AI Data Delivery F5

AI Data Delivery F5 — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

  • Easy8

    Easy8

    Easy8 is a project management platform. It is an extension to Redmine. == History == Easy8 Group, the company behind Easy8, was established in 2006 by Filip Morávek who serves as the company's CEO and is also a founder of the Mindfulness Foundation. In 2007, the company released an open-source project management software based on Redmine that included modules for project financing. The Easy8 Group has also developed an identical product distributed in Czechia and Hungary. In 2021 Easy8 11 was released with mobile application, Rails 6, Ruby 3.0, Sidekiq B2B CRM features. In 2022 Easy8 was available in 70 countries. In 2023 Easy8 13 was released in collaboration with Scrum certified expert. In March 2026, Easy Redmine and Easy Project rebranded to Easy8. == Overview == Easy8 covers Waterfall and Agile project management individually or simultaneously. It is available in public and private cloud hosting or on-premises server. It's based on open-source technologies such as Redmine. It covers the complete process from planning through implementation to helpdesk support. Easy8 also implements techniques such as risk and resource management, mind maps and Gantt charts. The application includes a CRM module focused on the B2B segment with partner access control and partner network management. Easy8 13 also has integration MediaWiki, the software that runs Wikipedia and GitLab, an AI-powered DevSecOps Platform. Easy8 is used by the Kazakh state administration, Bosch, Zentiva, Innogy, Ministry of Foreign Affairs of the Czech Republic, Axa, RTL Radio Berlin, Continental and Ogilvy among others. It features separately installable extensions. In 2017, it was reviewed by iX Special in comparison to GitKraken (previously known as Axosoft) and Agilo for Trac. PCmag while analyzing Redmine highlights that Easy8 enhances the core features of Redmine with a more polished interface and offers proprietary plug-ins for additional functionalities, such as tools for resource management, financial management, and support for agile methodologies. == Easy AI == Easy AI is an artificial intelligence extension integrated into the Easy8 project management suite, offering both cloud-based and on-premises deployment options. Easy AI uses the Llama 3.1 AI model and supports organizational data controls. The system includes assistants for personal, project, and service workflows, supporting tasks such as text summarization, project planning, and helpdesk ticket management. == License == The Easy8 website claims that "Easy8 is an Open Source software", but its source is neither freely downloadable nor is it licensed under an open-source license according to The Open Source Definition, since the Easy8 Group Commercial License does not allow free redistribution (among other restrictions).

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  • Intent-based network

    Intent-based network

    Intent-Based Networking (IBN) is an approach to network management that shifts the focus from manually configuring individual devices to specifying desired outcomes or business objectives, referred to as "intents". == Description == Rather than relying on low-level commands to configure the network, administrators define these high-level intents, and the network dynamically adjusts itself to meet these requirements. IBN simplifies the management of complex networks by ensuring that the network infrastructure aligns with the desired operational goals. For example, an implementer can explicitly state a network purpose with a policy such as "Allow hosts A and B to communicate with X bandwidth capacity" without the need to understand the detailed mechanisms of the underlying devices (e.g. switches), topology or routing configurations. == Architecture == Advances in Natural Language Understanding (NLU) systems, along with neural network-based algorithms like BERT, RoBERTa, GLUE, and ERNIE, have enabled the conversion of user queries into structured representations that can be processed by automated services. This capability is crucial for managing the increasing complexity of network services. Intent-Based Networking (IBN) leverages these advancements to simplify network management by abstracting network services, reducing operational complexity, and lowering costs. A proposed three-layered architecture integrates intent-based automation into network management systems. In the business layer, intents are based on Key Performance Indicators (KPIs) and Service Level Agreements (SLAs), reflecting business objectives. The intent layer evaluates and re-plans actions dynamically, where a Knowledge module abstracts and reasons about intents, while an Agent interfaces with network objects to execute actions. The data layer observes network objects, updates topology information, and interacts with the Knowledge and Agent modules to ensure accurate and timely responses to network changes. At the bottom, the network layer contains the physical infrastructure, transforming network data into a usable format for the intent layer to act upon.

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  • Knapsack problem

    Knapsack problem

    The knapsack problem is the following problem in combinatorial optimization: Given a set of items, each with a weight and a value, determine which items to include in the collection so that the total weight is less than or equal to a given limit and the total value is as large as possible. It derives its name from the problem faced by someone who is constrained by a fixed-size knapsack and must fill it with the most valuable items. The problem often arises in resource allocation where the decision-makers have to choose from a set of non-divisible projects or tasks under a fixed budget or time constraint, respectively. The knapsack problem has been studied for more than a century, with early works dating back to 1897. The subset sum problem is a special case of the decision and 0-1 problems where for each kind of item, the weight equals the value: w i = v i {\displaystyle w_{i}=v_{i}} . In the field of cryptography, the term knapsack problem is often used to refer specifically to the subset sum problem. The subset sum problem is one of Karp's 21 NP-complete problems. == Applications == Knapsack problems appear in real-world decision-making processes in a wide variety of fields, such as finding the least wasteful way to cut raw materials, selection of investments and portfolios, selection of assets for asset-backed securitization, and generating keys for the Merkle–Hellman and other knapsack cryptosystems. One early application of knapsack algorithms was in the construction and scoring of tests in which the test-takers have a choice as to which questions they answer. For small examples, it is a fairly simple process to provide the test-takers with such a choice. For example, if an exam contains 12 questions each worth 10 points, the test-taker need only answer 10 questions to achieve a maximum possible score of 100 points. However, on tests with a heterogeneous distribution of point values, it is more difficult to provide choices. Feuerman and Weiss proposed a system in which students are given a heterogeneous test with a total of 125 possible points. The students are asked to answer all of the questions to the best of their abilities. Of the possible subsets of problems whose total point values add up to 100, a knapsack algorithm would determine which subset gives each student the highest possible score. A 1999 study of the Stony Brook University Algorithm Repository showed that, out of 75 algorithmic problems related to the field of combinatorial algorithms and algorithm engineering, the knapsack problem was the 19th most popular and the third most needed after suffix trees and the bin packing problem. == Definition == The most common problem being solved is the 0-1 knapsack problem, which restricts the number x i {\displaystyle x_{i}} of copies of each kind of item to zero or one. Given a set of n {\displaystyle n} items numbered from 1 up to n {\displaystyle n} , each with a weight w i {\displaystyle w_{i}} and a value v i {\displaystyle v_{i}} , along with a maximum weight capacity W {\displaystyle W} , maximize ∑ i = 1 n v i x i {\displaystyle \sum _{i=1}^{n}v_{i}x_{i}} subject to ∑ i = 1 n w i x i ≤ W {\displaystyle \sum _{i=1}^{n}w_{i}x_{i}\leq W} and x i ∈ { 0 , 1 } {\displaystyle x_{i}\in \{0,1\}} . Here x i {\displaystyle x_{i}} represents the number of instances of item i {\displaystyle i} to include in the knapsack. Informally, the problem is to maximize the sum of the values of the items in the knapsack so that the sum of the weights is less than or equal to the knapsack's capacity. The bounded knapsack problem (BKP) removes the restriction that there is only one of each item, but restricts the number x i {\displaystyle x_{i}} of copies of each kind of item to a maximum non-negative integer value c {\displaystyle c} : maximize ∑ i = 1 n v i x i {\displaystyle \sum _{i=1}^{n}v_{i}x_{i}} subject to ∑ i = 1 n w i x i ≤ W {\displaystyle \sum _{i=1}^{n}w_{i}x_{i}\leq W} and x i ∈ { 0 , 1 , 2 , … , c } . {\displaystyle x_{i}\in \{0,1,2,\dots ,c\}.} The unbounded knapsack problem (UKP) places no upper bound on the number of copies of each kind of item and can be formulated as above except that the only restriction on x i {\displaystyle x_{i}} is that it is a non-negative integer. maximize ∑ i = 1 n v i x i {\displaystyle \sum _{i=1}^{n}v_{i}x_{i}} subject to ∑ i = 1 n w i x i ≤ W {\displaystyle \sum _{i=1}^{n}w_{i}x_{i}\leq W} and x i ∈ N . {\displaystyle x_{i}\in \mathbb {N} .} One example of the unbounded knapsack problem is given using the figure shown at the beginning of this article and the text "if any number of each book is available" in the caption of that figure. == Computational complexity == The knapsack problem is interesting from the perspective of computer science for many reasons: The decision problem form of the knapsack problem (Can a value of at least V be achieved without exceeding the weight W?) is NP-complete, thus there is no known algorithm that is both correct and fast (polynomial-time) in all cases. There is no known polynomial algorithm which can tell, given a solution, whether it is optimal (which would mean that there is no solution with a larger V). This problem is co-NP-complete. There is a pseudo-polynomial time algorithm using dynamic programming. There is a fully polynomial-time approximation scheme, which uses the pseudo-polynomial time algorithm as a subroutine, described below. Many cases that arise in practice, and "random instances" from some distributions, can nonetheless be solved exactly. There is a link between the "decision" and "optimization" problems in that if there exists a polynomial algorithm that solves the "decision" problem, then one can find the maximum value for the optimization problem in polynomial time by applying this algorithm iteratively while increasing the value of k. On the other hand, if an algorithm finds the optimal value of the optimization problem in polynomial time, then the decision problem can be solved in polynomial time by comparing the value of the solution output by this algorithm with the value of k. Thus, both versions of the problem are of similar difficulty. One theme in research literature is to identify what the "hard" instances of the knapsack problem look like, or viewed another way, to identify what properties of instances in practice might make them more amenable than their worst-case NP-complete behaviour suggests. The goal in finding these "hard" instances is for their use in public-key cryptography systems, such as the Merkle–Hellman knapsack cryptosystem. More generally, better understanding of the structure of the space of instances of an optimization problem helps to advance the study of the particular problem and can improve algorithm selection. Furthermore, notable is the fact that the hardness of the knapsack problem depends on the form of the input. If the weights and profits are given as integers, it is weakly NP-complete, while it is strongly NP-complete if the weights and profits are given as rational numbers. However, in the case of rational weights and profits it still admits a fully polynomial-time approximation scheme. === Unit-cost models === The NP-hardness of the Knapsack problem relates to computational models in which the size of integers matters (such as the Turing machine). In contrast, decision trees count each decision as a single step. Dobkin and Lipton show an 1 2 n 2 {\displaystyle {1 \over 2}n^{2}} lower bound on linear decision trees for the knapsack problem, that is, trees where decision nodes test the sign of affine functions. This was generalized to algebraic decision trees by Steele and Yao. If the elements in the problem are real numbers or rationals, the decision-tree lower bound extends to the real random-access machine model with an instruction set that includes addition, subtraction and multiplication of real numbers, as well as comparison and either division or remaindering ("floor"). This model covers more algorithms than the algebraic decision-tree model, as it encompasses algorithms that use indexing into tables. However, in this model all program steps are counted, not just decisions. An upper bound for a decision-tree model was given by Meyer auf der Heide who showed that for every n there exists an O(n4)-deep linear decision tree that solves the subset-sum problem with n items. Note that this does not imply any upper bound for an algorithm that should solve the problem for any given n. == Solving == Several algorithms are available to solve knapsack problems, based on the dynamic programming approach, the branch and bound approach or hybridizations of both approaches. === Dynamic programming in-advance algorithm === The unbounded knapsack problem (UKP) places no restriction on the number of copies of each kind of item. Besides, here we assume that x i > 0 {\displaystyle x_{i}>0} m [ w ′ ] = max ( ∑ i = 1 n v i x i ) {\displaystyle m[w']=\max \left(\sum _{i=1}^{n}v_{i}x_{i}\right)} subject to ∑

