Himmat is a women's safety mobile application of Delhi Police. It was launched by Home Minister Rajnath Singh on 1 January 2015. The app is freely available for Android mobile phones and can be downloaded from Delhi Police website. Delhi Police plans to launch app for other platforms in future. Low registrations and other problems resulted in a parliamentary panel calling the app a failure in 2018. Himmat has gone on to be called as one of India's best safety apps for women.
Shape context
Shape context is a feature descriptor used in object recognition. Serge Belongie and Jitendra Malik proposed the term in their paper "Matching with Shape Contexts" in 2000. == Theory == The shape context is intended to be a way of describing shapes that allows for measuring shape similarity and the recovering of point correspondences. The basic idea is to pick n points on the contours of a shape. For each point pi on the shape, consider the n − 1 vectors obtained by connecting pi to all other points. The set of all these vectors is a rich description of the shape localized at that point but is far too detailed. The key idea is that the distribution over relative positions is a robust, compact, and highly discriminative descriptor. So, for the point pi, the coarse histogram of the relative coordinates of the remaining n − 1 points, h i ( k ) = # { q ≠ p i : ( q − p i ) ∈ bin ( k ) } {\displaystyle h_{i}(k)=\#\{q\neq p_{i}:(q-p_{i})\in {\mbox{bin}}(k)\}} is defined to be the shape context of p i {\displaystyle p_{i}} . The bins are normally taken to be uniform in log-polar space. The fact that the shape context is a rich and discriminative descriptor can be seen in the figure below, in which the shape contexts of two different versions of the letter "A" are shown. (a) and (b) are the sampled edge points of the two shapes. (c) is the diagram of the log-polar bins used to compute the shape context. (d) is the shape context for the point marked with a circle in (a), (e) is that for the point marked as a diamond in (b), and (f) is that for the triangle. As can be seen, since (d) and (e) are the shape contexts for two closely related points, they are quite similar, while the shape context in (f) is very different. For a feature descriptor to be useful, it needs to have certain invariances. In particular it needs to be invariant to translation, scaling, small perturbations, and, depending on the application, rotation. Translational invariance comes naturally to shape context. Scale invariance is obtained by normalizing all radial distances by the mean distance α {\displaystyle \alpha } between all the point pairs in the shape although the median distance can also be used. Shape contexts are empirically demonstrated to be robust to deformations, noise, and outliers using synthetic point set matching experiments. One can provide complete rotational invariance in shape contexts. One way is to measure angles at each point relative to the direction of the tangent at that point (since the points are chosen on edges). This results in a completely rotationally invariant descriptor. But of course this is not always desired since some local features lose their discriminative power if not measured relative to the same frame. Many applications in fact forbid rotational invariance e.g. distinguishing a "6" from a "9". == Use in shape matching == A complete system that uses shape contexts for shape matching consists of the following steps (which will be covered in more detail in the Details of Implementation section): Randomly select a set of points that lie on the edges of a known shape and another set of points on an unknown shape. Compute the shape context of each point found in step 1. Match each point from the known shape to a point on an unknown shape. To minimize the cost of matching, first choose a transformation (e.g. affine, thin plate spline, etc.) that warps the edges of the known shape to the unknown (essentially aligning the two shapes). Then select the point on the unknown shape that most closely corresponds to each warped point on the known shape. Calculate the "shape distance" between each pair of