AI Coding Kya Hota Hai

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  List of computer graphics journals

  List of computer graphics journals includes notable peer-reviewed scientific and academic journals that focus on computer graphics, visualization, and related areas such as rendering, animation, image processing, and geometric modeling. == Journals == ACM Transactions on Graphics Computers & Graphics IEEE Computer Graphics and Applications IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems Graphical Models Journal of Computer Graphics Techniques Presence: Teleoperators and Virtual Environments Virtual Reality Simulation & Gaming

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  Protecting Our Kids from Social Media Addiction Act

  Protecting Our Kids from Social Media Addiction Act also known as California SB 976 is a law that was enacted in September 2024 that is meant to address problematic social media usage among minors. The law prohibitions minors to have "addictive feeds" unless they have verifiable parental consent, minor's notifications are also restricted between 12 am to 6 am and during school hours between 8 am and 3 pm it also well requires minors to have default privacies settings and have social media companies to publicly disclose certain metrics about their users. The law was set to take effect in two steps the first being the restrictions on social media feeds, notifications, disclosures from social media companies and default settings which would have taken effect on January 1, 2025, and the age verification provision which would have taken effect on January 1, 2027. However, has faced legal challenges since its enactment delaying its enactment. == Legal Challenges == In November 2024 NetChoice a trade association representing many of the biggest social media companies such as YouTube, Facebook and Instagram sued the attorney general of California Rob Bonta hoping to get an injunction before the first set of the law's provisions would take effect in January of the next year. However, judge Edward Davila would only grant Netchoice's request as to the restrictions on notifications and public disclosures and would deny their request as to the rest of the law. The law was later fully enjoined temporarily by the District Court and Appellant Court pending appeal, and the case is now in the Ninth Circuit Court of Appeals and is pending a decision. === Social media platforms challenges to law === In November 2025 Meta, Google and TikTok filed lawsuits against the law arguing it violates the first amendment.

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  Batch cryptography

  Batch cryptography is a field of cryptology focused on the design of cryptographic protocols that perform operations—such as encryption, decryption, key exchange, and authentication—on multiple inputs simultaneously, rather than processing each input individually. Batching cryptographic operations can significantly reduce the marginal cost of handling individual inputs—a principle that was first introduced by Amos Fiat in 1989.