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  • Cryptographic High Value Product

    Cryptographic High Value Product

    Cryptographic High Value Product (CHVP) is a designation used within the information security community to identify assets that have high value, and which may be used to encrypt / decrypt secure communications, but which do not retain or store any classified information. When disconnected from the secure communication network, the CHVP equipment may be handled with a lower level of controls than required for COMSEC equipment.

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  • Bayesian programming

    Bayesian programming

    Bayesian programming is a formalism and a methodology for having a technique to specify probabilistic models and solve problems when less than the necessary information is available. Edwin T. Jaynes proposed that probability could be considered as an alternative and an extension of logic for rational reasoning with incomplete and uncertain information. In his founding book Probability Theory: The Logic of Science he developed this theory and proposed what he called "the robot," which was not a physical device, but an inference engine to automate probabilistic reasoning—a kind of Prolog for probability instead of logic. Bayesian programming is a formal and concrete implementation of this "robot". Bayesian programming may also be seen as an algebraic formalism to specify graphical models such as, for instance, Bayesian networks, dynamic Bayesian networks, Kalman filters or hidden Markov models. Indeed, Bayesian programming is more general than Bayesian networks and has a power of expression equivalent to probabilistic factor graphs. == Formalism == A Bayesian program is a means of specifying a family of probability distributions. The constituent elements of a Bayesian program are presented below: Program { Description { Specification ( π ) { Variables Decomposition Forms Identification (based on δ ) Question {\displaystyle {\text{Program}}{\begin{cases}{\text{Description}}{\begin{cases}{\text{Specification}}(\pi ){\begin{cases}{\text{Variables}}\\{\text{Decomposition}}\\{\text{Forms}}\\\end{cases}}\\{\text{Identification (based on }}\delta )\end{cases}}\\{\text{Question}}\end{cases}}} A program is constructed from a description and a question. A description is constructed using some specification ( π {\displaystyle \pi } ) as given by the programmer and an identification or learning process for the parameters not completely specified by the specification, using a data set ( δ {\displaystyle \delta } ). A specification is constructed from a set of pertinent variables, a decomposition and a set of forms. Forms are either parametric forms or questions to other Bayesian programs. A question specifies which probability distribution has to be computed. === Description === The purpose of a description is to specify an effective method of computing a joint probability distribution on a set of variables { X 1 , X 2 , ⋯ , X N } {\displaystyle \left\{X_{1},X_{2},\cdots ,X_{N}\right\}} given a set of experimental data δ {\displaystyle \delta } and some specification π {\displaystyle \pi } . This joint distribution is denoted as: P ( X 1 ∧ X 2 ∧ ⋯ ∧ X N ∣ δ ∧ π ) {\displaystyle P\left(X_{1}\wedge X_{2}\wedge \cdots \wedge X_{N}\mid \delta \wedge \pi \right)} . To specify preliminary knowledge π {\displaystyle \pi } , the programmer must undertake the following: Define the set of relevant variables { X 1 , X 2 , ⋯ , X N } {\displaystyle \left\{X_{1},X_{2},\cdots ,X_{N}\right\}} on which the joint distribution is defined. Decompose the joint distribution (break it into relevant independent or conditional probabilities). Define the forms of each of the distributions (e.g., for each variable, one of the list of probability distributions). ==== Decomposition ==== Given a partition of { X 1 , X 2 , … , X N } {\displaystyle \left\{X_{1},X_{2},\ldots ,X_{N}\right\}} containing K {\displaystyle K} subsets, K {\displaystyle K} variables are defined L 1 , ⋯ , L K {\displaystyle L_{1},\cdots ,L_{K}} , each corresponding to one of these subsets. Each variable L k {\displaystyle L_{k}} is obtained as the conjunction of the variables { X k 1 , X k 2 , ⋯ } {\displaystyle \left\{X_{k_{1}},X_{k_{2}},\cdots \right\}} belonging to the k t h {\displaystyle k^{th}} subset. Recursive application of Bayes' theorem leads to: P ( X 1 ∧ X 2 ∧ ⋯ ∧ X N ∣ δ ∧ π ) = P ( L 1 ∧ ⋯ ∧ L K ∣ δ ∧ π ) = P ( L 1 ∣ δ ∧ π ) × P ( L 2 ∣ L 1 ∧ δ ∧ π ) × ⋯ × P ( L K ∣ L K − 1 ∧ ⋯ ∧ L 1 ∧ δ ∧ π ) {\displaystyle {\begin{aligned}&P\left(X_{1}\wedge X_{2}\wedge \cdots \wedge X_{N}\mid \delta \wedge \pi \right)\\={}&P\left(L_{1}\wedge \cdots \wedge L_{K}\mid \delta \wedge \pi \right)\\={}&P\left(L_{1}\mid \delta \wedge \pi \right)\times P\left(L_{2}\mid L_{1}\wedge \delta \wedge \pi \right)\times \cdots \times P\left(L_{K}\mid L_{K-1}\wedge \cdots \wedge L_{1}\wedge \delta \wedge \pi \right)\end{aligned}}} Conditional independence hypotheses then allow further simplifications. A conditional independence hypothesis for variable L k {\displaystyle L_{k}} is defined by choosing some variable X n {\displaystyle X_{n}} among the variables appearing in the conjunction L k − 1 ∧ ⋯ ∧ L 2 ∧ L 1 {\displaystyle L_{k-1}\wedge \cdots \wedge L_{2}\wedge L_{1}} , labelling R k {\displaystyle R_{k}} as the conjunction of these chosen variables and setting: P ( L k ∣ L k − 