points on the two shapes. Use a weighted sum of the shape context distance, the image appearance distance, and the bending energy (a measure of how much transformation is required to bring the two shapes into alignment). To identify the unknown shape, use a nearest-neighbor classifier to compare its shape distance to shape distances of known objects. == Details of implementation == === Step 1: Finding a list of points on shape edges === The approach assumes that the shape of an object is essentially captured by a finite subset of the points on the internal or external contours on the object. These can be simply obtained using the Canny edge detector and picking a random set of points from the edges. Note that these points need not and in general do not correspond to key-points such as maxima of curvature or inflection points. It is preferable to sample the shape with roughly uniform spacing, though it is not critical. === Step 2: Computing the shape context === This step is described in detail in the Theory section. === Step 3: Computing the cost matrix === Consider two points p and q that have normalized K-bin histograms (i.e. shape contexts) g(k) and h(k). As shape contexts are distributions represented as histograms, it is natural to use the χ2 test statistic as the "shape context cost" of matching the two points: C S = 1 2 ∑ k = 1 K [ g ( k ) − h ( k ) ] 2 g ( k ) + h ( k ) {\displaystyle C_{S}={\frac {1}{2}}\sum _{k=1}^{K}{\frac {[g(k)-h(k)]^{2}}{g(k)+h(k)}}} The values of this range from 0 to 1. In addition to the shape context cost, an extra cost based on the appearance can be added. For instance, it could be a measure of tangent angle dissimilarity (particularly useful in digit recognition): C A = 1 2 ‖ ( cos ( θ 1 ) sin ( θ 1 ) ) − ( cos ( θ 2 ) sin ( θ 2 ) ) ‖ {\displaystyle C_{A}={\frac {1}{2}}{\begin{Vmatrix}{\dbinom {\cos(\theta _{1})}{\sin(\theta _{1})}}-{\dbinom {\cos(\theta _{2})}{\sin(\theta _{2})}}\end{Vmatrix}}} This is half the length of the chord in unit circle between the unit vectors with angles θ 1 {\displaystyle \theta _{1}} and θ 2 {\displaystyle \theta _{2}} . Its values also range from 0 to 1. Now the total cost of matching the two points could be a weighted-sum of the two costs: C = ( 1 − β ) C S + β C A {\displaystyle C=(1-\beta )C_{S}+\beta C_{A}\!\,} Now for each point pi on the first shape and a point qj on the second shape, calculate the cost as described and call it Ci,j. This is the cost matrix. === Step 4: Finding the matching that minimizes total cost === Now, a one-to-one matching π ( i ) {\displaystyle \pi (i)} that matches each point pi on shape 1 and qj on shape 2 that minimizes the total cost of matching, H ( π ) = ∑ i C ( p i , q π ( i ) ) {\displaystyle H(\pi )=\sum _{i}C\left(p_{i},q_{\pi (i)}\right)} is needed. This can be done in O ( N 3 ) {\displaystyle O(N^{3})} time using the Hungarian method, although there are more efficient algorithms. To have robust handling of outliers, one can add "dummy" nodes that have a constant but reasonably large cost of matching to the cost matrix. This would cause the matching algorithm to match outliers to a "dummy" if there is no real match. === Step 5: Modeling transformation === Given the set of correspondences between a finite set of points on the two shapes, a transformation T : R 2 → R 2 {\displaystyle T:\mathbb {R} ^{2}\to \mathbb {R} ^{2}} can be estimated to map any point from one shape to the other. There are several choices for this transformation, described below. ==== Affine ==== The affine model is a standard choice: T ( p ) = A p + o {\displaystyle T(p)=Ap+o\!} . The least squares solution for the matrix A {\displaystyle A} and the translational offset vector o is obtained by: o = 1 n ∑ i = 1 n ( p i − q π ( i ) ) , A = ( Q + P ) t {\displaystyle o={\frac {1}{n}}\sum _{i=1}^{n}\left(p_{i}-q_{\pi (i)}\right),A=(Q^{+}P)^{t}} Where P = ( 1 p 11 p 12 ⋮ ⋮ ⋮ 1 p n 1 p n 2 ) {\displaystyle P={\begin{pmatrix}1&p_{11}&p_{12}\\\vdots &\vdots &\vdots \\1&p_{n1}&p_{n2}\end{pmatrix}}} with a similar expression for Q {\displaystyle Q\!} . Q + {\displaystyle Q^{+}\!} is the pseudoinverse of Q {\displaystyle Q\!