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National Cyber Security Policy 2013

National Cyber Security Policy is a policy framework by Department of Electronics and Information Technology (DeitY) It aims at protecting the public and private infrastructure from cyber attacks. The policy also intends to safeguard "information, such as personal information (of web users), financial and banking information and sovereign data". This was particularly relevant in the wake of US National Security Agency (NSA) leaks that suggested the US government agencies are spying on Indian users, who have no legal or technical safeguards against it. Ministry of Communications and Information Technology (India) defines Cyberspace as a complex environment consisting of interactions between people, software services supported by worldwide distribution of information and communication technology. == Reason for Cyber Security policies == India had no Cyber security policy before 2013. In 2013, The Hindu newspaper, citing documents leaked by NSA whistle-blower Edward Snowden, has alleged that much of the NSA surveillance was focused on India's domestic politics and its strategic and commercial interests. This sparked a furore among people. Under pressure, the government unveiled a National Cyber Security Policy 2013 on 2 July 2013. == Vision == To build a secure and resilient cyberspace for citizens, business, and government and also to protect anyone from intervening in user's privacy.It mentioned a five year target of training five lakh cyber security personnel by 2018. == Mission == To protect information and information infrastructure in cyberspace, build capabilities to prevent and respond to cyber threat, reduce vulnerabilities and minimize damage from cyber incidents through a combination of institutional structures, people, processes, technology, and cooperation. == Objective == Ministry of Communications and Information Technology (India) define objectives as follows: To create a secure cyber ecosystem in the country, generate adequate trust and confidence in IT system and transactions in cyberspace and thereby enhance adoption of IT in all sectors of the economy. To create an assurance framework for the design of security policies and promotion and enabling actions for compliance to global security standards and best practices by way of conformity assessment (Product, process, technology & people). To strengthen the Regulatory Framework for ensuring a SECURE CYBERSPACE ECOSYSTEM. To enhance and create National and Sectoral level 24x7 mechanism for obtaining strategic information regarding threats to ICT infrastructure, creating scenarios for response, resolution and crisis management through effective predictive, preventive, protective response and recovery actions. -To improve visibility of integrity of ICT products and services by establishing infrastructure for testing & validation of security of such product. To create workforce for 500,000 professionals skilled in next 5 years through capacity building skill development and training. To provide fiscal benefit to businesses for adoption of standard security practices and processes. To enable Protection of information while in process, handling, storage & transit so as to safeguard privacy of citizen's data and reducing economic losses due to cyber crime or data theft. To enable effective prevention, investigation and prosecution of cybercrime and enhancement of law enforcement capabilities through appropriate legislative intervention. == Strategies == Creating a secured Ecosystem. Creating an assurance framework. Encouraging Open Standards. Strengthening The regulatory Framework. Creating a mechanism for Security Threats Early Warning, Vulnerability management, and response to security threats. Securing E-Governance services. Protection and resilience of Critical Information Infrastructure. Promotion of Research and Development in cyber security. Reducing supply chain risks Human Resource Development (fostering education and training programs both in formal and informal sectors to Support the Nation's cyber security needs and build capacity. Creating cyber security awareness. Developing effective Public-Private partnerships. To develop bilateral and multilateral relationships in the area of cyber security with another country. (Information sharing and cooperation) a Prioritized approach for implementation.
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List of Haskell software and tools

This is a list of Haskell software and tools, including compilers, interpreters, build tools, package managers, integrated development environments, libraries, and other development utilities. == Compilers, interpreters and editors == Emacs — text editor Glasgow Haskell Compiler (GHC) Hugs — bytecode interpreter (discontinued) IntelliJ IDEA — IDE with Haskell support via plugins Vim — text editor Visual Studio Code — editor/IDE with Haskell support via extensions == Libraries and frameworks == Parsec — parser combinator library Servant — web framework Yesod — web framework == Build tools and package management == Cabal — build system and packaging infrastructure Haskell Platform — bundled distribution of Haskell tools and libraries (deprecated) Stack — build tool and dependency manager == Language tools and static analysis == Fourmolu — code formatter based on Ormolu Haskell Language Server — implementation of the Language Server Protocol for Haskell HLint — source code suggestion and linting tool Hoogle — Haskell API search engine Ormolu — code formatter Stan — static analysis tool Stylish Haskell — source code formatter == Interactive environments == GHCi — interactive REPL for the Glasgow Haskell Compiler IHaskell — Jupyter kernel for Haskell == Debugging and profiling tools == hp2ps — heap profiling visualization tool ThreadScope — parallel execution visualizer for Haskell programs == Documentation generators == Haddock — API documentation generator for Haskell == Parser and lexer generators == Alex — lexer generator for Haskell Happy — parser generator for Haskell == Testing frameworks == HUnit — unit testing framework QuickCheck — property-based testing library == Version control == Darcs — distributed version control system written in Haskell
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GNU social