1 ∧ ⋯ ∧ L 1 ∧ δ ∧ π ) = P ( L k ∣ R k ∧ δ ∧ π ) {\displaystyle P\left(L_{k}\mid L_{k-1}\wedge \cdots \wedge L_{1}\wedge \delta \wedge \pi \right)=P\left(L_{k}\mid R_{k}\wedge \delta \wedge \pi \right)} We then obtain: P ( X 1 ∧ X 2 ∧ ⋯ ∧ X N ∣ δ ∧ π ) = P ( L 1 ∣ δ ∧ π ) × P ( L 2 ∣ R 2 ∧ δ ∧ π ) × ⋯ × P ( L K ∣ R K ∧ δ ∧ π ) {\displaystyle {\begin{aligned}&P\left(X_{1}\wedge X_{2}\wedge \cdots \wedge X_{N}\mid \delta \wedge \pi \right)\\={}&P\left(L_{1}\mid \delta \wedge \pi \right)\times P\left(L_{2}\mid R_{2}\wedge \delta \wedge \pi \right)\times \cdots \times P\left(L_{K}\mid R_{K}\wedge \delta \wedge \pi \right)\end{aligned}}} Such a simplification of the joint distribution as a product of simpler distributions is called a decomposition, derived using the chain rule. This ensures that each variable appears at the most once on the left of a conditioning bar, which is the necessary and sufficient condition to write mathematically valid decompositions. ==== Forms ==== Each distribution P ( L k ∣ R k ∧ δ ∧ π ) {\displaystyle P\left(L_{k}\mid R_{k}\wedge \delta \wedge \pi \right)} appearing in the product is then associated with either a parametric form (i.e., a function f μ ( L k ) {\displaystyle f_{\mu }\left(L_{k}\right)} ) or a question to another Bayesian program P ( L k ∣ R k ∧ δ ∧ π ) = P ( L ∣ R ∧ δ ^ ∧ π ^ ) {\displaystyle P\left(L_{k}\mid R_{k}\wedge \delta \wedge \pi \right)=P\left(L\mid R\wedge {\widehat {\delta }}\wedge {\widehat {\pi }}\right)} . When it is a form f μ ( L k ) {\displaystyle f_{\mu }\left(L_{k}\right)} , in general, μ {\displaystyle \mu } is a vector of parameters that may depend on R k {\displaystyle R_{k}} or δ {\displaystyle \delta } or both. Learning takes place when some of these parameters are computed using the data set δ {\displaystyle \delta } . An important feature of Bayesian programming is this capacity to use questions to other Bayesian programs as components of the definition of a new Bayesian program. P ( L k ∣ R k ∧ δ ∧ π ) {\displaystyle P\left(L_{k}\mid R_{k}\wedge \delta \wedge \pi \right)} is obtained by some inferences done by another Bayesian program defined by the specifications π ^ {\displaystyle {\widehat {\pi }}} and the data δ ^ {\displaystyle {\widehat {\delta }}} . This is similar to calling a subroutine in classical programming and provides an easy way to build hierarchical models. === Question === Given a description (i.e., P ( X 1 ∧ X 2 ∧ ⋯ ∧ X N ∣ δ ∧ π ) {\displaystyle P\left(X_{1}\wedge X_{2}\wedge \cdots \wedge X_{N}\mid \delta \wedge \pi \right)} ), a question is obtained by partitioning { X 1 , X 2 , ⋯ , X N } {\displaystyle \left\{X_{1},X_{2},\cdots ,X_{N}\right\}} into three sets: the searched variables, the known variables and the free variables. The 3 variables S e a r c h e d {\displaystyle Searched} , K n o w n {\displaystyle Known} and F r e e {\displaystyle Free} are defined as the conjunction of the variables belonging to these sets. A question is defined as the set of distributions: P ( S e a r c h e d ∣ Known ∧ δ ∧ π ) {\displaystyle P\left(Searched\mid {\text{Known}}\wedge \delta \wedge \pi \right)} made of many "instantiated questions" as the cardinal of K n o w n {\displaystyle Known} , each instantiated question being the distribution: P ( Searched ∣ Known ∧ δ ∧ π ) {\displaystyle P\left({\text{Searched}}\mid {\text{Known}}\wedge \delta \wedge \pi \right)} === Inference === Given the joint distribution P ( X 1 ∧ X 2 ∧ ⋯ ∧ X N ∣ δ ∧ π ) {\displaystyle P\left(X_{1}\wedge X_{2}\wedge \cdots \wedge X_{N}\mid \delta \wedge \pi \right)} , it is always possible to compute any possible question using the following general inference: P ( Searched ∣ Known ∧ δ ∧ π ) = ∑ Free [ P ( Searched ∧ Free ∣ Known ∧ δ ∧ π ) ] = ∑ Free [ P ( Searched ∧ Free ∧ Known ∣ δ ∧ π ) ] P ( Known ∣ δ ∧ π ) = ∑ Free [ P ( Searched ∧ Free ∧ Known ∣ δ ∧ π ) ] ∑ Free ∧ Searched [ P ( Searched ∧ Free ∧ Known ∣ δ ∧ π ) ] = 1 Z × ∑ Free [ P ( Searched ∧ Free ∧ Known ∣ δ ∧ π ) ] {\displaystyle {\begin{aligned}&P\left({\text{Searched}}\mid {\text{Known}}\wedge \delta \wedge \pi \right)\\={}&\sum _{\text{Free}}\left[P\left({\text{Searched}}\wedge {\text{Free}}\mid {\text{Known}}\wedge \delta \wedge \

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  • Hilscher netx network controller

    Hilscher netx network controller

    The netX network controller family (based on ASICs), developed by Hilscher Gesellschaft für Systemautomation mbH, is a solution for implementing all proven Fieldbus and Real-Time Ethernet systems. It was the first Multi-Protocol ASIC which combines Real-Time-Ethernet and Fieldbus System in one solution. The Multiprotocol functionality is done over a flexible cpu sub system called XC. Through exchanging some microcode the XC is able to realize beside others a PROFINET IRT Switch, EtherCAT Slave, Ethernet Powerlink HUB, PROFIBUS, CAN bus, CC-Link Industrial Networks Interface. == The Hilscher netX family == === Multiplex Matrix IOs (MMIO) === The Multiplex Matrix is a set of PINs which could be configured freely with peripheral functions. Options are CAN, UART, SPI, I2C, GPIOs, PIOs and SYNC Trigger. === GPIOs === The GPIOs from Hilscher are able to generate Interrupts, could count level or flags, or could be connected to a timer unit to auto generate a PWM. The Resolution of the PWM is normally 10ns. In some netX ASICS is a dedicated Motion unit with a resolution if 1ns is available.