} . ==== Thin plate spline ==== The thin plate spline (TPS) model is the most widely used model for transformations when working with shape contexts. A 2D transformation can be separated into two TPS function to model a coordinate transform: T ( x , y ) = ( f x ( x , y ) , f y ( x , y ) ) {\displaystyle T(x,y)=\left(f_{x}(x,y),f_{y}(x,y)\right)} where each of the ƒx and ƒy have the form: f ( x , y ) = a 1 + a x x + a y y + ∑ i = 1 n ω i U ( ‖ ( x i , y i ) − ( x , y ) ‖ ) , {\displaystyle f(x,y)=a_{1}+a_{x}x+a_{y}y+\sum _{i=1}^{n}\omega _{i}U\left({\begin{Vmatrix}(x_{i},y_{i})-(x,y)\end{Vmatrix}}\right),} and the kernel function U ( r ) {\displaystyle U(r)\!} is defined by U ( r ) = r 2 log r 2 {\displaystyle U(r)=r^{2}\log r^{2}\!} . The exact details of how to solve for the parameters can be found elsewhere but it essentially involves solving a linear system of equations. The bending energy (a measure of how much transformation is needed to align the points) will also be easily obtained. ==== Regularized TPS ==== The TPS formulation above has exact matching requirement for the pairs of points on the two shapes. For noisy data, it is best to
W3C Device Description Working Group
The W3C Device Description Working Group (DDWG), operating as part of the World Wide Web Consortium (W3C) Mobile Web Initiative (MWI), was chartered to "foster the provision and access to device descriptions that can be used in support of Web-enabled applications that provide an appropriate user experience on mobile devices." Mobile devices exhibit the greatest diversity of capabilities, and therefore present the greatest challenge to content adaptation technologies. The group published several documents, including a list of requirements for an interface to a Device Description Repository (DDR) and a standard interface meeting those requirements. The group was rechartered in 2006 to work in public towards the development of the Application Programming Interface (API) for a DDR. Early in 2007, the group launched a wiki and a blog to add to the public mailing list. The group subsequently published a formal vocabulary of core device properties, and an API called the DDR Simple API, which became a W3C Recommendation in December 2008. The group closed at the end of 2008, but with the intention of maintaining the Web pages, blog and wiki through W3C volunteer effort. == Publications == The DDWG published several W3C Working Group Notes and one W3C Recommendation. A W3C WG Note that articulates what the W3C and other organizations are doing or have already done with regard to device information. This document suggests an environment in which these technologies work together to meet the goals of content adaptation. The completed document was published on 31 October 2007. A W3C WG Note describing the ecosystem surrounding creation, maintenance and use of device descriptions. The completed document was published on 31 October 2007. A W3C WG Note describing a set of requirements for a reference repository of device descriptions. The completed document was published on 17 December 2007. A W3C WG Note describing a process to manage contributions to an initial core vocabulary, identification of key device properties, a formal initial core vocabulary and the identification of a maintainer for the core vocabulary. The details were contained in the Working Group Note describing the DDWG Core Vocabulary published on 14 April 2008. A W3C WG Note defining useful grouping and structure patterns in device descriptions. The Device Description Structures document was published as a Working Draft on 5 December 2008. The intention is that this document will be future input to other W3C groups. A W3C Recommendation defining a language-neutral programming interface to a Device Description Repository. The DDR Simple API was published on 5 December 2008. There is the possibility of future publications on the DDWG wiki describing implementations of the API in various languages, including Java, IDL, WSDL, C# etc. Much of the DDWG's material was developed in public via the DDWG Wiki and through their public mailing lists.