GNU social (and its predecessor StatusNet) is a largely defunct free and open-source microblogging social networking service that implements the OStatus and ActivityPub standards for interoperability between installations. While offering similar functionality to social networks such as Twitter, GNU social seeks to provide the ability for open and federated communication between different microblogging communities, known as 'instances'. Both enterprises and individuals can install and control their own instances and user data. At its peak in popularity, GNU social had been deployed on hundreds of interconnected instances, however has since fallen into disuse as competing software like Mastodon and Pleroma have taken its position as the dominant federated microblogging services. Later on in its lifespan, the project split into two separate branches, with "v2" being a continuation of the original codebase for maintenance of existing instances, with "v3" being a complete redesign of the project meant to integrate further ActivityPub support and modernization of the user experience and its technological back-end. As of August 15, 2022, there had been no new commits to the v2 branch, with the v3 branch also no longer being actively developed not long after by November 25, 2022, with the project essentially abandoned. Despite its modern obsolescence and dated design compared to modern platforms, GNU social and StatusNet is regarded to be the origin of the Fediverse network and has had a major influence on the design of more modern decentralized social networks that succeeded it. == History == While being the main project within its lineage, GNU social originally began as a fork of StatusNet. The software was first developed for a service called identi.ca from Evan Prodromou, which offered free microblogging accounts to the public. The software quickly became one of the first popular examples of a decentralized social network, as identi.ca allowed any other server that was running the software to communicate with it, something which had not previously been attempted before in social media at such a large scale. === StatusNet === Originally, StatusNet (named Laconica at the time) was launched with a communication protocol designed specifically for the project called OpenMicroBlogging (OMB). With version 0.8.1, the name of the software was changed to StatusNet. Version 0.9.0 was released soon after in March 3, 2010, with the developers implementing a newly designed protocol dubbed OStatus, with support for OMB being dropped not long after. Compared to OpenMicroBlogging, OStatus could handle and federate more events and actions than the basic plaintext communication that OMB provided and was based on a variety of other web technologies, allowing for easier adoption of new implementations of the protocol for servers and clients compared to the fully custom architecture of OMB. With the StatusNet name change, the company developing both the software and OStatus as well as managing identi.ca rebranded from Control Yourself to StatusNet Inc. In August 2010, the company raised a new round of venture capital funds to establish a hosting service under the status.net domain from sources such as First Mark Capital, BOLDstart Ventures, iNovia Capital and Montreal Start Up, raising over $2.3 million in funding up to that point. The hosting service allowed anyone to establish their own StatusNet instance without maintaining a server, similar to WordPress.com and other blogging platforms. New registrations on identi.ca along with the ability to create new status.net instances was disabled in December 2012, in preparation for a migration to pump.io that has since been named by users of StatusNet and OStatus as "the Pumpocalypse". pump.io was a brand new software package like StatusNet, but with a new protocol designed for general purpose activity streams outside of microblogging and ease-of-use for developers building on the technology, much like the transition from OMB to OStatus. The announcement was seen as unexpected among identi.ca users, who were concerned about the possibility of their statuses being deleted with the transition. At the same time, server administrators running third-party instances and their users who were left behind on StatusNet were also worried, as it was unclear at the time whether future development of the software would be picked up by a new maintainer. The transition for identi.ca users to pump.io was completed on 12 July 2013. ==== Previous names ==== The original name of StatusNet was Laconica, a reference to the Laconic phrase; a particularly brief statement commonly attributed to the leaders of Sparta (Laconia being the Greek region containing Sparta). In microblogging, all messages are designed to be very short due to the traditional 140-character limit on message size, a limitation imported from SMS. Beginning with version 0.8.1, the name was changed to StatusNet. The developers said that the new name "simply reflects what our software does: send status updates into your social network." === GNU social === GNU social originally began as a side project of GNU FM (Libre.fm) maintainer Matt Lee, with the goal of being able to federate messages between Last.fm and other instances of GNU FM using StatusNet plugins. Around the same time, a developer named Mikael Nordfeldth forked StatusNet with the intention of maintaining it as a personal project, dubbing it "Free Social". However, following identi.ca's transition to pump.io and its developers' sudden abandonment of StatusNet, the projects received more attention from server administrators and other users looking for an actively updated alternative. Shortly after LibrePlanet 2012, a plan was formed to merge all three projects into a single service. On June 8, 2013, it was announced that along with Free Social, StatusNet would be merged into the GNU social project and stewarded by the Free Software Foundation, with the project since becoming the dominant variant of StatusNet. During GNU social's lifespan, a popular theme for the user interface named Quitter was used, which was similar to an earlier Twitter interface. Many instances were made specifically using the name Quitter such as Quitter.se, an instance created by the developer of the theme. Before the establishment of Mastodon's popularity and dominance within the network, Quitter was noted as a frequent location for users of Twitter to migrate to when users disagreed with moderation policies or feature updates, such as when an algorithmic feed was added to Twitter. A fork of GNU social was made called postActiv, which planned to rewrite the backend and user interface of GNU social, as well as to add compatibility for Diaspora's protocol. == Features == A basic GNU social instance takes the form of a microblogging service with a reverse chronological timeline that features status updates and small messages from followed accounts, similar to other services such as Twitter or Weibo. While users could see their own customized timeline, they could access another timeline that showcased every message that the instance knows of, including from other instances that were connected to each other if someone on the instance followed an account from it. Users could also create and join groups, which allows for discussion and collaboration on specific topics. Administrators can also customize their server via the plugin system, which allows developers to create new features or modify existing plugins to suit the needs of the instance via PHP. A notable plugin built for GNU social was Quitter, a revamp of the user interface that resembles an earlier version of Twitter's user interface.
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SwissCovid

SwissCovid is a COVID-19 contact tracing app used for digital contact tracing in Switzerland. Use of the app is voluntary and based on a decentralized approach using Bluetooth Low Energy and Decentralized Privacy-Preserving Proximity Tracing (dp3t). == Development == The app was developed in collaboration with the FOPH by Federal Office for Information Technology, Systems and Communications FOITT, École polytechnique fédérale de Lausanne (EPFL) and the Swiss Federal Institute of Technology in Zurich (ETH) as well as other experts. == Non-interoperability with applications in European countries == There is an agreement between EU countries to make applications compatible. However, there is no legal basis for the SwissCovid application to be part of this portal even though technically speaking it is ready, according to Sang-Ill Kim, head of the digital transformation department of the Federal Office of Public Health. == Criticism == === Not full open source and dependence on Google and Apple === In June 2020, researchers Serge Vaudenay and Martin Vuagnoux published a critical analysis of the application, noting that it relies heavily on Google and Apple's exposure notification system, which is integrated into their respective Android and iOS operating systems. Since Google and Apple have not released the full source code of this system, this would call into question the truly open source nature of the application. The researchers note that the dp3t collective, which includes the developers of the application, has asked Google and Apple to release their code. Moreover, they criticize the official description of the application and its functionalities, as well as the adequacy of the legal basis for its effective operation. === Cyber attacks === Professor Serge Vaudenay and Martin Vuagnoux identify also various security vulnerabilities in the application. The system would thus allow a third party to trace the movements of a phone using the application by means of Bluetooth sensors scattered along its path, for example in a building. Another possible attack would be to copy identifiers from the phones of people who may be ill (for example, in a hospital), and to reproduce those identifiers in order to receive notification of exposure to COVID-19 and illegitimately benefit from quarantine (thus entitling them to paid leave, a postponed examination, or other benefits). The system would also allow a third party to use a phone using the application by means of Bluetooth sensors scattered along the way. Paul-Olivier Dehaye of Personaldata.io and professor Joel Reardon of the University of Calgary published in June 2020 several examples of AEM (Associated Encrypted Metadata) replay and manipulation attacks via software development kits (SDKs) found in benign third-party mobile applications downloaded by the general public and having the phone's Bluetooth access permissions and in September 2020 a paper indicating that "Bluetooth-based proximity tracing apps are fundamentally insecure with respect to an attacker leveraging a malevolent app or SDK". === Costs === According to a publication by the federal administration, "the costs of developing the software for the mobile phone application, the GR back-end and the code management system as well as the costs for access management for the cantonal doctors' services are estimated at a one-off amount of 1.65 million francs. However, the Zurich-based company Ubique, responsible for the development of the application, was finally awarded the mandate to develop the application for an amount of 1.8 million francs. Through the Botnar Foundation based in Basel, École polytechnique fédérale de Lausanne received 3.5 million Swiss francs for the development of the application
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Reasoning model