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  • Instagram egg

    Instagram egg

    The Instagram egg is a photo of an egg posted by the account @world_record_egg on the social media platform Instagram. It became a global phenomenon and an internet meme within days of its publication on 4 January 2019. It is the second most-liked Instagram post and was the most-liked Instagram post from 14 January 2019 until 20 December 2022, when it was overtaken by Lionel Messi's post showing him and his teammates celebrating after Argentina won the 2022 FIFA World Cup. The owner of the account was revealed to be Chris Godfrey, a British advertising creative, who later worked with his two friends Alissa Khan-Whelan and CJ Brown on a Hulu commercial featuring the egg, intended to raise mental health awareness. == Background == The photo was originally taken by Serghei Platanov, who then posted it to Shutterstock on 23 June 2015 with the title "eggs isolated on white background". == History == On 4 January 2019, the @world_record_egg account was created, and posted an image of a bird egg with the caption, "Let's set a world record together and get the most liked post on Instagram. Beating the current world record held by Kylie Jenner (18 million)! We got this." Jenner's previous record, the first photo of her daughter Stormi, had garnered a total of 18.4 million likes. The post quickly reached 18.4 million likes in just under 10 days, becoming the most-liked Instagram post at the time. It then continued to rise over 45 million likes in the next 48 hours, surpassing the "Despacito" music video and taking the world record for the most-liked online post (on any media platform) in history. After the account became verified on 14 January 2019, the post rose in popularity and likes, which snowballed into coverage in various media outlets. By 18 March 2019, the post had accumulated over 53.3 million likes, nearly three times the previous record of 18.4 million. It posted frequent updates for a few days in the form of Instagram Stories. Alongside the like tally, as of January 2023 the post has 3.8 million comments. Several individuals tried to claim that they were the account's creator, the claims being dismissed by "the egg" on Instagram direct messages. On 3 February 2019, the creator of the Instagram egg was revealed by Hulu and The New York Times to be Chris Godfrey, a British advertising creative. Alissa Khan-Whelan, his colleague, was also outed. On 18 January 2019, the account posted a second picture of an egg, almost identical to the first one apart from a small crack at the top left. As of 25 February 2019, the post accumulated 11.8 million likes. On 22 January 2019, the account posted a third picture of an egg, this time having two larger cracks. In less than 25 minutes, the post accumulated 1 million likes, and by 25 February 2019, it had accumulated 9.5 million likes. On 29 January 2019, a fourth picture of an egg was posted to the account which has another large crack on the right hand side, attracting 7.6 million likes by 25 February 2019. On 1 February 2019, a fifth picture of an egg was posted with stitching like that of a football, referencing the upcoming Super Bowl. That post had accumulated 6.5 million likes by 25 February 2019. The account promised that it would reveal what was inside the egg on 3 February, on the subscription video on demand service Hulu. The Hulu Instagram egg reveal was used to promote an animation about a mental health campaign. A caption from the clip read, "Recently I've started to crack, the pressure of social media is getting to me. If you're struggling too, talk to someone." The video was later posted on the @world_record_egg Instagram account, and this post received over 33 million views by May 2019. As of May 2020, it had received over 41 million views. On 16 July 2019, Chris Godfrey (the creator of the account) was listed as one of the top 25 most influential people on the internet. On 20 December 2022, the record for the most-liked Instagram post was surpassed by a post from Argentine footballer Lionel Messi, showing him and his teammates celebrating after winning the 2022 FIFA World Cup with their national team. The world record egg responded to being overtaken in likes by Messi with "Today [Lionel Messi] has taken the crown, for now. But I'm still left with one question… Who is the greatest of all time – Cristiano Ronaldo or Leo Messi?" The account sold to Dubai-based investor Mustafa El Fishawy in April 2024 for an undisclosed seven-figure sum. Reed Smith, who advised Godfrey, Brown, and Khan-Whelan in the transaction, stated they opted to sell it to "focus on new ventures." On 3 June, @world_record_egg posted an egg with the flag of Palestine in support of the country during the Gaza war; the post's caption described it as an "Egg for Peace" and hoped to "set a new world record together and get the most liked post on Instagram for a good cause." == Reception == In response to breaking the world record for the most-liked Instagram post, the account's owner wrote "This is madness. What a time to be alive." Hours later, Jenner posted a video on Instagram of her cracking open an egg and pouring its yolk onto the ground, with the caption: "Take that little egg." Pundits pontificated on the meaning of the egg picture's dominance over social media's "first family". As Vogue observed, tapping a heart pictogram is easy, and eggs are "lovable". More pointedly: [T]he attention economy is a scam based on requiring little to no labor from both producer and consumer despite commanding the most space, and therefore value, in our digital lives... but it very well could be: As a metaphor for the fragility of the influencer ecosystem, the egg has broken the Internet. The significance of the event and its massive republishing are a topic of discussion. A University of Westminster researcher of internet memes compared it to the movement to name a scientific research vessel in the United Kingdom as Boaty McBoatface. The Instagrammer's success is a rare victory for the unpaid viral campaign on social media. "There is a bit of an anti-celebrity revolt here – 'look what we can do with a simple egg'" The researcher suggests that the accomplishment of becoming such a widely heralded unpaid viral post may become increasingly rare, as social networks rely more on paid and business promotion. The post's spread has been characterized as a populist backlash against "consumerism" and is seen by some as a triumph of community over celebrity. However, propelled by their popular success, the creators promised to release 'egg-centric' memorabilia. Hundreds of games based on the Instagram egg have appeared on Apple's App Store. The creators of the Instagram egg also reached a deal to promote Hulu.