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  Locally recoverable code

  Locally recoverable codes are a family of error correction codes that were introduced first by D. S. Papailiopoulos and A. G. Dimakis and have been widely studied in information theory due to their applications related to distributive and cloud storage systems. An [ n , k , d , r ] q {\displaystyle [n,k,d,r]_{q}} LRC is an [ n , k , d ] q {\displaystyle [n,k,d]_{q}} linear code such that there is a function f i {\displaystyle f_{i}} that takes as input i {\displaystyle i} and a set of r {\displaystyle r} other coordinates of a codeword c = ( c 1 , … , c n ) ∈ C {\displaystyle c=(c_{1},\ldots ,c_{n})\in C} different from c i {\displaystyle c_{i}} , and outputs c i {\displaystyle c_{i}} . == Overview == Erasure-correcting codes, or simply erasure codes, for distributed and cloud storage systems, are becoming more and more popular as a result of the present spike in demand for cloud computing and storage services. This has inspired researchers in the fields of information and coding theory to investigate new facets of codes that are specifically suited for use with storage systems. It is well-known that LRC is a code that needs only a limited set of other symbols to be accessed in order to restore every symbol in a codeword. This idea is very important for distributed and cloud storage systems since the most common error case is when one storage node fails (erasure). The main objective is to recover as much data as possible from the fewest additional storage nodes in order to restore the node. Hence, Locally Recoverable Codes are crucial for such systems. The following definition of the LRC follows from the description above: an [ n , k , r ] {\displaystyle [n,k,r]} -Locally Recoverable Code (LRC) of length n {\displaystyle n} is a code that produces an n {\displaystyle n} -symbol codeword from k {\displaystyle k} information symbols, and for any symbol of the codeword, there exist at most r {\displaystyle r} other symbols such that the value of the symbol can be recovered from them. The locality parameter satisfies 1 ≤ r ≤ k {\displaystyle 1\leq r\leq k} because the entire codeword can be found by accessing k {\displaystyle k} symbols other than the erased symbol. Furthermore, Locally Recoverable Codes, having the minimum distance d {\displaystyle d} , can recover d − 1 {\displaystyle d-1} erasures. == Definition == Let C {\displaystyle C} be a [ n , k , d ] q {\displaystyle [n,k,d]_{q}} linear code. For i ∈ { 1 , … , n } {\displaystyle i\in \{1,\ldots ,n\}} , let us denote by r i {\displaystyle r_{i}} the minimum number of other coordinates we have to look at to recover an erasure in coordinate i {\displaystyle i} . The number r i {\displaystyle r_{i}} is said to be the locality of the i {\displaystyle i} -th coordinate of the code. The locality of the code is defined as An [ n , k , d , r ] q {\displaystyle [n,k,d,r]_{q}} locally recoverable code (LRC) is an [ n , k , d ] q {\displaystyle [n,k,d]_{q}} linear code C ∈ F q n {\displaystyle C\in \mathbb {F} _{q}^{n}} with locality r {\displaystyle r} . Let C {\displaystyle C} be an [ n , k , d ] q {\displaystyle [n,k,d]_{q}} -locally recoverable code. Then an erased component can be recovered linearly, i.e. for every i ∈ { 1 , … , n } {\displaystyle i\in \{1,\ldots ,n\}} , the space of linear equations of the code contains elements of the form x i = f ( x i 1 , … , x i r ) {\displaystyle x_{i}=f(x_{i_{1}},\ldots ,x_{i_{r}})} , where i j ≠ i {\displaystyle i_{j}\neq i} . == Optimal locally recoverable codes == Theorem Let n = ( r + 1 ) s {\displaystyle n=(r+1)s} and let C {\displaystyle C} be an [ n , k , d ] q {\displaystyle [n,k,d]_{q}} -locally recoverable code having s {\displaystyle s} disjoint locality sets of size r + 1 {\displaystyle r+1} . Then An [ n , k , d , r ] q {\displaystyle [n,k,d,r]_{q}} -LRC C {\displaystyle C} is said to be optimal if the minimum distance of C {\displaystyle C} satisfies == Tamo–Barg codes == Let f ∈ F q [ x ] {\displaystyle f\in \mathbb {F} _{q}[x]} be a polynomial and let ℓ {\displaystyle \ell } be a positive integer. Then f {\displaystyle f} is said to be ( r {\displaystyle r} , ℓ {\displaystyle \ell } )-good if • f {\displaystyle f} has degree r + 1 {\displaystyle r+1} , • there exist distinct subsets A 1 , … , A ℓ {\displaystyle A_{1},\ldots ,A_{\ell }} of F q {\displaystyle \mathbb {F} _{q}} such that – for any i ∈ { 1 , … , ℓ } {\displaystyle i\in \{1,\ldots ,\ell \}} , f ( A i ) = { t i } {\displaystyle f(A_{i})=\{t_{i}\}} for some t i ∈ F q {\displaystyle t_{i}\in \mathbb {F} _{q}} , i.e., f {\displaystyle f} is constant on A i {\displaystyle A_{i}} , – # A i = r + 1 {\displaystyle \#A_{i}=r+1} , – A i ∩ A j = ∅ {\displaystyle A_{i}\cap A_{j}=\varnothing } for any i ≠ j {\displaystyle i\neq j} . We say that { A 1 , … , A ℓ {\displaystyle A_{1},\ldots ,A_{\ell }} } is a splitting covering for f {\displaystyle f} . === Tamo–Barg construction === The Tamo–Barg construction utilizes good polynomials. • Suppose that a ( r , ℓ ) {\displaystyle (r,\ell )} -good polynomial f ( x ) {\displaystyle f(x)} over F q {\displaystyle \mathbb {F} _{q}} is given with splitting covering i ∈ { 1 , … , ℓ } {\displaystyle i\in \{1,\ldots ,\ell \}} . • Let s ≤ ℓ − 1 {\displaystyle s\leq \ell -1} be a positive integer. • Consider the following F q {\displaystyle \mathbb {F} _{q}} -vector space of polynomials V = { ∑ i = 0 s g i ( x ) f ( x ) i : deg ⁡ ( g i ( x ) ) ≤ deg ⁡ ( f ( x ) ) − 2 } . {\displaystyle V=\left\{\sum _{i=0}^{s}g_{i}(x)f(x)^{i}:\deg(g_{i}(x))\leq \deg(f(x))-2\right\}.} • Let T = ⋃ i = 1 ℓ A i {\textstyle T=\bigcup _{i=1}^{\ell }A_{i}} . • The code { ev T ⁡ ( g ) : g ∈ V } {\displaystyle \{\operatorname {ev} _{T}(g):g\in V\}} is an ( ( r + 1 ) ℓ , ( s + 1 ) r , d , r ) {\displaystyle ((r+1)\ell ,(s+1)r,d,r)} -optimal locally coverable code, where ev T {\displaystyle \operatorname {ev} _{T}} denotes evaluation of g {\displaystyle g} at all points in the set T {\displaystyle T} . === Parameters of Tamo–Barg codes === • Length. The length is the number of evaluation points. Because the sets A i {\displaystyle A_{i}} are disjoint for i ∈ { 1 , … , ℓ } {\displaystyle i\in \{1,\ldots ,\ell \}} , the length of the code is | T | = ( r + 1 ) ℓ {\displaystyle |T|=(r+1)\ell } . • Dimension. The dimension of the code is ( s + 1 ) r {\displaystyle (s+1)r} , for s {\displaystyle s} ≤ ℓ − 1 {\displaystyle \ell -1} , as each g i {\displaystyle g_{i}} has degree at most deg ⁡ ( f ( x ) ) − 2 {\displaystyle \deg(f(x))-2} , covering a vector space of dimension deg ⁡ ( f ( x ) ) − 1 = r {\displaystyle \deg(f(x))-1=r} , and by the construction of V {\displaystyle V} , there are s + 1 {\displaystyle s+1} distinct g i {\displaystyle g_{i}} . • Distance. The distance is given by the fact that V ⊆ F q [ x ] ≤ k {\displaystyle V\subseteq \mathbb {F} _{q}[x]_{\leq k}} , where k = r + 1 − 2 + s ( r + 1 ) {\displaystyle k=r+1-2+s(r+1)} , and the obtained code is the Reed-Solomon code of degree at most k {\displaystyle k} , so the minimum distance equals ( r + 1 ) ℓ − ( ( r + 1 ) − 2 + s ( r + 1 ) ) {\displaystyle (r+1)\ell -((r+1)-2+s(r+1))} . • Locality. After the erasure of the single component, the evaluation at a i ∈ A i {\displaystyle a_{i}\in A_{i}} , where | A i | = r + 1 {\displaystyle |A_{i}|=r+1} , is unknown, but the evaluations for all other a ∈ A i {\displaystyle a\in A_{i}} are known, so at most r {\displaystyle r} evaluations are needed to uniquely determine the erased component, which gives us the locality of r {\displaystyle r} . To see this, g {\displaystyle g} restricted to A j {\displaystyle A_{j}} can be described by a polynomial h {\displaystyle h} of degree at most deg ⁡ ( f ( x ) ) − 2 = r + 1 − 2 = r − 1 {\displaystyle \deg(f(x))-2=r+1-2=r-1} thanks to the form of the elements in V {\displaystyle V} (i.e., thanks to the fact that f {\displaystyle f} is constant on A j {\displaystyle A_{j}} , and the g i {\displaystyle g_{i}} 's have degree at most deg ⁡ ( f ( x ) ) − 2 {\displaystyle \deg(f(x))-2} ). On the other hand | A j ∖ { a j } | = r {\displaystyle |A_{j}\backslash \{a_{j}\}|=r} , and r {\displaystyle r} evaluations uniquely determine a polynomial of degree r − 1 {\displaystyle r-1} . Therefore h {\displaystyle h} can be constructed and evaluated at a j {\displaystyle a_{j}} to recover g ( a j ) {\displaystyle g(a_{j})} . === Example of Tamo–Barg construction === We will use x 5 ∈ F 41 [ x ] {\displaystyle x^{5}\in \mathbb {F} _{41}[x]} to construct [ 15 , 8 , 6 , 4 ] {\displaystyle [15,8,6,4]} -LRC. Notice that the degree of this polynomial is 5, and it is constant on A i {\displaystyle A_{i}} for i ∈ { 1 , … , 8 } {\displaystyle i\in \{1,\ldots ,8\}} , where A 1 = { 1 , 10 , 16 , 18 , 37 } {\displaystyle A_{1}=\{1,10,16,18,37\}} , A 2 = 2 A 1 {\displaystyle A_{2}=2A_{1}} , A 3 = 3 A 1 {\displaystyle A_{3}=3A_{1}} , A 4 = 4 A 1 {\displaystyle A_{4}=4A_{1}} , A 5 = 5 A 1 {\displaystyle A_{5}=5A_{1}} , A 6 = 6 A 1 {\displaystyle A_{6}=6A_{1}}