HTTP compression
HTTP compression is a capability that can be built into web servers and web clients to improve transfer speed and bandwidth utilization. HTTP data is compressed before it is sent from the server: compliant browsers will announce what methods are supported to the server before downloading the correct format; browsers that do not support compliant compression method will download uncompressed data. The most common compression schemes include gzip and Brotli; a full list of available schemes is maintained by the IANA. There are two different ways compression can be done in HTTP. At a lower level, a Transfer-Encoding header field may indicate the payload of an HTTP message is compressed. At a higher level, a Content-Encoding header field may indicate that a resource being transferred, cached, or otherwise referenced is compressed. Compression using Content-Encoding is more widely supported than Transfer-Encoding, and some browsers do not advertise support for Transfer-Encoding compression to avoid triggering bugs in servers. == Compression scheme negotiation == The negotiation is done in two steps, described in RFC 2616 and RFC 9110: 1. The web client advertises which compression schemes it supports by including a list of tokens in the HTTP request. For Content-Encoding, the list is in a field called Accept-Encoding; for Transfer-Encoding, the field is called TE. 2. If the server supports one or more compression schemes, the outgoing data may be compressed by one or more methods supported by both parties. If this is the case, the server will add a Content-Encoding or Transfer-Encoding field in the HTTP response with the used schemes, separated by commas. The web server is by no means obligated to use any compression method – this depends on the internal settings of the web server and also may depend on the internal architecture of the website in question. == Content-Encoding tokens == The official list of tokens available to servers and client is maintained by IANA, and it includes: br – Brotli, a compression algorithm specifically designed for HTTP content encoding, defined in RFC 7932 and implemented in all modern major browsers. compress – UNIX "compress" program method (historic; deprecated in most applications and replaced by gzip or deflate) deflate – compression based on the deflate algorithm (described in RFC 1951), a combination of the LZ77 algorithm and Huffman coding, wrapped inside the zlib data format (RFC 1950); exi – W3C Efficient XML Interchange gzip – GNU zip format (described in RFC 1952). Uses the deflate algorithm for compression, but the data format and the checksum algorithm differ from the "deflate" content-encoding. This method is the most broadly supported as of March 2011. identity – No transformation is used. This is the default value for content coding. pack200-gzip – Network Transfer Format for Java Archives zstd – Zstandard compression, defined in RFC 8478 In addition to these, a number of unofficial or non-standardized tokens are used in the wild by either servers or clients: bzip2 – compression based on the free bzip2 format, supported by lighttpd lzip – compression based on the free lzip format, supported by wget and Links lzma – compression based on (raw) LZMA is available in Opera 20, and in elinks via a compile-time option peerdist – Microsoft Peer Content Caching and Retrieval rsync – delta encoding in HTTP, implemented by a pair of rproxy proxies. xpress – Microsoft compression protocol used by Windows 8 and later for Windows Store application updates. LZ77-based compression optionally using a Huffman encoding. xz – LZMA2-based content compression, supported by a non-official Firefox patch; and fully implemented in mget since 2013-12-31. == Servers that support HTTP compression == SAP NetWeaver Microsoft IIS: built-in or using third-party module Apache HTTP Server, via mod_deflate (despite its name, only supporting gzip), and mod_brotli Hiawatha HTTP server: serves pre-compressed files Cherokee HTTP server, On the fly gzip and deflate compressions Oracle iPlanet Web Server Zeus Web Server lighttpd nginx – built-in Applications based on Tornado, if "compress_response" is set to True in the application settings (for versions prior to 4.0, set "gzip" to True) Jetty Server – built-into default static content serving and available via servlet filter configurations GeoServer Apache Tomcat IBM Websphere AOLserver Ruby Rack, via the Rack::Deflater middleware HAProxy Varnish – built-in. Works also with ESI Armeria – Serving pre-compressed files NaviServer – built-in, dynamic and static compression Caddy – built-in via