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  • G.hn

    G.hn

    Gigabit Home Networking (G.hn) is a specification for wired home networking that supports speeds up to 2 Gbit/s and operates over four types of legacy wires: telephone wiring, coaxial cables, power lines and plastic optical fiber. Some benefits of a multi-wire standard are lower equipment development costs and lower deployment costs for service providers (by allowing customer self-install). == History == G.hn was developed under the International Telecommunication Union's Telecommunication Standardization sector (the ITU-T) and promoted by the HomeGrid Forum and several other organizations. ITU-T Recommendation (the ITU's term for standard) G.9960, which received approval on October 9, 2009, specified the physical layers and the architecture of G.hn. The Data Link Layer (Recommendation G.9961) was approved on June 11, 2010. Prominent organizations, including CEPca, HomePNA, and UPA, who were creators of some of these interfaces, rallied behind the latest version of the standard, emphasizing its potential and significance in the home networking domain. Moreover, the ITU-T extended the technology with multiple input, multiple output (MIMO) technology to increase data rates and signaling distance. This new feature was approved in March 2012 under G.9963 Recommendation. The development and promotion of G.hn have been significantly supported by the HomeGrid Forum and several other organizations. The technology was not only designed to address home-networking challenges but also found applications beyond this initial scope, showcasing its versatility and potential in the networking domain. == Technical specifications == === Technical overview === G.hn specifies a single physical layer based on fast Fourier transform (FFT) orthogonal frequency-division multiplexing (OFDM) modulation and low-density parity-check code (LDPC) forward error correction (FEC) code. G.hn includes the capability to notch specific frequency bands to avoid interference with amateur radio bands and other licensed radio services. G.hn includes mechanisms to avoid interference with legacy home networking technologies and also with other wireline systems such as VDSL2 or other types of DSL used to access the home. OFDM systems split the transmitted signal into multiple orthogonal sub-carriers. In G.hn each one of the sub-carriers is modulated using QAM. The maximum QAM constellation supported by G.hn is 4096-QAM (12-bit QAM). The G.hn media access control is based on a time division multiple access (TDMA) architecture, in which a "domain master" schedules Transmission Opportunities (TXOPs) that can be used by one or more devices in the "domain". There are two types of TXOPs: Contention-Free Transmission Opportunities (CFTXOP), which have a fixed duration and are allocated to a specific pair of transmitter and receiver. CFTXOP are used for implementing TDMA Channel Access for specific applications that require quality of service (QoS) guarantees. Shared Transmission Opportunities (STXOP), which are shared among multiple devices in the network. STXOP are divided into Time Slots (TS). There are two types of TS: Contention-Free Time Slots (CFTS), which are used for implementing "implicit" token passing Channel Access. In G.hn, a series of consecutive CFTS is allocated to a number of devices. The allocation is performed by the "domain master" and broadcast to all nodes in the network. There are pre-defined rules that specify which device can transmit after another device has finished using the channel. As all devices know "who is next", there is no need to explicitly send a "token" between devices. The process of "passing the token" is implicit and ensures that there are no collisions during Channel access. Contention-Based Time Slots (CBTS), which are used for implementing CSMA/CARP Channel Access. In general, CSMA systems cannot completely avoid collisions, so CBTS are only useful for applications that do not have strict Quality of Service requirements. ==== Optimization for each medium ==== Although most elements of G.hn are common for all three media supported by the standard (power lines, phone lines and coaxial cable), G.hn includes media-specific optimizations for each media. Some of these media-specific parameters include: OFDM Carrier Spacing: 195.31 kHz in coaxial, 48.82 kHz in phone lines, 24.41 kHz in power lines. FEC Rates: G.hn's FEC can operate with code rates 1/2, 2/3, 5/6, 16/18 and 20/21. Although these rates are not media specific, it is expected that the higher code rates will be used in cleaner media (such as coaxial) while the lower code rates will be used in noisy environments such as power lines. Automatic repeat request (ARQ) mechanisms: G.hn supports operation both with and without ARQ (re-transmission). Although this is not media specific, it is expected that ARQ-less operation is sometimes appropriate for cleaner media (such as coaxial) while ARQ operation is appropriate for noisy environments such as power lines. Power levels and frequency bands: G.hn defines different power masks for each medium. MIMO support: Recommendation G.9963 includes provisions for transmitting G.hn signals over multiple AC wires (phase, neutral, ground), if they are physically available. In July 2016, G.9963 was updated to include MIMO support over twisted pairs. ==== Security ==== G.hn uses the Advanced Encryption Standard (AES) encryption algorithm (with a 128-bit key length) using the CCMP protocol to ensure confidentiality and message integrity. Authentication and key exchange is done following ITU-T Recommendation X.1035. G.hn specifies point-to-point security inside a domain, which means that each pair of transmitter and receiver uses a unique encryption key which is not shared by other devices in the same domain. For example, if node Alice sends data to node Bob, node Eve (in the same domain as Alice and Bob) will not be able to easily eavesdrop their communication. G.hn supports the concept of relays, in which one device can receive a message from one node and deliver it to another node farther away in the same domain. Relaying becomes critical for applications with complex network topologies that need to cover large distances, such as those found in industrial or utility applications. While a relay can read the source and target addresses, it cannot read the message's content due to its body being end-to-end-encrypted. ==== Profiles ==== The G.hn architecture includes the concept of profiles. Profiles are intended to address G.hn nodes with significantly different levels of complexity. In G.hn the higher complexity profiles are proper supersets of lower complexity profiles, so that devices based on different profiles can interoperate with each other. Examples of G.hn devices based on high complexity profiles are Residential Gateways or Set-Top Boxes. Examples of G.hn devices based on low complexity profiles are home automation, home security and smart grid devices. ==== Technical parameters ==== The chart depicts a summary of the crucial technical specifications of the G.hn standard. Many of these technical elements are consistent across different physical media, with variations seen in areas such as Tone Spacing and frequency ranges. This uniformity is essential as it allows silicon manufacturers to produce a singular chip capable of implementing all three media types, leading to cost savings. Presently, G.hn chipsets are compatible with all three media types. This compatibility allows system manufacturers to create devices that can adjust to any wiring type simply by modifying a software configuration in the equipment. === Spectrum === The G.hn spectrum depends on the medium as shown in the diagram below: === Protocol stack === G.hn specifies the physical layer and the data link layer, according to the OSI model. The G.hn Data Link Layer (Recommendation G.9961) is divided into three sub-layers: The Application Protocol Convergence (APC) Layer, which accepts frames (usually in Ethernet format) from the upper layer (Application Entity) and encapsulates them into G.hn APC protocol data units (APDUs). The maximum payload of each APDU is 214 bytes. The logical link control (LLC), which is responsible for encryption, aggregation, segmentation and automatic repeat-request. This sub-layer is also responsible for "relaying" of APDUs between nodes that may not be able to communicate through a direct connection. The medium access control (MAC), which schedules channel access. The G.hn physical layer (Recommendation G.9960) is divided into three sub-layers: The Physical Coding Sub-layer (PCS), responsible for generating PHY headers. The Physical Medium Attachment (PMA), responsible for scrambling and forward error correction coding/decoding. The Physical Medium Dependent (PMD), responsible for bit-loading and OFDM modulation. The interface between the Application Entity and the Data Link Layer is called A-interface. The interface between the Data Link Layer and the ph

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  • Meesho

    Meesho

    Meesho Limited (short for Meri shop, transl. My shop) is an Indian e-commerce company, headquartered in Bengaluru. Founded by Vidit Aatrey and Sanjeev Barnwal in December 2015, Meesho is an online marketplace in categories such as fashion, home and kitchen, beauty and personal care, electronics accessories, and daily use products. == History == Meesho Private Limited, formerly Fashnear Technologies Private Limited, was established by IIT Delhi graduates Vidit Aatrey and Sanjeev Barnwal in December, 2015 In 2016, the founders came up with the idea of re-establishing the platform as Meesho, one that would enable country-wide shipping for resellers with the use of social media sites as tools for marketing. In February 2019, the platform reported having around 209,000 users and about 1.2 million monthly orders, and in March 2020, it reported approximately 563,000 users and 3.1 million monthly orders. In 2021, the Meesho mobile application was ranked among the most downloaded shopping apps globally. In 2022, Meesho had about 120 million monthly users and about 910 million orders were made through the platform, with a gross merchandise value (GMV) of about $5 billion. According to report as of August 2023 Meesho delisted 42 lakh counterfeit listings and 10 lakh restricted products under its initiative Project Suraksha. During the same period, the platform blocked access for over 12,000 user accounts flagged for policy violations. The Court granted injunctive relief by directing domain registrars to suspend the infringing websites. Additionally, the Court ordered law enforcement authorities to initiate criminal investigations, freeze associated financial accounts against the identified offenders. In 2023, Meesho became the fastest shopping app to cross over 500 million downloads. In 2024, Meesho introduced Valmo, a logistics marketplace, to provide shipment services to sellers by aggregating multiple logistics providers. Meesho employs over 3,000 small businesses and 10-12 large firms for warehousing and sorting operations within its logistics framework. According to media reports, Valmo operating in approximately 15,000 pincodes in India with around 6,000 partners. It is reported to handle over 50% of Meesho's daily orders. In November 2024, Meesho introduced a generative AI-powered voice bot for customer support, managing approximately 60,000 calls daily in English and Hindi. According to media reports, the system resolves the majority of queries without human assistance, with only a small fraction of calls requiring manual intervention. According to media reports, in 2024, Meesho prevented over 22 million suspicious or potentially fraudulent transactions on its platform. The company initiated legal proceedings, resulting in the filing of twelve cases, including nine specifically targeting over forty individuals in the cities of Kolkata and Ranchi. The company filed a suit in the Delhi High Court for a permanent injunction against parties operating deceptive websites misappropriating its brand identity. Meesha went public through an initial public offering in December 2025, raising $603 million. It is listed on both the BSE and NSE. == Recognition == In 2023, Meesho was named one of the most influential companies of the year by Time (magazine).