A reasoning model, also known as a reasoning language model (RLM) or large reasoning model (LRM), is a type of large language model (LLM) that has been specifically trained to solve complex tasks requiring multiple steps of logical reasoning. These models demonstrate superior performance on logic, mathematics, and programming tasks compared to standard LLMs. They possess the ability to revisit and revise earlier reasoning steps and utilize additional computation during inference as a method to scale performance, complementing traditional scaling approaches based on training data size, model parameters, and training compute. == Overview == Unlike traditional language models that generate responses immediately, reasoning models allocate additional compute, or thinking, time before producing an answer to solve multi-step problems. OpenAI introduced this terminology in September 2024 when it released the o1 series, describing the models as designed to "spend more time thinking" before responding. The company framed o1 as a reset in model naming that targets complex tasks in science, coding, and mathematics, and it contrasted o1's performance with GPT-4o on benchmarks such as AIME and Codeforces. Independent reporting the same week summarized the launch and highlighted OpenAI's claim that o1 automates chain-of-thought style reasoning to achieve large gains on difficult exams. In operation, reasoning models generate internal chains of intermediate steps, then select and refine a final answer. OpenAI reported that o1's accuracy improves as the model is given more reinforcement learning during training and more test-time compute at inference. The company initially chose to hide raw chains and instead return a model-written summary, stating that it "decided not to show" the underlying thoughts so researchers could monitor them without exposing unaligned content to end users. Commercial deployments document separate "reasoning tokens" that meter hidden thinking and a control for "reasoning effort" that tunes how much compute the model uses. These features make the models slower than ordinary chat systems while enabling stronger performance on difficult problems. == History == The research trajectory toward reasoning models combined advances in supervision, prompting, and search-style inference. Early alignment work on reinforcement learning from human feedback showed that models can be fine-tuned to follow instructions with "human feedback" and preference-based rewards. In 2022, Google Research scientists Jason Wei and Denny Zhou showed that chain-of-thought prompting "significantly improves the ability" of large models on complex reasoning tasks. Input → Step 1 → Step 2 → ⋯ → Step n ⏟ Reasoning chain → Answer {\displaystyle {\text{Input}}\rightarrow \underbrace {{\text{Step}}_{1}\rightarrow {\text{Step}}_{2}\rightarrow \cdots \rightarrow {\text{Step}}_{n}} _{\text{Reasoning chain}}\rightarrow {\text{Answer}}} A companion result demonstrated that the simple instruction "Let's think step by step" can elicit zero-shot reasoning. Follow-up work introduced self-consistency decoding, which "boosts the performance" of chain-of-thought by sampling diverse solution paths and choosing the consensus, and tool-augmented methods such as ReAct, a portmanteau of Reason and Act, that prompt models to "generate both reasoning traces" and actions. Research then generalized chain-of-thought into search over multiple candidate plans. The Tree-of-Thoughts framework from Princeton computer scientist Shunyu Yao proposes that models "perform deliberate decision making" by exploring and backtracking over a tree of intermediate thoughts. OpenAI's reported breakthrough focused on supervising reasoning processes rather than only outcomes, with Lightman et al.'s "Let's Verify Step by Step" reporting that rewarding each correct step "significantly outperforms outcome supervision" on challenging math problems and improves interpretability by aligning the chain-of-thought with human judgment. OpenAI's o1 announcement ties these strands together with a large-scale reinforcement learning algorithm that trains the model to refine its own chain of thought, and it reports that accuracy rises with more training compute and more time spent thinking at inference. Together, these developments define the core of reasoning models. They use supervision signals that evaluate the quality of intermediate steps, they exploit inference-time exploration such as consensus or tree search, and they expose controls for how much internal thinking compute to allocate. OpenAI's o1 family made this approach available at scale in September 2024 and popularized the label "reasoning model" for LLMs that deliberately think before they answer. The development of reasoning models illustrates Richard S. Sutton's "bitter lesson" that scaling compute typically outperforms methods based on human-designed insights. This principle was demonstrated by researchers at the Generative AI Research Lab (GAIR), who initially attempted to replicate o1's capabilities using sophisticated methods including tree search and reinforcement learning in late 2024. Their findings, published in the "o1 Replication Journey" series, revealed that knowledge distillation, a comparatively straightforward technique that trains a smaller model to mimic o1's outputs, produced unexpectedly strong performance. This outcome illustrated how direct scaling approaches can, at times, outperform more complex engineering solutions. === Drawbacks === Reasoning models require significantly more computational resources during inference compared to non-reasoning models. Research on the American Invitational Mathematics Examination (AIME) benchmark found that reasoning models were 10 to 74 times more expensive to operate than their non-reasoning counterparts. The extended inference time is attributed to the detailed, step-by-step reasoning outputs that these models generate, which are typically much longer than responses from standard large language models that provide direct answers without showing their reasoning process. One researcher in early 2025 argued that these models may face potential additional denial-of-service concerns with "overthinking attacks." === Releases === ==== 2024 ==== In September 2024, OpenAI released o1-preview, a large language model with enhanced reasoning capabilities. The full version, o1, was released in December 2024. OpenAI initially shared preliminary results on its successor model, o3, in December 2024, with the full o3 model becoming available in 2025. Alibaba released reasoning versions of its Qwen large language models in November 2024. In December 2024, the company introduced QvQ-72B-Preview, an experimental visual reasoning model. In December 2024, Google introduced Deep Research in Gemini, a feature designed to conduct multi-step research tasks. On December 16, 2024, researchers demonstrated that by scaling test-time compute, a relatively small Llama 3B model could outperform a much larger Llama 70B model on challenging reasoning tasks. This experiment suggested that improved inference strategies can unlock reasoning capabilities even in smaller models. ==== 2025 ==== In January 2025, DeepSeek released R1, a reasoning model that achieved performance comparable to OpenAI's o1 at significantly lower computational cost. The release demonstrated the effectiveness of Group Relative Policy Optimization (GRPO), a reinforcement learning technique used to train the model. On January 25, 2025, DeepSeek enhanced R1 with web search capabilities, allowing the model to retrieve information from the internet while performing reasoning tasks. Research during this period further validated the effectiveness of knowledge distillation for creating reasoning models. The s1-32B model achieved strong performance through budget forcing and scaling methods, reinforcing findings that simpler training approaches can be highly effective for reasoning capabilities. On February 2, 2025, OpenAI released Deep Research, a feature powered by their o3 model that enables users to conduct comprehensive research tasks. The system generates detailed reports by automatically gathering and synthesizing information from multiple web sources. OpenAI called GPT-4.5 its "last non-chain-of-thought model", and implemented with GPT-5 a router model that selects a model based on the difficulty of the task. ==== 2026 ==== In January 2026, Moonshot AI released Kimi K2.5, an open-source 1 trillion parameter MoE model with 32 billion active parameters. It uses an “Agent Swarm” system that dynamically decomposes tasks into sub-agents for reasoning and execution, enabling more scalable multi-step problem solving than a single sequential reasoning chain. == Training == Reasoning models follow the familiar large-scale pretraining used for frontier language models, then diverge in the post-training and optimization. OpenAI reports that o1 is trained with a large-
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Mojito (framework)