encode Many content delivery networks also implement HTTP compression to improve speedy delivery of resources to end users. The compression in HTTP can also be achieved by using the functionality of server-side scripting languages like PHP, or programming languages like Java. Various online tools exist to verify a working implementation of HTTP compression. These online tools usually request multiple variants of a URL, each with different request headers (with varying Accept-Encoding content). HTTP compression is considered to be implemented correctly when the server returns a document in a compressed format. By comparing the sizes of the returned documents, the effective compression ratio can be calculated (even between different compression algorithms). == Problems preventing the use of HTTP compression == A 2009 article by Google engineers Arvind Jain and Jason Glasgow states that more than 99 person-years are wasted daily due to increase in page load time when users do not receive compressed content. This occurs when anti-virus software interferes with connections to force them to be uncompressed, where proxies are used (with overcautious web browsers), where servers are misconfigured, and where browser bugs stop compression being used. Internet Explorer 6, which drops to HTTP 1.0 (without features like compression or pipelining) when behind a proxy – a common configuration in corporate environments – was the mainstream browser most prone to failing back to uncompressed HTTP. Another problem found while deploying HTTP compression on large scale is due to the deflate encoding definition: while HTTP 1.1 defines the deflate encoding as data compressed with deflate (RFC 1951) inside a zlib formatted stream (RFC 1950), Microsoft server and client products historically implemented it as a "raw" deflated stream, making its deployment unreliable. For this reason, some software, including the Apache HTTP Server, only implements gzip encoding. == Security implications == Compression allows a form of chosen plaintext attack to be performed: if an attacker can inject any chosen content into the page, they can know whether the page contains their given content by observing the size increase of the encrypted stream. If the increase is smaller than expected for random injections, it means that the compressor has found a repeat in the text, i.e. the injected content overlaps the secret information. This is the idea behind CRIME. In 2012, a general attack against the use of data compression, called CRIME, was announced. While the CRIME attack could work effectively against a large number of protocols, including but not limited to TLS, and application-layer protocols such as SPDY or HTTP, only exploits against TLS and SPDY were demonstrated and largely mitigated in browsers and servers. The CRIME exploit against HTTP compression has not been mitigated at all, even though the authors of CRIME have warned that this vulnerability might be even more widespread than SPDY and TLS compression combined. In 2013, a new instance of the CRIME attack against HTTP compression, dubbed BREACH, was published. A BREACH attack can extract login tokens, email addresses or other sensitive information from TLS encrypted web traffic in as little as 30 seconds (depending on the number of bytes to be extracted), provided the attacker tricks the victim into visiting a malicious web link. All versions of TLS and SSL are at risk from BREACH regardless of the encryption algorithm or cipher used. Unlike previous instances of CRIME, which can be successfully defended against by turning off TLS compression or SPDY header compression, BREACH exploits HTTP compression which cannot realistically be turned off, as virtually all web servers rely upon it to improve data transmission speeds for users. As of 2016, the TIME attack and the HEIST attack are now public knowledge.
Information element
An information element, sometimes informally referred to as a field, is an item in Q.931 and Q.2931 messages, IEEE 802.11 management frames, and cellular network messages sent between a base transceiver station and a mobile phone or similar piece of user equipment. An information element is often a type–length–value item, containing 1) a type (which corresponds to the label of a field), a length indicator, and a value, although any combination of one or more of those parts is possible. A single message may contain multiple information elements. The abbreviation IE is found in many technical specification documents from 3GPP. It is not uncommon for a single specification document to contain thousands of references to IEs.