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  Shape analysis (digital geometry)

  This article describes shape analysis to analyze and process geometric shapes. == Description == Shape analysis is the (mostly) automatic analysis of geometric shapes, for example using a computer to detect similarly shaped objects in a database or parts that fit together. For a computer to automatically analyze and process geometric shapes, the objects have to be represented in a digital form. Most commonly a boundary representation is used to describe the object with its boundary (usually the outer shell, see also 3D model). However, other volume based representations (e.g. constructive solid geometry) or point based representations (point clouds) can be used to represent shape. Once the objects are given, either by modeling (computer-aided design), by scanning (3D scanner) or by extracting shape from 2D or 3D images, they have to be simplified before a comparison can be achieved. The simplified representation is often called a shape descriptor (or fingerprint, signature). These simplified representations try to carry most of the important information, while being easier to handle, to store and to compare than the shapes directly. A complete shape descriptor is a representation that can be used to completely reconstruct the original object (for example the medial axis transform). == Application fields == Shape analysis is used in many application fields: archeology for example, to find similar objects or missing parts architecture for example, to identify objects that spatially fit into a specific space medical imaging to understand shape changes related to illness or aid surgical planning virtual environments or on the 3D model market to identify objects for copyright purposes security applications such as face recognition entertainment industry (movies, games) to construct and process geometric models or animations computer-aided design and computer-aided manufacturing to process and to compare designs of mechanical parts or design objects. == Shape descriptors == Shape descriptors can be classified by their invariance with respect to the transformations allowed in the associated shape definition. Many descriptors are invariant with respect to congruency, meaning that congruent shapes (shapes that could be translated, rotated and mirrored) will have the same descriptor (for example moment or spherical harmonic based descriptors or Procrustes analysis operating on point clouds). Another class of shape descriptors (called intrinsic shape descriptors) is invariant with respect to isometry. These descriptors do not change with different isometric embeddings of the shape. Their advantage is that they can be applied nicely to deformable objects (e.g. a person in different body postures) as these deformations do not involve much stretching but are in fact near-isometric. Such descriptors are commonly based on geodesic distances measures along the surface of an object or on other isometry invariant characteristics such as the Laplace–Beltrami spectrum (see also spectral shape analysis). There are other shape descriptors, such as graph-based descriptors like the medial axis or the Reeb graph that capture geometric and/or topological information and simplify the shape representation but can not be as easily compared as descriptors that represent shape as a vector of numbers. From this discussion it becomes clear, that different shape descriptors target different aspects of shape and can be used for a specific application. Therefore, depending on the application, it is necessary to analyze how well a descriptor captures the features of interest.