Mojito is an environment agnostic, Model-View-Controller (MVC) web application framework. It was designed by Yahoo. == Features == Mojito supports agile development of web applications. Mojito has built-in support for unit testing, Internationalization, syntax and coding convention checks. Both server and client components are written in JavaScript. Mojito allows developers designing web applications to leverage the utilities of both configuration and MVC framework. Mojito is capable of running on both JavaScript-enabled web browsers and servers using Node.js because they both utilize JavaScript. Mojito applications mainly consist of two components: JSON Configuration files: these define relationships between code components, assets, routing paths, and framework defaults and are available at the application and mojit level. Directories: these reflect MVC architecture and are used to separate resources such as assets, libraries, middleware, etc. == Architecture == In Mojito, both server and "client" side scripting is done in JavaScript, allowing it to run on both client and server thereby breaking the "front-end back-end barrier." It has both client and server runtimes. === Server runtime === This block houses operations needed by server side components. Services include: Routing rules, HTTP Server, config loader and disk-based loader. === Client runtime === This block houses operations called upon while running client sides components. Services include local storage/cache access and JSON based /URL based loader === Core === Core function can be accessed on client or server. Services include Registry, Dispatcher, Front controller, Resource store. === Container === mojit object comes into the picture. This container also include the services used by mojits. API and Mojito services are the blocks which caters to services needed for execution of mojits. === API (Action Context) === Mojito services are a customizable service block. It offers mojits a range of services which might be needed by mojit to carry out certain actions. These services can be availed at both client and server side. Reusable services can be created and aggregated to the core here. == Mojits == Mojits are the modules of a Mojito application. An application consists of one or more mojits. A mojit encompasses a Model, Views and a Controller defined by JSON configuration files. It includes a View factory where views are created according to the model and a View cache that holds frequently requested views to aid performance. === Application Architecture === A Mojito application is a set of mojits facilitated by configurable JSON files which define the code for model, view and controller. This MVC structure works with API block and Mojito services, and can be deployed at both client and server side. While the application is deployed at client side, it can call server-side modules using binders. Binders are mojit codes that let mojits request services from each other. Mojit Proxy acts as an intermediary between binders and mojit's API (application context) block and other mojits. Controllers are command-issuing units of mojits. Models mirror the core logic and hold data. Applications can have multiple models. They can be centrally accessed from controllers. View files are created in accordance with controllers and models, and are marked-up before they are sent to users as output. === Application Directory Structure === Directory structure of a Mojito application with one mojit: [mojito_app]/ |-- application.json |-- assets/ | `-- favicon.icon |-- yui_modules/ | `-- .{affinity}.js |-- index.js |-- mojits/ | `-- [mojit_name | |-- assets/ | |-- yui_modules/ | | `-- .{affinity}.js | |-- binders/ | | `-- {view_name}.js | |-- controller.{affinity}.js | |-- defaults.json | |-- definition.json | |-- lang/ | | `-- {mojit_name}_{lang}.js | |-- models/ | | `-- {model_name}.{affinity}.js | |-- tests/ | | |-- yui_modules/ | | | `-- {module_name}.{affinity}-tests.js | | |-- controller.{affinity}-tests.js | | `-- models/ | | `-- {model_name}.{affinity}-tests.js | `-- views/ | |-- {view_name}.{view_engine}.html | `-- {view_name}.{device}.{view_engine}.html |-- package.json |-- routes.json (deprecated) |-- server.js == Model, View and Controller == The Model hosts data, which is accessed by the Controller and presented to the View. Controller also handles any client requests for data, in which case controller fetches data from the model and passes the data to the client. All three components are clustered in the mojit. Mojits are physically illustrated by directory structures and an application can have multiple mojits. Every mojit can have one controller, one or more views and zero or more models. === Model === The model it represents the application data and is independent of view or controller. Model contains code to manipulate the data. They are found in the models directory of each mojit. Functions include: Storing information for access by controller. Validation and error handling. Metadata required by the view === Controller === The controller acts like a connecting agent between model and view. It supplies input to Model and after fetching data from model, passes it to View. Functions include Redirection Monitors authentication Web safety Encoding === View === The view acts as a presentation filter by highlighting some model attributes and suppressing others. A view can be understood as a visual permutation of the model. The view renders data received from controller and displays it to the end user.
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Software component