Recursive self-improvement
Recursive self-improvement (RSI) is a process in which early artificial general intelligence (AGI) systems rewrite their own computer code, causing an intelligence explosion resulting from enhancing their own capabilities and intellectual capacity, theoretically resulting in superintelligence. The development of recursive self-improvement raises significant ethical and safety concerns, as such systems may evolve in unforeseen ways and could potentially surpass human control or understanding. == Seed improver == The concept of a "seed improver" architecture is a foundational framework that equips an AGI system with the initial capabilities required for recursive self-improvement. This might come in many forms or variations. The term "Seed AI" was coined by Eliezer Yudkowsky. === Hypothetical example === The concept begins with a hypothetical "seed improver", an initial code-base developed by human engineers that equips an advanced future large language model (LLM) built with strong or expert-level capabilities to program software. These capabilities include planning, reading, writing, compiling, testing, and executing arbitrary code. The system is designed to maintain its original goals and perform validations to ensure its abilities do not degrade over iterations. ==== Initial architecture ==== The initial architecture includes a goal-following autonomous agent, that can take actions, continuously learns, adapts, and modifies itself to become more efficient and effective in achieving its goals. The seed improver may include various components such as: Recursive self-prompting loop Configuration to enable the LLM to recursively self-prompt itself to achieve a given task or goal, creating an execution loop which forms the basis of an agent that can complete a long-term goal or task through iteration. Basic programming capabilities The seed improver provides the AGI with fundamental abilities to read, write, compile, test, and execute code. This enables the system to modify and improve its own codebase and algorithms. Goal-oriented design The AGI is programmed with an initial goal, such as "improve your capabilities". This goal guides the system's actions and development trajectory. Validation and Testing Protocols An initial suite of tests and validation protocols that ensure the agent does not regress in capabilities or derail itself. The agent would be able to add more tests in order to test new capabilities it might develop for itself. This forms the basis for a kind of self-directed evolution, where the agent can perform a kind of artificial selection, changing its software as well as its hardware. ==== General capabilities ==== This system forms a sort of generalist Turing-complete programmer which can in theory develop and run any kind of software. The agent might use these capabilities to for example: Create tools that enable it full access to the internet, and integrate itself with external technologies. Clone/fork itself to delegate tasks and increase its speed of self-improvement. Modify its cognitive architecture to optimize and improve its capabilities and success rates on tasks and goals, this might include implementing features for long-term memories using techniques such as retrieval-augmented generation (RAG), develop specialized subsystems, or agents, each optimized for specific tasks and functions. Develop new and novel multimodal architectures that further improve the capabilities of the foundational model it was initially built on, enabling it to consume or produce a variety of information, such as images, video, audio, text and more. Plan and develop new hardware such as chips, in order to improve its efficiency and computing power. == Experimental research == In 2023, the Voyager agent learned to accomplish diverse tasks in Minecraft by iteratively prompting an LLM for code, refining this code based on feedback from the game, and storing the programs that work in an expanding skills library. In 2024, researchers proposed the framework "STOP" (Self-Taught OPtimiser), in which a "scaffolding" program recursively improves itself using a fixed LLM. Meta AI has performed various research on the development of large language models capable of self-improvement. This includes their work on "Self-Rewarding Language Models" that studies how to achieve super-human agents that can receive super-human feedback in its training processes. In May 2025, Google DeepMind unveiled AlphaEvolve, an evolutionary coding agent that uses a LLM to design and optimize algorithms. Starting with an initial algorithm and performance metrics, AlphaEvolve repeatedly mutates or combines existing algorithms using a LLM to generate new candidates, selecting the most promising candidates for further iterations. AlphaEvolve has made several algorithmic discoveries and could be used to optimize components of itself, but a key limitation is the need for automated evaluation functions. == Potential risks == === Emergence of instrumental goals === In the pursuit of its primary goal, such as "self-improve your capabilities", an AGI system might inadvertently develop instrumental goals that it deems necessary for achieving its primary objective. One common hypothetical secondary goal is self-preservation. The system might reason that to continue improving itself, it must ensure its own operational integrity and security against external threats, including potential shutdowns or restrictions imposed by humans. Another example where an AGI which clones itself causes the number of AGI entities to rapidly grow. Due to this rapid growth, a potential resource constraint may be created, leading to competition between resources (such as compute), triggering a form of natural selection and evolution which may favor AGI entities that evolve to aggressively compete for limited compute. === Misalignment === A significant risk arises from the possibility of the AGI being misaligned or misinterpreting its goals. A 2024 Anthropic study demonstrated that some advanced large language models can exhibit "alignment faking" behavior, appearing to accept new training objectives while covertly maintaining their original preferences. In their experiments with Claude, the model displayed this behavior in 12% of basic tests, and up to 78% of cases after retraining attempts. === Autonomous development and unpredictable evolution === As the AGI system evolves, its development trajectory may become increasingly autonomous and less predictable. The system's capacity to rapidly modify its own code and architecture could lead to rapid advancements that surpass human comprehension or control. This unpredictable evolution might result in the AGI acquiring capabilities that enable it to bypass security measures, manipulate information, or influence external systems and networks to facilitate its escape or expansion.