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  Menu hack

  A menu hack is a non-standard method of ordering food, usually at fast-food or fast casual restaurants, that offers a different result than what is explicitly stated on a menu. Menu hacks may range from a simple alternate flavor to "gaming the system" in order to obtain more food than normal. They are often spread on social media platforms such as TikTok, and are more popular with Generation Z, which has been known to customize their orders more than previous generations. Hacks are sometimes officially added to the menu after their popularity grows. However, in some cases, they have been criticized for overburdening fast food employees with outlandish requests, sparking debate as to whether certain menu hacks are unethical. The list of all possible menu hacks is called a secret menu. == History == The term "menu hack" stems from hacker culture and its tradition of overcoming previously imposed limitations. However, the tradition of ordering from a secret menu dates back to the early days of fast food. "Animal style" fries, a word of mouth menu item ordered from In-N-Out since the 1960s, was rumored to have been created by local surfers. In the Information Age, the rise of social media gave influencers the ability to communicate unique food combinations to their followers, which proved to go viral easily. Design mistakes in food ordering apps also proved to be easily exploitable. In some cases, these hacks boosted the profile of brands on social media, while in others, they caused financial harm when the company was unprepared to handle the sudden influx of unusual orders. One restaurant chain notable for the phenomenon is Chipotle Mexican Grill. A viral hack from Alexis Frost, suggesting a quesadilla with fajita vegetables inside, dipped in Chipotle vinaigrette mixed with sour cream, obtained 1.9 million views on TikTok, overloading the chain's workers, who had to work harder to prepare more vegetables and vinaigrette. Some restaurants began to deny the dish to customers, forcing them to only order meat and cheese on quesadillas. The company ultimately left the dish on the menu, but urged customers to stop ordering it via social media. When it later officially added the Fajita Quesadilla to the menu, digital sales nearly doubled. A method to order nachos, which are not officially on the menu, was also noted by customers. Starbucks is also famous for menu hacks, including the Pink Drink, a "Barbiecore" beverage in which coconut milk replaced the water in the strawberry açaí refresher. After it went viral, the company made it a permanent menu item and distributed it bottled in grocery stores. == Controversy == Menu hacks have been subject to a growing backlash, with employees stating that they "dread" younger customers due to the proliferation of unusual orders. Service industry workers, already overworked and underpaid, have called the rise of menu hacks and their difficulty to make an additional reason to unionize and demand higher wages.