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  • Cipher

    Cipher

    In cryptography, a cipher (or cypher) is an algorithm for performing encryption or decryption—a series of well-defined steps that can be followed as a procedure. An alternative, less common term is encipherment. To encipher or encode is to convert information into cipher or code. In common parlance, "cipher" is synonymous with "code", as they are both a set of steps that encrypt a message; however, the concepts are distinct in cryptography, especially classical cryptography. Codes generally substitute different length strings of characters in the output, while ciphers generally substitute the same number of characters as are input. A code maps one meaning with another. Words and phrases can be coded as letters or numbers. Codes typically have direct meaning from input to key. Codes primarily function to save time. Ciphers are algorithmic. The given input must follow the cipher's process to be solved. Ciphers are commonly used to encrypt written information. Codes operated by substituting according to a large codebook which linked a random string of characters or numbers to a word or phrase. For example, "UQJHSE" could be the code for "Proceed to the following coordinates.". When using a cipher the original information is known as plaintext, and the encrypted form as ciphertext. The ciphertext message contains all the information of the plaintext message, but is not in a format readable by a human or computer without the proper mechanism to decrypt it. The operation of a cipher usually depends on a piece of auxiliary information, called a key (or, in traditional NSA parlance, a cryptovariable). The encrypting procedure is varied depending on the key, which changes the detailed operation of the algorithm. A key must be selected before using a cipher to encrypt a message, with some exceptions such as ROT13 and Atbash. Most modern ciphers can be categorized in several ways: By whether they work on blocks of symbols usually of a fixed size (block ciphers), or on a continuous stream of symbols (stream ciphers). By whether the same key is used for both encryption and decryption (symmetric key algorithms), or if a different key is used for each (asymmetric key algorithms). If the algorithm is symmetric, the key must be known to the recipient and sender and to no one else. If the algorithm is an asymmetric one, the enciphering key is different from, but closely related to, the deciphering key. If one key cannot be deduced from the other, the asymmetric key algorithm has the public/private key property and one of the keys may be made public without loss of confidentiality. == Etymology == Originating from the Sanskrit word for zero शून्य (śuṇya), via the Arabic word صفر (ṣifr), the word "cipher" spread to Europe as part of the Arabic numeral system during the Middle Ages. The Roman numeral system lacked the concept of zero, and this limited advances in mathematics. In this transition, the word was adopted into Medieval Latin as cifra, and then into Middle French as cifre. This eventually led to the English word cipher (also spelt cypher). One theory for how the term came to refer to encoding is that the concept of zero was confusing to Europeans, and so the term came to refer to a message or communication that was not easily understood. The term cipher was later also used to refer to any Arabic digit, or to calculation using them, so encoding text in the form of Arabic numerals is literally converting the text to "ciphers". == Versus codes == In casual contexts, "code" and "cipher" can typically be used interchangeably; however, the technical usages of the words refer to different concepts. Codes contain meaning; words and phrases are assigned to numbers or symbols, creating a shorter message. An example of this is the commercial telegraph code which was used to shorten long telegraph messages which resulted from entering into commercial contracts using exchanges of telegrams. Another example is given by whole word ciphers, which allow the user to replace an entire word with a symbol or character, much like the way written Japanese utilizes Kanji (meaning Chinese characters in Japanese) characters to supplement the native Japanese characters representing syllables. An example using English language with Kanji could be to replace "The quick brown fox jumps over the lazy dog" by "The quick brown 狐 jumps 上 the lazy 犬". Stenographers sometimes use specific symbols to abbreviate whole words. Ciphers, on the other hand, work at a lower level: the level of individual letters, small groups of letters, or, in modern schemes, individual bits and blocks of bits. Some systems used both codes and ciphers in one system, using superencipherment to increase the security. In some cases the terms codes and ciphers are used synonymously with substitution and transposition, respectively. Historically, cryptography was split into a dichotomy of codes and ciphers, while coding had its own terminology analogous to that of ciphers: "encoding, codetext, decoding" and so on. However, codes have a variety of drawbacks, including susceptibility to cryptanalysis and the difficulty of managing a cumbersome codebook. Because of this, codes have fallen into disuse in modern cryptography, and ciphers are the dominant technique. == Types == There are a variety of different types of encryption. Algorithms used earlier in the history of cryptography are substantially different from modern methods, and modern ciphers can be classified according to how they operate and whether they use one or two keys. === Historical === The Caesar Cipher is one of the earliest known cryptographic systems. Julius Caesar used a cipher that shifts the letters in the alphabet in place by three and wrapping the remaining letters to the front to write to Marcus Tullius Cicero in approximately 50 BC. Historical pen and paper ciphers used in the past are sometimes known as classical ciphers. They include simple substitution ciphers (such as ROT13) and transposition ciphers (such as a Rail Fence Cipher). For example, "GOOD DOG" can be encrypted as "PLLX XLP" where "L" substitutes for "O", "P" for "G", and "X" for "D" in the message. Transposition of the letters "GOOD DOG" can result in "DGOGDOO". These simple ciphers and examples are easy to crack, even without plaintext-ciphertext pairs. In the 1640s, the Parliamentarian commander, Edward Montagu, 2nd Earl of Manchester, developed ciphers to send coded messages to his allies during the English Civil War. The English theologian John Wilkins published a book in 1641 titled "Mercury, or The Secret and Swift Messenger" and described a musical cipher wherein letters of the alphabet were substituted for music notes. This species of melodic cipher was depicted in greater detail by author Abraham Rees in his book Cyclopædia (1778). Simple ciphers were replaced by polyalphabetic substitution ciphers (such as the Vigenère) which changed the substitution alphabet for every letter. For example, "GOOD DOG" can be encrypted as "PLSX TWF" where "L", "S", and "W" substitute for "O". With even a small amount of known or estimated plaintext, simple polyalphabetic substitution ciphers and letter transposition ciphers designed for pen and paper encryption are easy to crack. It is possible to create a secure pen and paper cipher based on a one-time pad, but these have other disadvantages. During the early twentieth century, electro-mechanical machines were invented to do encryption and decryption using transposition, polyalphabetic substitution, and a kind of "additive" substitution. In rotor machines, several rotor disks provided polyalphabetic substitution, while plug boards provided another substitution. Keys were easily changed by changing the rotor disks and the plugboard wires. Although these encryption methods were more complex than previous schemes and required machines to encrypt and decrypt, other machines such as the British Bombe were invented to crack these encryption methods. === Modern === Modern encryption methods can be divided by two criteria: by type of key used, and by type of input data. By type of key used ciphers are divided into: symmetric key algorithms (Private-key cryptography), where one same key is used for encryption and decryption, and asymmetric key algorithms (Public-key cryptography), where two different keys are used for encryption and decryption. In a symmetric key algorithm (e.g., DES and AES), the sender and receiver must have a shared key set up in advance and kept secret from all other parties; the sender uses this key for encryption, and the receiver uses the same key for decryption. The design of AES (Advanced Encryption System) was beneficial because it aimed to overcome the flaws in the design of the DES (Data encryption standard). AES's designer's claim that the common means of modern cipher cryptanalytic attacks are ineffective against AES due to its design structure. Ciphers can be distinguished into two types by the type o

