In cryptography, a nonce is an arbitrary number that can be used just once in a cryptographic communication. It is often a random or pseudo-random number issued in an authentication protocol to ensure that each communication session is unique, and therefore that old communications cannot be reused in replay attacks. Nonces can also be useful as initialization vectors and in cryptographic hash functions. == Definition == A nonce is an arbitrary number used only once in a cryptographic communication, in the spirit of a nonce word. They are often random or pseudo-random numbers. Many nonces also include a timestamp to ensure exact timeliness, though this requires clock synchronisation between organisations. The addition of a client nonce ("cnonce") helps to improve the security in some ways as implemented in digest access authentication. To ensure that a nonce is used only once, it should be time-variant (including a suitably fine-grained timestamp in its value), or generated with enough random bits to ensure an insignificantly low chance of repeating a previously generated value. Some authors define pseudo-randomness (or unpredictability) as a requirement for a nonce. Nonce is a word dating back to Middle English for something only used once or temporarily (often with the construction "for the nonce"). It descends from the construction "then anes" ("the one [purpose]"). A false etymology claiming it to stand for "number used once" or similar is incorrect. == Usage == === Authentication === Authentication protocols may use nonces to ensure that old communications cannot be reused in replay attacks. For instance, nonces are used in HTTP digest access authentication to calculate an MD5 digest of the password. The nonces are different each time the 401 authentication challenge response code is presented, thus making replay attacks virtually impossible. The scenario of ordering products over the Internet can provide an example of the usefulness of nonces in replay attacks. An attacker could take the encrypted information and—without needing to decrypt—could continue to send a particular order to the supplier, thereby ordering products over and over again under the same name and purchase information. The nonce is used to give 'originality' to a given message so that if the company receives any other orders from the same person with the same nonce, it will discard those as invalid orders. A nonce may be used to ensure security for a stream cipher. Where the same key is used for more than one message and then a different nonce is used to ensure that the keystream is different for different messages encrypted with that key; often the message number is used. Secret nonce values are used by the Lamport signature scheme as a signer-side secret which can be selectively revealed for comparison to public hashes for signature creation and verification. === Hashing === Nonces are used in proof-of-work systems to vary the input to a cryptographic hash function so as to obtain a hash for a certain input that fulfils certain arbitrary conditions. In doing so, it becomes far more difficult to create a "desirable" hash than to verify it, shifting the burden of work onto one side of a transaction or system. For example, proof of work, using hash functions, was considered as a means to combat email spam by forcing email senders to find a hash value for the email (which included a timestamp to prevent pre-computation of useful hashes for later use) that had an arbitrary number of leading zeroes, by hashing the same input with a large number of values until a "desirable" hash was obtained. Similarly, the Bitcoin blockchain hashing algorithm can be tuned to an arbitrary difficulty by changing the required minimum/maximum value of the hash so that the number of bitcoins awarded for new blocks does not increase linearly with increased network computation power as new users join. This is likewise achieved by forcing Bitcoin miners to add nonce values to the value being hashed to change the hash algorithm output. As cryptographic hash algorithms cannot easily be predicted based on their inputs, this makes the act of blockchain hashing and the possibility of being awarded bitcoins something of a lottery, where the first "miner" to find a nonce that delivers a desirable hash is awarded bitcoins.
MoFA Mitra
MoFA Mitra is a mobile application launched by the Ministry of Foreign Affairs of Nepal to provide digital consular services, emergency support, rescue coordination, and complaint registration facilities for Nepali citizens living and working abroad. The application allows Nepali migrant workers, students, tourists, and Non-Resident Nepalis (NRNs) to access embassy services, emergency help, and official information directly from their smartphones. == Background == The need for a centralized digital support platform for Nepalis abroad had been discussed for several years due to increasing complaints related to labor exploitation, rescue delays, documentation problems, and lack of communication with Nepali diplomatic missions. Media organizations and migrant rights advocates had continuously highlighted issues faced by Nepali workers abroad, including human trafficking, fraudulent recruitment, delayed repatriation, and difficulties in receiving emergency assistance. In response, the Ministry of Foreign Affairs developed the MoFA Mitra app to digitize complaint handling, improve communication between embassies and citizens, and make emergency response faster and more accessible. == Features == The app includes several services and features for Nepali citizens abroad, including complaint registration, rescue coordination, embassy communication, and digital consular support services. Features of the application include: Online complaint registration Emergency rescue request system Direct contact with Nepali embassies and consulates Digital consular information Passport and document-related assistance Labor and migration support information Emergency hotline access Real-time notifications and alerts Location-based embassy information Tracking and coordination support for stranded citizens According to reports, the application was designed to simplify access to diplomatic services and strengthen emergency response coordination for Nepalis abroad. == Launch == The application was officially launched by Nepal’s Ministry of Foreign Affairs in Kathmandu in May 2026. Government officials stated that the app would strengthen Nepal’s digital governance system and improve support mechanisms for Nepali citizens residing overseas. Officials said the platform would help improve communication between Nepali diplomatic missions and citizens during emergencies and rescue operations. == Reception == The launch of the app received positive coverage from Nepali and international media outlets. Commentators described the initiative as a significant step toward modernization of Nepal’s diplomatic and consular services and digital governance infrastructure. Some observers also emphasized the importance of effective implementation, rapid response mechanisms, and continuous monitoring to ensure practical benefits for migrant workers abroad.