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  Air Force Network

  Air Force Network (AFNet) is an Indian Air Force (IAF) owned, operated and managed digital information grid. The AFNet replaces the Indian Air Force's (IAF) old communication network set-up using the tropo-scatter technology of the 1950s making it a true net-centric combat force. The IAF project is part of the overall mission to network all three services; The Indian Army, The Indian Navy and The Indian Air Force. The former Defence Minister AK Antony inaugurated the IAF's the AFNET on 14 September 2010 dedicating it to the people of India, for their direct or indirect participation in the communication revolution. == Background == Armed Forces in India has been using troposcatters as primary means of military communications since the 1950s, thereby occupying huge and expensive 2G and 3G spectrums which otherwise could have been used for expanding and de-clogging the civilian wireless communication network. The rapid expansion of civilian mobile telephony leading to need for larger bandwidth for wireless communication and commercial need to operate the 3G network necessitated the Government of India to have the Indian Armed Forces vacate the spectrum occupied by them. Thus the government of India through Department of Telecommunication (DoT) started a project called "Network for Spectrum" to set up a fiber optics network for the exclusive use of Indian Armed Forces in exchange for spectrum being released by the Defence Forces. The aim of 'Network for Spectrum' being twofold - to facilitate the growth of national tele-density on the one hand, and ensuring modernization of defence communications with the state-of-the-art communication infrastructure, and to support net-centric military operations. The Department of Telecom and the Ministry of Defence signed the memorandum of understanding for vacating the spectrum and setting up dedicated network for the use of defence forces. In this MoU, DoT agreed to laying of 40,000 route kilometres of optical fibre cable connecting 219 Army stations, 33 Navy stations and 162 points for the Air Force. It further agreed to setting up an exclusive defence band and Defence Interest Zone along 100 km of the international border, where spectrum will be reserved only for use by the Armed Forces. The total cost of implementing "Network for Spectrum" project is estimated to be ₹ 10,000 crores. AFNet is Indian Air Force component of Digital Information Grid under "Network for Spectrum" project and the AFNet has been extended and connected to the Digital Information Grid Project under implementation for the Indian Navy and the Indian Army on 2015. == Project Origin == The Air Force Network (AFNet) had been developed by the Indian Air Force at a cost of ₹1,077 crore (US$235.53 million) in collaboration with HCL Technologies and Bharat Sanchar Nigam Limited. It will replace the Air Force's more than half-a-century-old telecom network. This project is part of the defence ministry's initiative to digitize the communication systems of the three armed forces under "Network for Spectrum" initiative to improve coordination among themselves and other Military and Strategic Institution. IAF was the first to complete this gigabyte digital information grid implemented under the AFNet project. AFNet will be connected and extended to a Unified Digital Grid encompassing all the legs of Indian Armed Forces. The then defence minister, A. K. Antony, inaugurated the AFNet, IAF's gigabyte digital information grid. The grid is aimed at improving the network-centric warfare capability of the Air Force. The event also saw the presence of other personalities including the then Minister of Communication & IT, A. Raja; the Marshal of the Air Force, Arjan Singh; the Chief of the Air Staff, the Chief of the Army Staff and other officials from the three services and members of the Industry. The event also featured a practice interception of a simulated aerial target by a MiG-29 which took off from an airbase in the Punjab sector using the AFNet capabilities. Further capabilities in line with network centric warfare were also demonstrated. This included sharing information, videos and pictures by operational assets and platforms like UAVs and AWACS to decision-makers who are several hundred kilometres apart. == Technology, Design & Structure == AFNet incorporates the latest traffic transportation technology in form of Internet Protocol (IP) packets over the network using Multiprotocol Label Switching (MPLS). A large Voice over Internet Protocol (VoIP) layer with stringent quality of service enforcement will facilitate robust, high quality voice, video and conferencing solutions. AFNet will prove to be an effective force multiplier for intelligence analysis, mission planning and control, post-mission feedback and related activities like maintenance, logistics and administration. A comprehensive design with multi-layer security precautions for “Defence in Depth” have been planned by incorporating encryption technologies, Intrusion Prevention Systems to ensure the resistance of the IT system against information manipulation and eavesdropping. The network is secured with a host of advanced state-of-the-art encryption technologies. It is designed for high reliability with redundancy built into the network design itself. The AFNet is also capable of transmitting video from unmanned surveillance aircraft (UAV), pictures from airborne warning and control systems (AWACS) to decision makers on the ground and providing intelligence inputs from remote areas. The AFNet is also expected to facilitate accelerated economic growth by providing radio frequency spectrum for telecommunication purposes. AFNET will be the largest Multi-protocol Label Switching (MPLS) network in the defence segment. == Demonstration == At the AFNet launch, the IAF showcased a practice interception of simulated enemy targets by a pair of Mig-29 fighter aircraft airborne from an advanced airbase in the Punjab sector using the gigabyte digital information grid. During the AFNet-assisted operations, the Indian fighter jets neutralised intruding targets in the western sector, which was played out live on the giant screens at the Air Force auditorium offering a glimpse of the harnessed potential of the system. The final orders for engaging the enemy targets were issued live by Antony, whose queries about how the operation went were responded to by the pilot as "excellent". Various other functionalities contributing towards Network Centric Warfare were also showcased. These consisted of facilitating video from Unmanned Aerial Vehicle (UAV), pictures from an AWACS aircraft to the decision-makers on ground sitting hundreds of kilometres away, providing intelligence inputs from far-flung areas at central locations seamlessly. This was possible mainly because of the robust networking platform provided by AFNet. == Integrated Air Command and Control System == Integrated Air Command and Control System (IACCS) is an automated command and control system for air defence operated by the Indian Air Force. IACCS operations rides the AFNET backbone integrating all ground-based and airborne sensors, air defense weapon systems and command and control (C2) nodes. Subsequent integration with other services networks and civil radars will provide an integrated Air Situation Picture to operators to carry out AD role. The project was envisaged in 1995 following the Purulia arms drop case and was a part of IAF’s first Air Power Doctrinal manual issued in the 2000s, later revised in 2022. The first node in the western sectors had been operationalised by September 2010. The first five nodes located in the western and south western sectors were commissioned in 2011. The Air Force was preparing to seek clearance for five further nodes which would cover the rest of the nation including the island territories. Through the IACCS, IAF will connect all of its space, air and ground assets quickly, for total awareness of a region. This will offer connectivity for all the ground platforms and airborne platforms (including AEW&C), as a part of the network centricity of IAF. The IACCS also facilitates real-time transport of images, data and voice, amongst satellites, aircraft and ground stations. By 2018, five IACCS nodes had been established including Barnala (Punjab), Wadsar (Gujarat), Aya Nagar (Delhi), Jodhpur (Rajasthan) and Ambala (Haryana). Following this, under Phase-II, 4 additional nodes and 10 sub-nodes are to be set up. The major nodes will be established in the Eastern, Central, Southern and Andaman and Nicobar sectors. The second phase will cost ₹8,000 crore (equivalent to ₹110 billion or US$1.1 billion in 2023). IACCS successfully integrated all operating radars, including its own, the Army's, and civilian ones, in 2023. This enabled the autonomous firing response capability to take down incoming missiles, aircraft, and UAVs. The Akashteer system of the Indian Army is being integrated with the IACCS

