in the fight against mis- and disinformation as well as in the policy of protecting, promoting and projecting Europe's values and interests in the world.