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  • Social bot

    Social bot

    A social bot, refers to fully or partially automated social media accounts designed to perform most regular users’ actions, such as liking, posting content, and chatting with other users. Although their levels of autonomy vary, and often include a human-in-the-loop, social bots can use artificial intelligence to perform social media actions and can use large language models to mimic human dialogue. Social bots can operate alone or in groups that coordinate messaging as part of a network of coordinated inauthentic behavior. Social bots are often used to perform ad fraud by artificially boosting viewership and engagement metrics and to spread disinformation on social media. == Uses == Social bots are used for a large number of purposes on a variety of social media platforms, including Twitter, Instagram, Facebook, and YouTube. One common use of social bots is to inflate a social media user's apparent popularity, usually by artificially manipulating their engagement metrics with large volumes of fake likes, reposts, or replies. Social bots can similarly be used to artificially inflate a user's follower count with fake followers, creating a false perception of a larger and more influential online following than is the case. The use of social bots to create the impression of a large social media influence allows individuals, brands, and organizations to attract a higher number of human followers and boost their online presence. Fake engagement can be bought and sold in the black market of social media engagement. Corporations typically use automated customer service agents on social media to affordably manage high levels of support requests. Social bots are used to send automated responses to users’ questions, sometimes prompting the user to private message the support account with additional information. The increased use of automated support bots and virtual assistants has led to some companies laying off customer-service staff. Social bots are also often used to influence public opinion. Autonomous bot accounts can flood social media with large numbers of posts expressing support for certain products, companies, or political campaigns, creating the impression of organic grassroots support. This can create a false perception of the number of people who support a certain position, which may also have effects on the direction of stock prices or on elections. Messages with similar content can also influence fads or trends. Many social bots are also used to amplify phishing attacks. These malicious bots are used to trick a social media user into giving up their passwords or other personal data. This is usually accomplished by posting links claiming to direct users to news articles that would in actuality direct to malicious websites containing malware. Scammers often use URL shortening services such as TinyURL and bit.ly to disguise a link's domain address, increasing the likelihood of a user clicking the malicious link. The presence of fake social media followers and high levels of engagement help convince the victim that the scammer is in fact a trusted user. Social bots can be a tool for computational propaganda. Bots can also be used for algorithmic curation, algorithmic radicalization, and/or influence-for-hire, a term that refers to the selling of an account on social media platforms. == History == Bots have coexisted with computer technology since the earliest days of computing. Social bots have their roots in the 1950s with Alan Turing, whose work focused on machine intelligence with the development of the Turing Test. The following decades saw further progress made towards the goal of creating programs capable of mimicking human behavior, notably with Joseph Weizenbaum’s creation of ELIZA. Considered to be one of the first Chatbots, ELIZA could simulate natural conversations with human users through pattern matching. Its most famous script was DOCTOR, a simulation of a Rogerian psychotherapist that was programmed to chat with patients and respond to questions. With the growth of social media platforms in the early 2000s, these bots could be used to interact with much larger user groups in an inconspicuous manner. Early instances of autonomous agents on social media could be found on sites like MySpace, with social bots being used by marketing firms to inflate activity on a user’s page in an effort to make them appear more popular. Social bots have been observed on a large variety of social media websites, with Twitter being one of the most widely observed examples. The creation of Twitter bots is generally against the site’s terms of service when used to post spam or to automatically like and follow other users, but some degree of automation using Twitter’s API may be permitted if used for “entertainment, informational, or novelty purposes.” Other platforms such as Reddit and Discord also allow for the use of social bots as long as they are not used to violate policies regarding harmful content and abusive behavior. Social media platforms have developed their own automated tools to filter out messages that come from bots, although they cannot detect all bot messages. == Legal regulation == Due to the difficulty of recognizing social bots and separating them from "eligible" automation via social media APIs, it is unclear how legal regulation can be enforced. Social bots are expected to play a role in shaping public opinion by autonomously acting as influencers. Some social bots have been used to rapidly spread misinformation, manipulate stock markets, influence opinion on companies and brands, promote political campaigns, and engage in malicious phishing campaigns. In the United States, some states have started to implement legislation in an attempt to regulate the use of social bots. In 2019, California passed the Bolstering Online Transparency Act (the B.O.T. Act) to make it unlawful to use automated software to appear indistinguishable from humans for the purpose of influencing a social media user's purchasing and voting decisions. Other states such as Utah and Colorado have passed similar bills to restrict the use of social bots. The Artificial Intelligence Act (AI Act) in the European Union is the first comprehensive law governing the use of Artificial Intelligence. The law requires transparency in AI to prevent users from being tricked into believing they are communicating with another human. AI-generated content on social media must be clearly marked as such, preventing social bots from using AI in a manner that mimics human behavior. == Detection == The first generation of bots could sometimes be distinguished from real users by their often superhuman capacities to post messages. Later developments have succeeded in imprinting more "human" activity and behavioral patterns in the agent. With enough bots, it might be even possible to achieve artificial social proof. To unambiguously detect social bots as what they are, a variety of criteria must be applied together using pattern detection techniques, some of which are: cartoon figures as user pictures sometimes also random real user pictures are captured (identity fraud) reposting rate temporal patterns sentiment expression followers-to-friends ratio length of user names variability in (re)posted messages engagement rate (like/followers rate) analysis of the time series of social media posts Social bots are always becoming increasingly difficult to detect and understand. The bots' human-like behavior, ever-changing behavior of the bots, and the sheer volume of bots covering every platform may have been a factor in the challenges of removing them. Social media sites, like Twitter, are among the most affected, with CNBC reporting up to 48 million of the 319 million users (roughly 15%) were bots in 2017. Botometer (formerly BotOrNot) is a public Web service that checks the activity of a Twitter account and gives it a score based on how likely the account is to be a bot. The system leverages over a thousand features. An active method for detecting early spam bots was to set up honeypot accounts that post nonsensical content, which may get reposted (retweeted) by the bots. However, bots evolve quickly, and detection methods have to be updated constantly, because otherwise they may get useless after a few years. One method is the use of Benford's Law for predicting the frequency distribution of significant leading digits to detect malicious bots online. This study was first introduced at the University of Pretoria in 2020. Another method is artificial-intelligence-driven detection. Some of the sub-categories of this type of detection would be active learning loop flow, feature engineering, unsupervised learning, supervised learning, and correlation discovery. Some operations of bots work together in a synchronized way. For example, ISIS used Twitter to amplify its Islamic content by numerous orchestrated accounts which further pushed an item to the Hot List news, thus further a