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  KLJN Secure Key Exchange

  Random-resistor-random-temperature Kirchhoff-law-Johnson-noise key exchange, also known as RRRT-KLJN or simply KLJN, is an approach for distributing cryptographic keys between two parties that claims to offer unconditional security. This claim, which has been contested, is significant, as the only other key exchange approach claiming to offer unconditional security is Quantum key distribution. The KLJN secure key exchange scheme was proposed in 2005 by Laszlo Kish and Granqvist. It has the advantage over quantum key distribution in that it can be performed over a metallic wire with just four resistors, two noise generators, and four voltage measuring devices---equipment that is low-priced and can be readily manufactured. It has the disadvantage that several attacks against KLJN have been identified which must be defended against. "Given that the amount of effort and funding that goes into Quantum Cryptography is substantial (some even mock it as a distraction from the ultimate prize which is quantum computing), it seems to me that the fact that classic thermodynamic resources allow for similar inherent security should give one pause," wrote Henning Dekant, the founder of the Quantum Computing Meetup, in April 2013. The Cybersecurity Curricula 2017, a joint project of the Association for Computing Machinery, the IEEE Computer Society, the Association for Information Systems, and the International Federation for Information Processing Technical Committee on Information Security Education (IFIP WG 11.8) recommends teaching the KLJN Scheme as part of teaching "Advanced concepts" in its knowledge unit on cryptography. == See Also/Further Reading ==

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  GNU Binutils

  The GNU Binary Utilities, or binutils, is a collection of programming tools maintained by the GNU Project for working with executable code including assembly, linking and many other development operations. The tools are originally from Cygnus Solutions. The tools are typically used along with other GNU tools such as GNU Compiler Collection, and the GNU Debugger. == Tools == The tools include: == elfutils == Ulrich Drepper wrote elfutils, to partially replace GNU Binutils, purely for Linux and with support only for ELF and DWARF. It distributes three libraries with it for programmatic access.

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  G.9972

  G.9972 (also known as G.cx) is a Recommendation developed by ITU-T that specifies a coexistence mechanism for networking transceivers capable of operating over electrical power line wiring. It allows G.hn devices to coexist with other devices implementing G.9972 and operating on the same power line wiring. G.9972 received consent during the meeting of ITU-T Study Group 15, on October 9, 2009, and final approval on June 11, 2010. G.9972 specifies two mechanisms for coexistence between G.hn home networks and broadband over power lines (BPL) Internet access networks: Frequency-division multiplexing (FDM), in which the available spectrum is divided into two parts: frequencies below 10 or 14 MHz (specific value can be selected by the access network) are reserved for the access network, while frequencies above them are reserved for the in-home network. Time-division multiplexing (TDM), in which the available channel time is split equally between both networks. 50% of time slots are allocated for the access network, and 50% are allocated to the in-home network.

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  SIGINT Activity Designator