A software component is a modular unit of software that encapsulates specific functionality. The desired characteristics of a component are reusability and maintainability. == Value == Components allow software developers to assemble software with reliable parts rather than writing code for every aspect. It makes implementation more like factory assembly than custom building. == Attributes == Desirable attributes of a component include but are not limited to: Cohesive – encapsulates related functionality Reusable Robust Substitutable – can be replaced by another component with the same interface Documented Tested == Third-party == Some components are built in-house by the same organization or team building the software system. Some are third-party, developed elsewhere and assembled into the software system. == Component-based software engineering == For large-scale systems, component-based development encourages a disciplined process to manage complexity. == Framework == Some components conform to a framework technology that allows them to be consumed in a well-known way. Examples include: CORBA, COM, Enterprise JavaBeans, and the .NET Framework. == Modeling == Component design is often modeled visually. In Unified Modeling Language (UML) 2.0 a component is shown as a rectangle, and an interface is shown as a lollipop to indicate a provided interface and as a socket to indicate consumption of an interface. == History == The idea of reusable software components was promoted by Douglas McIlroy in his presentation at the NATO Software Engineering Conference of 1968. (One goal of that conference was to resolve the so-called software crisis of the time.) In the 1970s, McIlroy put this idea into practice with the addition of the pipeline feature to the Unix operating system. Brad Cox refined the concept of a software component in the 1980s. He attempted to create an infrastructure and market for reusable third-party components by inventing the Objective-C programming language. IBM introduced System Object Model (SOM) in the early 1990s. Microsoft introduced Component Object Model (COM) in the early 1990s. Microsoft built many domain-specific component technologies on COM, including Distributed Component Object Model (DCOM), Object Linking and Embedding (OLE), and ActiveX.
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Color layout descriptor

In digital image and video processing, a color layout descriptor (CLD) is designed to capture the spatial distribution of color in an image. The feature extraction process consists of two parts: grid based representative color selection and discrete cosine transform with quantization. Color is the most basic quality of the visual contents, therefore it is possible to use colors to describe and represent an image. The MPEG-7 standard has tested the most efficient procedure to describe the color and has selected those that have provided more satisfactory results. This standard proposes different methods to obtain these descriptors, and one tool defined to describe the color is the CLD, that permits describing the color relation between sequences or group of images. The CLD captures the spatial layout of the representative colors on a grid superimposed on a region or image. Representation is based on coefficients of the discrete cosine transform (DCT). This is a very compact descriptor being highly efficient in fast browsing and search applications. It can be applied to still images as well as to video segments. == Definition == The CLD is a very compact and resolution-invariant representation of color for high-speed image retrieval and it has been designed to efficiently represent the spatial distribution of colors. This feature can be used for a wide variety of similarity-based retrieval, content filtering and visualization. It is especially useful for spatial structure-based retrieval applications. This descriptor is obtained by applying the DCT transformation on a 2-D array of local representative colors in Y or Cb or Cr color space. The functionalities of the CLD are basically the matching: – Image-to-image matching – Video clip-to-video clip matching Remark that the CLD is one of the most precise and fast color descriptor. == Extraction == The extraction process of this color descriptor consists of four stages: Image partitioning Representative color selection DCT transformation Zigzag scanning The standard MPEG-7 recommends using the YCbCr color space for the CLD. === Image partitioning === In the image partitioning stage, the input picture (on RGB color space) is divided into 64 blocks to guarantee the invariance to resolution or scale. The inputs and outputs of this step are summarized in the following table: === Representative color selection === After the image partitioning stage, a single representative color is selected from each block. Any method to select the representative color can be applied, but the standard recommends the use of the average of the pixel colors in a block as the corresponding representative color, since it is simpler and the description accuracy is sufficient in general. The selection results in a tiny image icon of size 8x8. The next figure shows this process. Note that in the image of the figure, the resolution of the original image has been maintained only in order to facilitate its representation. The inputs and outputs of this stage are summarized in the next table: Once the tiny image icon is obtained, the color space conversion between RGB and YCbCr is applied. === DCT transformation === In the fourth stage, the luminance (Y) and the blue and red chrominance (Cb and Cr) are transformed by 8x8 DCT, so three sets of 64 DCT coefficients are obtained. To calculate the DCT in a 2D array, the formulas below are used. B p q = α p α q ∑ m = 0 M − 1 ∑ n = 0 N − 1 A m n cos ⁡ π ( 2 m + 1 ) p 2 M cos ⁡ π ( 2 n + 1 ) q 2 N , 0 ≤ p ≤ M − 1 , 0 ≤ q ≤ N − 1 {\displaystyle B_{pq}=\alpha _{p}\alpha _{q}\sum _{m=0}^{M-1}\sum _{n=0}^{N-1}A_{mn}\cos {\frac {\pi (2m+1)p}{2M}}\cos {\frac {\pi (2n+1)q}{2N}},\qquad 0\leq p\leq M-1,\;0\leq q\leq N-1} α p = { 1 M , p = 0 2 M , 1 ≤ p ≤ M − 1 α q = { 1 N , q = 0 2 N , 1 ≤ q ≤ N − 1 {\displaystyle \alpha _{p}={\begin{cases}{\frac {1}{\sqrt {M}}},&p=0\\{\sqrt {\frac {2}{M}}},&1\leq p\leq M-1\end{cases}}\qquad \alpha _{q}={\begin{cases}{\frac {1}{\sqrt {N}}},&q=0\\{\sqrt {\frac {2}{N}}},&1\leq q\leq N-1\end{cases}}} The inputs and outputs of this stage are summarized in the next table: === Zigzag scanning === A zigzag scanning is performed with these three sets of 64 DCT coefficients, following the schema presented in the figure. The purpose of the zigzag scan is to group the low frequency coefficients of the 8x8 matrix into a vector. The inputs and outputs of this stage are summarized in the next table: Finally, these three set of matrices correspond to the CLD of the input image. == Matching == The matching process helps to evaluate if two elements are equal comparing both elements and calculating the distance between them. In the case of color descriptors the matching process helps to evaluate if two images are similar. Its procedure is the following: – Given an image as an input, the application attempts to find an image with a similar descriptor in a data base of images. If we consider two CLDs: {DY, DCb, DCr} { DY‟, DCb‟, DCr‟ }, The distance between the two descriptors can be computed as: D = ∑ i w y i ( D Y i − D Y i ′ ) 2 + ∑ i w b i ( D C b i − D C b i ′ ) 2 + ∑ i w r i ( D C r i − D C r i ′ ) 2 {\displaystyle D={\sqrt {\sum _{i}w_{yi}(DY_{i}-DY_{i}')^{2}}}+{\sqrt {\sum _{i}w_{bi}(DCb_{i}-DCb_{i}')^{2}}}+{\sqrt {\sum _{i}w_{ri}(DCr_{i}-DCr_{i}')^{2}}}} The subscript i represents the zigzag-scanning order of the coefficients. Furthermore, notice that is possible to weight the coefficients (w) in order to adjust the performance of the matching process. These weights let us give to some components of the descriptor more importance than others. Observing the formula, it can be extracted that: – 2 images are the same if the distance is 0 – 2 images are similar if the distance is near to 0 Therefore, this matching process will let to identify images with similar color descriptors. Since the complexity of the similarity matching process shown above is low, high-speed image matching can be achieved.
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Content Threat Removal