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  • Signals intelligence

    Signals intelligence

    Signals intelligence (SIGINT) is the act and field of intelligence-gathering by interception of signals, whether communications between people (communications intelligence—abbreviated to COMINT) or from electronic signals not directly used in communication (electronic intelligence—abbreviated to ELINT). As classified and sensitive information is usually encrypted, signals intelligence may necessarily involve cryptanalysis (to decipher the messages). Traffic analysis—the study of who is signaling to whom and in what quantity—is also used to integrate information, and it may complement cryptanalysis. == History == === Origins === Electronic interceptions appeared as early as 1900, during the Boer War of 1899–1902. The British Royal Navy had installed wireless sets produced by Marconi on board their ships in the late 1890s, and the British Army used some limited wireless signalling. The Boers captured some wireless sets and used them to make vital transmissions. Since the British were the only people transmitting at the time, the British did not need special interpretation of the signals that they were. The birth of signals intelligence in a modern sense dates from the Russo-Japanese War of 1904–1905. As the Russian fleet prepared for conflict with Japan in 1904, the British ship HMS Diana stationed in the Suez Canal intercepted Russian naval wireless signals being sent out for the mobilization of the fleet, for the first time in history. === Development in World War I === Over the course of the First World War, a new method of signals intelligence reached maturity. Russia's failure to properly protect its communications fatally compromised the Russian Army's advance early in World War I and led to their disastrous defeat by the Germans under Ludendorff and Hindenburg at the Battle of Tannenberg. In 1918, French intercept personnel captured a message written in the new ADFGVX cipher, which was cryptanalyzed by Georges Painvin. This gave the Allies advance warning of the German 1918 Spring Offensive. The British in particular, built up great expertise in the newly emerging field of signals intelligence and codebreaking (synonymous with cryptanalysis). On the declaration of war, Britain cut all German undersea cables. This forced the Germans to communicate exclusively via either (A) a telegraph line that connected through the British network and thus could be tapped; or (B) through radio which the British could then intercept. Rear Admiral Henry Oliver appointed Sir Alfred Ewing to establish an interception and decryption service at the Admiralty; Room 40. An interception service known as 'Y' service, together with the post office and Marconi stations, grew rapidly to the point where the British could intercept almost all official German messages. The German fleet was in the habit each day of wirelessing the exact position of each ship and giving regular position reports when at sea. It was possible to build up a precise picture of the normal operation of the High Seas Fleet, to infer from the routes they chose where defensive minefields had been placed and where it was safe for ships to operate. Whenever a change to the normal pattern was seen, it immediately signalled that some operation was about to take place, and a warning could be given. Detailed information about submarine movements was also available. The use of radio-receiving equipment to pinpoint the location of any single transmitter was also developed during the war. Captain H.J. Round, working for Marconi, began carrying out experiments with direction-finding radio equipment for the army in France in 1915. By May 1915, the Admiralty was able to track German submarines crossing the North Sea. Some of these stations also acted as 'Y' stations to collect German messages, but a new section was created within Room 40 to plot the positions of ships from the directional reports. Room 40 played an important role in several naval engagements during the war, notably in detecting major German sorties into the North Sea. The battle of Dogger Bank was won in no small part due to the intercepts that allowed the Navy to position its ships in the right place. It played a vital role in subsequent naval clashes, including at the Battle of Jutland as the British fleet was sent out to intercept them. The direction-finding capability allowed for the tracking and location of German ships, submarines, and Zeppelins. The system was so successful that by the end of the war, over 80 million words, comprising the totality of German wireless transmission over the course of the war, had been intercepted by the operators of the Y-stations and decrypted. However, its most astonishing success was in decrypting the Zimmermann Telegram, a telegram from the German Foreign Office sent via Washington to its ambassador Heinrich von Eckardt in Mexico. === Postwar consolidation === With the importance of interception and decryption firmly established by the wartime experience, countries established permanent agencies dedicated to this task in the interwar period. In 1919, the British Cabinet's Secret Service Committee, chaired by Lord Curzon, recommended that a peace-time codebreaking agency should be created. The Government Code and Cypher School (GC&CS) was the first peace-time codebreaking agency, with a public function "to advise as to the security of codes and cyphers used by all Government departments and to assist in their provision", but also with a secret directive to "study the methods of cypher communications used by foreign powers". GC&CS officially formed on 1 November 1919, and produced its first decrypt on 19 October. By 1940, GC&CS was working on the diplomatic codes and ciphers of 26 countries, tackling over 150 diplomatic cryptosystems. The US Cipher Bureau was established in 1919 and achieved some success at the Washington Naval Conference in 1921, through cryptanalysis by Herbert Yardley. Secretary of War Henry L. Stimson closed the US Cipher Bureau in 1929 with the words "Gentlemen do not read each other's mail." === World War II === The use of SIGINT had even greater implications during World War II. The combined effort of intercepts and cryptanalysis for the whole of the British forces in World War II came under the code name "Ultra", managed from Government Code and Cypher School at Bletchley Park. Properly used, the German Enigma and Lorenz ciphers should have been virtually unbreakable, but flaws in German cryptographic procedures, and poor discipline among the personnel carrying them out, created vulnerabilities which made Bletchley's attacks feasible. Bletchley's work was essential to defeating the U-boats in the Battle of the Atlantic, and to the British naval victories in the Battle of Cape Matapan and the Battle of North Cape. In 1941, Ultra exerted a powerful effect on the North African desert campaign against German forces under General Erwin Rommel. General Sir Claude Auchinleck wrote that were it not for Ultra, "Rommel would have certainly got through to Cairo". Ultra decrypts featured prominently in the story of Operation SALAM, László Almásy's mission across the desert behind Allied lines in 1942. Prior to the Normandy landings on D-Day in June 1944, the Allies knew the locations of all but two of Germany's fifty-eight Western Front divisions. Winston Churchill was reported to have told King George VI: "It is thanks to the secret weapon of General Menzies, put into use on all the fronts, that we won the war!" Supreme Allied Commander, Dwight D. Eisenhower, at the end of the war, described Ultra as having been "decisive" to Allied victory. Official historian of British Intelligence in World War II Sir Harry Hinsley argued that Ultra shortened the war "by not less than two years and probably by four years"; and that, in the absence of Ultra, it is uncertain how the war would have ended. At a lower level, German cryptanalysis, direction finding, and traffic analysis were vital to Rommel's early successes in the Western Desert Campaign until British forces tightened their communications discipline and Australian raiders destroyed his principal SIGINT Company. == Technical definitions == The United States Department of Defense has defined the term "signals intelligence" as: A category of intelligence comprising either individually or in combination all communications intelligence (COMINT), electronic intelligence (ELINT), and foreign instrumentation signals intelligence (FISINT), however transmitted. Intelligence derived from communications, electronic, and foreign instrumentation signals. Being a broad field, SIGINT has many sub-disciplines. The two main ones are communications intelligence (COMINT) and electronic intelligence (ELINT). == Disciplines shared across the branches == === Targeting === A collection system has to know to look for a particular signal. "System", in this context, has several nuances. Targeting is the process of developing collection requirements: "1. A

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  • Data classification (data management)

    Data classification (data management)

    Data classification is the process of organizing data into categories based on attributes like file type, content, or metadata. The data is then assigned class labels that describe a set of attributes for the corresponding data sets. The goal is to provide meaningful class attributes to former less structured information, enabling organizations to manage, protect, and govern their data more effectively. Data classification can be viewed as a multitude of labels that are used to define the type of data, especially on confidentiality and integrity issues. == Approaches == Classification techniques might be used for reports generated by ERP systems or where the data includes specific personal information that is identified. Many organizations also employ context-based classification that considers factors such as data source, user identity, and application context. == Regulatory frameworks == Data classification schemes are mandated or implied by numerous regulatory frameworks that require organizations to identify, categorize, and protect sensitive information according to its level of sensitivity. The Health Insurance Portability and Accountability Act (HIPAA) Security Rule requires covered entities to conduct an accurate and thorough assessment of potential risks and vulnerabilities to the confidentiality, integrity, and availability of protected health information under 45 CFR 164.308(a)(1)(ii)(A), which necessitates classification of data to distinguish protected health information from other organizational data."Security Standards: Administrative Safeguards". U.S. Department of Health and Human Services. Retrieved April 1, 2026. The December 2024 HIPAA Security Rule notice of proposed rulemaking (90 FR 898) would mandate comprehensive technology asset inventories and require mapping of how electronic protected health information moves through an organization, formalizing data classification as an explicit compliance obligation."HIPAA Security Rule To Strengthen the Cybersecurity of Electronic Protected Health Information". Federal Register. January 6, 2025. Retrieved April 1, 2026. NIST Special Publication 800-60 provides guidelines for mapping information types to security categories, establishing a structured methodology for federal agencies to classify data and apply appropriate security controls based on the potential impact of a security breach."NIST SP 800-60 Vol. 1 Rev. 1: Guide for Mapping Types of Information and Information Systems to Security Categories". National Institute of Standards and Technology. August 2008. Retrieved April 1, 2026.

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  • List of cryptosystems

    List of cryptosystems

    A cryptosystem is a set of cryptographic algorithms that map ciphertexts and plaintexts to each other. == Private-key cryptosystems == Private-key cryptosystems use the same key for encryption and decryption. Caesar cipher Substitution cipher Enigma machine Data Encryption Standard Twofish Serpent Camellia Salsa20 ChaCha20 Blowfish CAST5 Kuznyechik RC4 3DES Skipjack Safer IDEA Advanced Encryption Standard, also known as AES and Rijndael. == Public-key cryptosystems == Public-key cryptosystems use a public key for encryption and a private key for decryption. Diffie–Hellman key exchange RSA encryption Rabin cryptosystem Schnorr signature ElGamal encryption Elliptic-curve cryptography Lattice-based cryptography McEliece cryptosystem Multivariate cryptography Isogeny-based cryptography

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  • Trust federation

    Trust federation

    A trust federation is part of the evolving Identity Metasystem that will bring a new layer of persistent identity and trusted data sharing to the Internet. Although the concept of trust federations is technology neutral, several protocols like SAML, OpenID, Information Card, XDI can handle the challenges of technical interoperability. The challenge of business and social interoperability requires a new type of cooperative association similar to a credit card association. Instead of banks, however, a trust federation is an alliance of i-brokers and their customers who agree to abide by a common set of agreements in the care and handling of customer data. A model for trust federations is offered by Open Identity Exchange and Kantara Initiative, which is applied in the U.S. Government ICAM Trust Framework. Some operational trust federations are: InCommon (academic, USA) REFEDs (Research and Education Federations, Europe) IGTF Interoperable Global Trust Federation Portalverbund Government Portal Federation, Austria Trust federations are not limited to the social web use case, but apply to all federations where trust in identity and compliance to other objectives of information security such as confidentiality, integrity and privacy is brokered.

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