Digital media service
A digital media service (DMS) is an online service provider that sells access to digital library of content such as films, software, games, images, literature, etc. While no transfer of property is made, a nearly perfect duplicate of the data (song movie, etc.) is made on a customer's computer. Content is either primarily hosted on a dedicated server, which is owned by the service provider, or it is hosted primarily on the hard drives of its customers using a P2P protocol with, perhaps, a dedicated server to supplement. == History == One example of the older business model is the iTunes Store, which still markets and prices data as individual retail products. There are no examples of the latter business model in operation yet, but one is currently in development by Global Gaming Factory X and expected to begin operation some time after they acquire The Pirate Bay domain on August 27, 2009. A key difference between the two models is that the model which relies on its customer base for offering their bandwidth for other customers to access customer hosted data can operate at significantly lower costs than a company that seeks to limit data access to a per-download fee in order to supplement the cost of using its own hosting and bandwidth. The P2P model holds the potential for companies to offer unlimited access to the largest data library in the history of the internet to its customers for a reasonably low membership rate that is relevant to the cost of operation. While the market is virtually untouched, the P2P supplemented model will need entrepreneurs who are able to overcome a series of challenges in order to compete with the older business model as well as that which is offered for free (and often against the wishes of copyright holders) by hundreds of P2P communities on the internet. These challenges include, but are not limited to: Offering better data quality, speed, convenience and ease of use, protocol, sense of security, indexing and search organization, site up time, data library size, customer support, advertising, artist/copyright holder incentives and compensation, incentives and compensation for customers hosting data and providing bandwidth, guaranteed seeding (available access to indexed data at all times), than competitors.
The Big Book of Social Media
The Big Book of Social Media: Case Studies, Stories, Perspectives, released in November 2010 by Yorkshire Publishing, is a compilation of non-fiction articles and chapters written by social media experts in their respective fields and edited by Robert Fine, organizer of the Cool Social Conferences World Tour and founder of Cool Blue Press, with a foreword by Sam Feist, political director for CNN. == Synopsis == The publisher, on its site, summed up the book as, "Not business. Not marketing. This is an idea book." And an article in Business Insider described the book as bringing "the social back into social media." == Contributors == Contributing authors include: Alan Rosenblatt, Alane Anderson, Alecia Dantico, Alex Priest, Alfred Naranjo, Becky Carroll, Carri Bugbee, Cathy Scott, Colleen Crinklaw, Constantine Markides, Cordelia Mendoza, Craig Kanalley, Dave Ingland, Eric Andersen, Eric Brown, Gary Zukowski, Haja Rasambainarivo, Jennifer Kaplan, Kari Quaas, Lauri Stevens, Lev Ekster, Mark Stelzner, Matthew Felling, Matt Stewart, Melani Gordon, Michael Bourne, Michele Mattia, Mirna Bard, Neal Schaffer, Nic Evans, Noaf Ereiqat, Pek Pongpaet, Perri Gorman, Phil Baumann, Regina Holliday, Rory Cooper, Sam Feist, Shashi Bellamkonda, Shrinath Navghane, Steve Pratt, Ted Nguyen, Todd Schnick, Tonia Ries, Wayne Burke, as well as Robert Fine. In December 2011, some of the contributing authors organized "Tweet It Forward," a holiday charity fundraiser, with net proceeds benefitting the Food Bank for New York City. == Reception == Reviewer Mike Brown wrote on the Brainzooming blog that the book goes "beyond the valueless chatter out there; it provides solid discussions of real-life social media strategy implementations that have truly integrated organizational objectives and delivered real metrics." And Tech Cocktail wrote it in its review, "Through a collection of entertaining anecdotes and insightful marketing agendas, one sees what social media is truly all about and how it is revolutionizing the communications industry." In 2011, at the SXSW social media festival in Austin, Texas, Fine launched Cool Blue Press and reintroduced The Big Book of Social Media, with plans, he told a reporter from the Washington Examiner, for other new media books and publishing projects, including The Social Media Monthly magazine. The book was reviewed in 2012 by SAGE Publications for its Journalism and Mass Communication Educator magazine. It is also cited in several books and journals. === Awards === The book was a winner in the 4th Annual Reader's Choice "Small Business Book Awards" for 2011. Windmill Networking named it the Top 15 recommended social media books of 2010.