Content Threat Removal (CTR) is a cybersecurity technology intended to defeat the threat posed by handling digital content in the cyberspace. Unlike other defenses, including antivirus software and sandboxed execution, CTR does not rely on being able to detect threats. Similar to Content Disarm and Reconstruction, CTR is designed to remove the threat without knowing whether it has done so and acts without knowing if data contains a threat or not. Detection strategies work by detecting unsafe content, and then blocking or removing that content. Content that is deemed safe is delivered to its destination. In contrast, Content Threat Removal assumes all data is hostile and delivers none of it to the destination, regardless of whether it is actually hostile. Although no data is delivered, the business information carried by the data is delivered using new data created for the purpose. == Threat == Advanced attacks continuously defeat defenses that are based on detection. These are often referred to as zero-day attacks, because as soon as they are discovered attack detection mechanisms must be updated to identify and neutralize the attack, and until they are, all systems are unprotected. These attacks succeed because attackers find new ways of evading detection. Polymorphic code can be used to evade the detection of known unsafe data and sandbox detection allows attacks to evade dynamic analysis. == Method == A Content Threat Removal defence works by intercepting data on its way to its destination. The business information carried by the data is extracted and the data is discarded. Then entirely new, clean and safe data is built to carry the information to its destination. The effect of building new data to carry the business information is that any unsafe elements of the original data are left behind and discarded. This includes executable data, macros, scripts and malformed data that trigger vulnerabilities in applications. While CTR is a form of content transformation, not all transformations provide a complete defence against the content threat. == Applicability == CTR is applicable to user-to-user traffic, such as email and chat, and machine-to-machine traffic, such as web services. Data transfers can be intercepted by in-line application layer proxies and these can transform the way information content is delivered to remove any threat. CTR works by extracting business information from data and it is not possible to extract information from executable code. This means CTR is not directly applicable to web browsing, since most web pages are code. It can, however, be applied to content that is downloaded from, and uploaded to, websites. Although most web pages cannot be transformed to render them safe, web browsing can be isolated and the remote access protocols used to reach the isolated environment can be subjected to CTR. CTR provides a solution to the problem of stegware. It naturally removes detectable steganography and eliminates symbiotic and permutation steganography through normalisation.
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TinEye

TinEye is a reverse image search engine developed and offered by Idée, Inc., a company based in Toronto, Ontario, Canada. It was the first image search engine on the web to use image identification technology rather than keywords, metadata or watermarks. TinEye allows users to search not using keywords but with images. Upon submitting an image, TinEye creates a "unique and compact digital signature or fingerprint" of the image and matches it with other indexed images. This procedure is able to match even heavily edited versions of the submitted image, but will not usually return similar images in the results. == History == Idée, Inc. was founded by Leila Boujnane and Paul Bloore in 1999. Idée launched the service on May 6, 2008 and went into open beta in August that year. While computer vision and image identification research projects began as early as the 1980s, the company claims that TinEye is the first web-based image search engine to use image identification technology. The service was created with copyright owners and brand marketers as the intended user base, to look up unauthorized use and track where the brands are showing up respectively. In June 2014, TinEye claimed to have indexed more than five billion images for comparisons. However, this is a relatively small proportion of the total number of images available on the World Wide Web. As of September 2025, TinEye's search results claim to have over 77.6 billion images indexed for comparison. == Technology == A user uploads an image to the search engine (the upload size is limited to 20 MB) or provides a URL for an image or for a page containing the image. The search engine will look up other usage of the image in the internet, including modified images based upon that image, and report the date and time at which they were posted. TinEye does not recognize outlines of objects or perform facial recognition, but recognizes the entire image, and some altered versions of that image. This includes smaller, larger, and cropped versions of the image. TinEye has shown itself capable of retrieving different images from its database of the same subject, such as famous landmarks. TinEye is capable of searching for images in JPEG, PNG, WebP, GIF, BMP and TIFF format. Results generated from TinEye include the total number of matches in their database, a preview image, and the URL to each match. TinEye can sort results by best match, most changed, biggest image, newest, and oldest. User registration is optional and offers storage of the user's previous queries. Other features include embeddable widgets and bookmarklets. TinEye has also released their commercial API. == Usage == TinEye's ability to search the web for specific images (and modifications of those images) makes it a potential tool for the copyright holders of visual works to locate infringements on their copyright. It also creates a possible avenue for people who are looking to make use of imagery under orphan works to find the copyright holders of that imagery. Being that orphan works can be defined as "copyrighted works whose owners are difficult or impossible to identify and/or locate," the use of TinEye could potentially remove the orphan work status from online images that can be found in its database. === Fact-checking === It has been recommended by fact-checkers as a useful resource in attempts to verify the origin of images. As of 2019, TinEye specialized in copyright violations and finding exact versions of images online.
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Situational application