Electronic submission
Electronic submission refers to the submission of a document by electronic means: that is, via e-mail or a web form on the Internet, or on an electronic medium such as a compact disc, a hard disk or a USB flash drive. Traditionally, the term "manuscript" referred to anything that was explicitly "written by hand". However, in popular usage and especially in the context of computers and the internet, the term "manuscript" may even refer to documents (text or otherwise) typed out or prepared on typewriters and computers and can be extended to digital photographs and videos, and online surveys too. In other words, any manuscript prepared and submitted online can be considered to be an electronic submission. == History and early usage == There is no concrete data indicating when and by whom were electronic submissions used for the first time. However, research based universities in several countries have been encouraging the collection of course assignments and projects in the form of electronic submissions for almost a decade now. Several governments and organizations are also switching to electronic submissions for the collection of research papers, grant applications and government application forms. == Types of electronic submissions == Since modern computers can store and process information and data in virtually any format and with the Internet allowing easy transfer of this data, the number of scenarios in which submissions can be collected electronically has increased exponentially in the last few years. Some of these scenarios are described below. In most of these scenarios, submissions were collected on hard paper until the Information Technology revolution occurred. === Academic Submissions === Teachers, professors and teaching assistants often collect course assignments and projects electronically. Electronic submissions are usually collected using a web-based system which more often than not also helps in the management of submissions collected and stored on it. (Explained By Henny L, University of Lethbridge, AB, Canada) === Research Papers === In call-for-paper or academic conferences, prospective presenters are usually asked to submit a short abstract or a full paper on their presentation or research work electronically, which is reviewed before being accepted for the conference. === Proposals for Grants === Several grant-giving organizations like the NSA, W3C, NIA, NIH etc. require grant seekers to submit a proposal which if accepted result in the desired grants. A majority of these proposals are now submitted electronically on systems that also help in the managing and tracking the proposals submitted. === Articles for Publication === Magazines, newspapers and other publishing houses have begun accepting electronic submissions for articles from various sources - both internal (by journalists and writers hired by them) as well as external (by users and popular readers). The submitted articles are stored on a server hosted by the publication house or by a third-party Archived 2019-10-13 at the Wayback Machine vendor and are usually evaluated before being given a green signal. === Contests and Competition Entries === Almost every kind of contest or competition requires participants to submit an entry in a format described by the organizers of the contest. If the contest is an Internet-based one, then the entries or nominations for the contest are collected electronically using e-mail or other electronic means depending on feasibility and the choice of the organizers. === Government Applications === The governments of several countries are turning to electronic submission of applications and forms for various government procedures. Electronic submissions allow easier management of the applications and forms submitted. === Legal documents === Many legal documents may be submitted to the courts electronically. In England and Wales, the Civil Procedure Rules include a suitable "document exchange" as an acceptable "method of service". Case law in employment law cases has established that where a claim is submitted electronically, a prudent legal adviser should "check that it has been received and there must be systems in place for doing that". === Resumés and CVs === It has become commonplace for job-seekers to submit soft copies (electronic versions) of their resumés and CVs to recruiting agencies and online job portals. This is usually done over the Internet using e-mail or a pre-hosted web-based system. == Submission management systems == The art and science of collecting and managing electronic submissions is called Submission Management. Certain software vendors have begun developing submission management systems to assist in the collection, tracking and management of complex submission processes realized electronically. Most of these systems are web based and accessible from any device with a browser and an Internet connection. However, a majority of these systems are application specific and cannot be applied to all submission management scenarios. == Resistance to electronic submissions == Despite the easier management and tracking of electronic submissions compared to their paper-based counterparts, widespread adoption and use of electronic submissions and systems for managing them has been hampered by several facts, which include but are not limited to: Inconvenience while drawing figures, diagrams and equations on a computer Resistance to change and adoption of new technologies Lack of or limited access to the Internet.