Polyfill (programming)
In software development, a polyfill is code that implements a new standard feature of a deployment environment within an old version of that environment that does not natively support the feature. Most often, it refers to JavaScript code that implements an HTML5 or CSS web standard, either an established standard (supported by some browsers) on older browsers, or a proposed standard (not supported by any browsers) on existing browsers. Polyfills are also used in PHP and Python. Polyfills allow web developers to use an API regardless of whether or not it is supported by a browser, and usually with minimal overhead. Typically they first check if a browser supports an API, and use it if available, otherwise using their own implementation. Polyfills themselves use other, more supported features, and thus different polyfills may be needed for different browsers. The term is also used as a verb: polyfilling is providing a polyfill for a feature. == Definition == The term is a neologism, coined by Remy Sharp, who required a word that meant "replicate an API using JavaScript (or Flash or whatever) if the browser doesn’t have it natively" while co-writing the book Introducing HTML5 in 2009. Formally, "a shim is a library that brings a new API to an older environment, using only the means of that environment." Polyfills exactly fit this definition; the term shim was also used for early polyfills. However, to Sharp shim connoted non-transparent APIs and workarounds, such as spacer GIFs for layout, sometimes known as shim.gif, and similar terms such as progressive enhancement and graceful degradation were not appropriate, so he invented a new term. The term is based on the multipurpose filling paste brand Polyfilla, a paste used to cover up cracks and holes in walls, and the meaning "fill in holes (in functionality) in many (poly-) ways." The word has since gained popularity, particularly due to its use by Paul Irish and in Modernizr documentation. The distinction that Sharp makes is: What makes a polyfill different from the techniques we have already, like a shim, is this: if you removed the polyfill script, your code would continue to work, without any changes required in spite of the polyfill being removed. This distinction is not drawn by other authors. At times various other distinctions are drawn between shims, polyfills, and fallbacks, but there are no generally accepted distinctions: most consider polyfills a form of shim. The term polyfiller is also occasionally found. == Examples == === core-js === core-js is one of the most popular JavaScript standard library polyfills. Includes polyfills for ECMAScript up to the latest version of the standard: promises, symbols, collections, iterators, typed arrays, many other features, ECMAScript proposals, some cross-platform WHATWG / W3C features and proposals like URL. You can load only required features or use it without global namespace pollution. It can be integrated with Babel, which allows it to automatically inject required core-js modules into your code. === html5shiv === In IE versions prior to 9, unknown HTML elements like
Instance selection
Instance selection (or dataset reduction, or dataset condensation) is an important data pre-processing step that can be applied in many machine learning (or data mining) tasks. Approaches for instance selection can be applied for reducing the original dataset to a manageable volume, leading to a reduction of the computational resources that are necessary for performing the learning process. Algorithms of instance selection can also be applied for removing noisy instances, before applying learning algorithms. This step can improve the accuracy in classification problems. Algorithm for instance selection should identify a subset of the total available data to achieve the original purpose of the data mining (or machine learning) application as if the whole data had been used. Considering this, the optimal outcome of IS would be the minimum data subset that can accomplish the same task with no performance loss, in comparison with the performance achieved when the task is performed using the whole available data. Therefore, every instance selection strategy should deal with a trade-off between the reduction rate of the dataset and the classification quality. == Instance selection algorithms == The literature provides several different algorithms for instance selection. They can be distinguished from each other according to several different criteria. Considering this, instance selection algorithms can be grouped in two main classes, according to what instances they select: algorithms that preserve the instances at the boundaries of classes and algorithms that preserve the internal instances of the classes. Within the category of algorithms that select instances at the boundaries it is possible to cite DROP3, ICF and LSBo. On the other hand, within the category of algorithms that select internal instances, it is possible to mention ENN and LSSm. In general, algorithm such as ENN and LSSm are used for removing harmful (noisy) instances from the dataset. They do not reduce the data as the algorithms that select border instances, but they remove instances at the boundaries that have a negative impact on the data mining task. They can be used by other instance selection algorithms, as a filtering step. For example, the ENN algorithm is used by DROP3 as the first step, and the LSSm algorithm is used by LSBo. There is also another group of algorithms that adopt different selection criteria. For example, the algorithms LDIS, CDIS and XLDIS select the densest instances in a given arbitrary neighborhood. The selected instances can include both, border and internal instances. The LDIS and CDIS algorithms are very simple and select subsets that are very representative of the original dataset. Besides that, since they search by the representative instances in each class separately, they are faster (in terms of time complexity and effective running time) than other algorithms, such as DROP3 and ICF. Besides that, there is a third category of algorithms that, instead of selecting actual instances of the dataset, select prototypes (that can be synthetic instances). In this category it is possible to include PSSA, PSDSP and PSSP. The three algorithms adopt the notion of spatial partition (a hyperrectangle) for identifying similar instances and extract prototypes for each set of similar instances. In general, these approaches can also be modified for selecting actual instances of the datasets. The algorithm ISDSP adopts a similar approach for selecting actual instances (instead of prototypes).
Signal-to-crosstalk ratio
The signal-to-crosstalk ratio at a specified point in a circuit is the ratio of the power of the wanted signal to the power of the unwanted signal from another channel. The signals are adjusted in each channel so that they are of equal power at the zero transmission level point in their respective channels. The signal-to-crosstalk ratio is usually expressed in dB.