In computing, a situational application is "good enough" software created for a narrow group of users with a unique set of needs. The application typically (but not always) has a short life span, and is often created within the group where it is used, sometimes by the users themselves. As the requirements of a small team using the application change, the situational application often also continues to evolve to accommodate these changes. Although situational applications are specifically designed to embrace change, significant changes in requirements may lead to an abandonment of the situational application altogether – in some cases it is just easier to develop a new one than to evolve the one in use. == Characteristics == Situational applications are developed fast, easy to use, uncomplicated, and serve a unique set of requirements. They have a narrow focus on a specific business problem, and they are written in a way where if the business problem changes rapidly, so can the situational application. This contrasts with more common enterprise applications, which are designed to address a large set of business problems, require meticulous planning, and impose a sometimes-slow and often-meticulous change process. == Origination == Clay Shirky in his essay entitled "Situated Software" described a type of software that "...is designed for use by a specific social group, rather than for a generic set of "users"." IBM later morphed the term into "situational applications". == Evolution == The successful large-scale implementation of a situational application environment in an organization requires a strategy, mindset, methodology and support structure quite different from traditional application development. This is now evolving as more companies learn how to best leverage the ideas behind situational applications. In addition, the advent of cloud-based application development and deployment platforms makes the implementation of a comprehensive situational application environment much more feasible. == Examples == A structured wiki that can host wiki applications lends itself to creation of situational applications. Some mashups can also be considered situational applications. A forms application such as a Microsoft Access Database (MDB file) can be considered a situational application. The latest implementations of situational application environments include Longjump, Force.com and WorkXpress.
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Quickly (software)

Quickly is a framework for creating software programs for a Linux distribution using Python, PyGTK, Glade Interface Designer and Desktop Couch. It then allows for easy publishing using bzr and Launchpad. Quickly is designed to speed up the start of new projects with the use of templates, not only for programs but for any type of project. These templates are used to automate project configuration and maintenance. Delegating into templates and not into a specific library allows projects created using Quickly not to require dependencies on any particular library or runtime of Quickly itself. The project was started by Rick Spencer after his frustration as a beginner Ubuntu developer. == Updates == Last available software update is on 2013-01-31 for Ubuntu 11.04.
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Stanza Living

Stanza Living is the common brand name for Dtwelve Spaces Private Limited. It provides fully-managed shared living accommodations to students and young professionals. Founded by Anindya Dutta and Sandeep Dalmia, the company is present across 23 cities including Delhi, NCR, Bangalore, Visakhapatnam, Hyderabad, Chennai, Coimbatore, Indore, Pune, Baroda, Vijayawada, and Dehradun, Kota in India, with a capacity of 70,000 beds. Stanza Living is a technology-enabled housing concept which provides fully-furnished residences with amenities like meals, internet, laundry services, housekeeping, security and community engagement programmes. The company has an asset-light business model under which it engages in long-term lease agreements with property owners/developers, who convert their assets into shared living residences as per company guidelines. These assets are subsequently operated by Stanza Living. == Industry background == A report by Cushman & Wakefield (C&W) titled 'Exploring the Student Housing Universe in India City Insights', estimates that there were over 9.08 million migrant student enrolments in India's higher educational institutions (HEIs) for the year 2018-19 who need quality accommodation facilities. According to the report, Delhi-NCR, Mumbai, and Pune are the three biggest markets for student housing in the country, and these cities require an additional 4.75 lakh beds from organized co-living operators to meet the current demand. == History == Stanza Living provides tech-enabled, fully managed community living facilities for students and working professionals. The company was launched as a student housing business in Delhi NCR with a capacity of 100 beds, and grew to 14 cities by 2019. By early 2020, the company began catering to working professionals as well. The company has a combined inventory of 70,000 beds under management for both students and working professionals. Stanza Living is currently valued at $300 million. It has raised a capital of about $70 million from leading global investors like Falcon Edge Capital, Sequoia Capital, Matrix Partners and Accel Partners. November 2017 – Seed funding, September 2018 – Series A, March 2019 – Debt financing, July 2019 – Series C round, December 2019 - Debt financing. The company has invested in building technology products for business efficiency and consumer experience, like the Stanza Resident App and Stanza Real Estate App. Stanza Living has close to 1,500 employees across India. It is recognized among Top Real Estate Tech Startups of 2020 across the globe by research and analysis company Tracxn. The company has been shortlisted among Top 25 Start-ups of India in 2019 by LinkedIn == Founders == Stanza Living was co-founded by Anindya Dutta and Sandeep Dalmia. Sandeep Dalmia is an alumnus of Delhi College of Engineering and IIM Ahmedabad. Prior to Stanza, he was a Principal at Boston Consulting Group, working across India, US and South East Asia markets. Anindya Dutta was previously a Real Estate investor with Oaktree Capital and prior to that, he worked at Goldman Sachs in London. He is an alumnus of IIT Kharagpur and IIM Ahmedabad.
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View model

A view model or viewpoints framework in systems engineering, software engineering, and enterprise engineering is a framework which defines a coherent set of views to be used in the construction of a system architecture, software architecture, or enterprise architecture. A view is a representation of the whole system from the perspective of a related set of concerns. Since the early 1990s there have been a number of efforts to prescribe approaches for describing and analyzing system architectures. A result of these efforts have been to define a set of views (or viewpoints). They are sometimes referred to as architecture frameworks or enterprise architecture frameworks, but are usually called "view models". Usually a view is a work product that presents specific architecture data for a given system. However, the same term is sometimes used to refer to a view definition, including the particular viewpoint and the corresponding guidance that defines each concrete view. The term view model is related to view definitions. == Overview == The purpose of views and viewpoints is to enable humans to comprehend very complex systems, to organize the elements of the problem and the solution around domains of expertise and to separate concerns. In the engineering of physically intensive systems, viewpoints often correspond to capabilities and responsibilities within the engineering organization. Most complex system specifications are so extensive that no single individual can fully comprehend all aspects of the specifications. Furthermore, we all have different interests in a given system and different reasons for examining the system's specifications. A business executive will ask different questions of a system make-up than would a system implementer. The concept of viewpoints framework, therefore, is to provide separate viewpoints into the specification of a given complex system in order to facilitate communication with the stakeholders. Each viewpoint satisfies an audience with interest in a particular set of aspects of the system. Each viewpoint may use a specific viewpoint language that optimizes the vocabulary and presentation for the audience of that viewpoint. Viewpoint modeling has become an effective approach for dealing with the inherent complexity of large distributed systems. Architecture description practices, as described in IEEE Std 1471-2000, utilize multiple views to address several areas of concerns, each one focusing on a specific aspect of the system. Examples of architecture frameworks using multiple views include Kruchten's "4+1" view model, the Zachman Framework, TOGAF, DoDAF, and RM-ODP. == History == In the 1970s, methods began to appear in software engineering for modeling with multiple views. Douglas T. Ross and K.E. Schoman in 1977 introduce the constructs context, viewpoint, and vantage point to organize the modeling process in systems requirements definition. According to Ross and Schoman, a viewpoint "makes clear what aspects are considered relevant to achieving ... the overall purpose [of the model]" and determines How do we look at [a subject being modelled]? As examples of viewpoints, the paper offers: Technical, Operational and Economic viewpoints. In 1992, Anthony Finkelstein and others published a very important paper on viewpoints. In that work: "A viewpoint can be thought of as a combination of the idea of an “actor”, “knowledge source”, “role” or “agent” in the development process and the idea of a “view” or “perspective” which an actor maintains." An important idea in this paper was to distinguish "a representation style, the scheme and notation by which the viewpoint expresses what it can see" and "a specification, the statements expressed in the viewpoint's style describing particular domains". Subsequent work, such as IEEE 1471, preserved this distinction by utilizing two separate terms: viewpoint and view, respectively. Since the early 1990s there have been a number of efforts to codify approaches for describing and analyzing system architectures. These are often termed architecture frameworks or sometimes viewpoint sets. Many of these have been funded by the United States Department of Defense, but some have sprung from international or national efforts in ISO or the IEEE. Among these, the IEEE Recommended Practice for Architectural Description of Software-Intensive Systems (IEEE Std 1471-2000) established useful definitions of view, viewpoint, stakeholder and concern and guidelines for documenting a system architecture through the use of multiple views by applying viewpoints to address stakeholder concerns. The advantage of multiple views is that hidden requirements and stakeholder disagreements can be discovered more readily. However, studies show that in practice, the added complexity of reconciling multiple views can undermine this advantage. IEEE 1471 (now ISO/IEC/IEEE 42010:2011, Systems and software engineering — Architecture description) prescribes the contents of architecture descriptions and describes their creation and use under a number of scenarios, including precedented and unprecedented design, evolutionary design, and capture of design of existing systems. In all of these scenarios the overall process is the same: identify stakeholders, elicit concerns, identify a set of viewpoints to be used, and then apply these viewpoint specifications to develop the set of views relevant to the system of interest. Rather than define a particular set of viewpoints, the standard provides uniform mechanisms and requirements for architects and organizations to define their own viewpoints. In 1996 the ISO Reference Model for Open Distributed Processing (RM-ODP) was published to provide a useful framework for describing the architecture and design of large-scale distributed systems. == View model topics == === View === A view of a system is a representation of the system from the perspective of a viewpoint. This viewpoint on a system involves a perspective focusing on specific concerns regarding the system, which suppresses details to provide a simplified model having only those elements related to the concerns of the viewpoint. For example, a security viewpoint focuses on security concerns and a security viewpoint model contains those elements that are related to security from a more general model of a system. A view allows a user to examine a portion of a particular interest area. For example, an Information View may present all functions, organizations, technology, etc. that use a particular piece of information, while the Organizational View may present all functions, technology, and information of concern to a particular organization. In the Zachman Framework views comprise a group of work products whose development requires a particular analytical and technical expertise because they focus on either the “what,” “how,” “who,” “where,” “when,” or “why” of the enterprise. For example, Functional View work products answer the question “how is the mission carried out?” They are most easily developed by experts in functional decomposition using process and activity modeling. They show the enterprise from the point of view of functions. They also may show organizational and information components, but only as they relate to functions. === Viewpoints === In systems engineering, a viewpoint is a partitioning or restriction of concerns in a system. Adoption of a viewpoint is usable so that issues in those aspects can be addressed separately. A good selection of viewpoints also partitions the design of the system into specific areas of expertise. Viewpoints provide the conventions, rules, and languages for constructing, presenting and analysing views. In ISO/IEC 42010:2007 (IEEE-Std-1471-2000) a viewpoint is a specification for an individual view. A view is a representation of a whole system from the perspective of a viewpoint. A view may consist of one or more architectural models. Each such architectural model is developed using the methods established by its associated architectural system, as well as for the system as a whole. === Modeling perspectives === Modeling perspectives is a set of different ways to represent pre-selected aspects of a system. Each perspective has a different focus, conceptualization, dedication and visualization of what the model is representing. In information systems, the traditional way to divide modeling perspectives is to distinguish the structural, functional and behavioral/processual perspectives. This together with rule, object, communication and actor and role perspectives is one way of classifying modeling approaches === Viewpoint model === In any given viewpoint, it is possible to make a model of the system that contains only the objects that are visible from that viewpoint, but also captures all of the objects, relationships and constraints that are present in the system and relevant to that viewpoint. Such a model is said to be a viewpoint model, or a view of the
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Comparison gallery of image scaling algorithms

This gallery shows the results of numerous image scaling algorithms. == Scaling methods == An image size can be changed in several ways. Consider resizing a 160x160 pixel photo to the following 40x40 pixel thumbnail and then scaling the thumbnail to a 160x160 pixel image. Also consider doubling the size of the following image containing text. == Examples of enlarged images == Below are examples of various images enlarged 4x using each scaling algorithm.
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