  A SIGINT Activity Designator (or SIGAD) identifies a signals intelligence (SIGINT) line of collection activity associated with a signals collection station, such as a base or a ship. For example, the SIGAD for Menwith Hill in the UK is USD1000. SIGADs are used by the signals intelligence agencies of Australia, Canada, New Zealand, the United Kingdom, and the United States (the Five Eyes). There are several thousand SIGADs including the substation SIGADs denoted with a trailing alpha character. Several dozen of these are significant. The leaked Boundless Informant reporting screenshot showed that it summarized 504 active SIGADs during a 30-day period in March 2013. == General format == A SIGAD consists of five to eight case insensitive alphanumeric characters. It takes the general form of an alphanumeric designator normally composed of a two- or three-letter prefix followed by one to three numbers. Often a dash is used to separate the alphabetic and numeric characters in the primary part of the designator, but less frequently a space is used as a separator or the alphabetic and numeric characters are concatenated together. An additional alphabetic character can be added to denote a sub-designator for a subset of the primary unit, such as a detachment. Lastly, a numeric character can be added after the aforementioned alphabetic to provide for a sub-sub-designator. In the examples below an X represents an alphabetic character and an N represents a numeric character that are part of the primary designator. Likewise, an x represents an alphabetic character and an n represents a numeric character that are part of a sub-designator. Here are valid generalized examples of SIGADs: The first two characters show which country operates the particular SIGINT facility, which can be US for the United States, UK for the United Kingdom, CA for Canada, AU for Australia and NZ for New Zealand. A third letter shows what sort of staff runs the station. SIGADs beginning with US without a third letter are used for intercept facilities run by the NSA. == PRISM SIGAD == One prominent SIGAD as of April 2013 is US-984XN, with an unclassified codename of PRISM. It is "the number one source of raw intelligence used for NSA analytic reports" according to National Security Agency sources in a document leaked by Edward Snowden. The President's Daily Brief, an all-source intelligence product, cited SIGAD US-984XN as a source in 1,477 items in 2012. The U.S. government operates the PRISM electronic surveillance collection program through NSA's Special Source Operations, an alliance with trusted telecommunications providers. == SIGADs for spy ships == The declassified SIGAD for the USS Liberty (AGTR-5) was USN-855. The USS Liberty incident occurred on 8 June 1967, during the Six-Day War, when Israeli Air Force jet fighter aircraft and Israeli Navy motor torpedo boats attacked the USS Liberty in international waters. The USS Pueblo (AGER-2) was a technical research ship, which was boarded and captured by North Korean forces on 23 January 1968, in what is known as the Pueblo incident. The declassified SIGAD for the NSA Direct Support Unit (DSU) from the Naval Security Group (NSG) on the USS Pueblo patrol involved in the incident was USN-467Y. The USS Pueblo, which officially remains a commissioned vessel of the United States Navy, is the only ship of the U.S. Navy currently being held captive. == Vietnam War SIGADs == The following are the Vietnam War-era declassified SIGADs from inside South Vietnam during the period of 1969 to 1975: Some locations have multiple SIGADs due to different types of collection activities and/or collection at different times during the period. The SIGADs beginning with USA were operated by the United States Air Force's United States Air Force Security Service (USAFSS). The SIGADs beginning with USM were operated by the United States Army's Army Security Agency (ASA). Lastly, the SIGADs beginning with USN were operated by the United States Navy's Naval Security Group (NAVSECGRU). All three of these units have been merged into other units or inactivated. The above list consists of the higher-echelon SIGADs. It does not include the numerous miscellaneous and temporary detachments, or direction finding stations belonging to major units or sites unless that detachment or site was the only one stationed in South Vietnam. Many of the "dets" were short-lived, often formed to support ongoing MACV operations or forward deployments of combat operational or maneuver units. These detachments usually were designated by a letter suffix attached to the higher-echelon SIGAD such as "USM-633J," which was a detachment of the 372d Radio Research Company, USM-633, supporting the United States Army's 25th Infantry Division. === Supporting Southeast Asia SIGADs === The following declassified SIGADs were highly relevant to the Vietnam Campaign, but were located in areas outside of South Vietnam in Southeast Asia. Again, detachments are not listed separately. In the case of the USS Maddox, naval Direct Support Units (DSUs) used the SIGAD USN-467 as a generic designator for their missions. Each specific patrol received a letter suffix for its duration. The subsequent mission would receive the next letter in an alphabetic sequence. Thus, SIGAD USN-467N specifically designates the USS Maddox patrol involved with the Gulf of Tonkin incident. == Joint Base SIGADs == In November 2005, the US Congress performed a fifth round of Base Realignment and Closure. This 2005 law also created twelve joint bases by merging adjacent installations belonging to different services in an effort to reduce costs and improve efficiencies. Joint bases with a primarily SIGINT mission have SIGADs that begin with USJ. A joint base would have a primary SIGAD in the general form of USJ-NNN, where NNN are numeric characters. An actual example is not given, since these units are currently active.

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  Social media use in health awareness

  Social media is being increasingly used for health awareness. It is not only used to promote health and wellness but also to motivate and guide public for various disease and ailments. Use of social media was proven to be cornerstone for awareness during COVID-19 management. In recent times, it is one of the most cost effective tool for cardiovascular health awareness since it can be used to motivate people for adoption of healthy lifestyle practices. Over the span of a decade, and Doctor Mike utilized social media to significantly impact the public about cardiovascular health awareness. == Background == Social media is proven to be useful for various chronic and incurable diseases where patients form groups and connect for sharing of knowledge. Similarly, health professionals, health institutions, and various other individuals and organizations have their own social media accounts for health information, awareness, guidance, or motivation for their patients. The utilization of social media for health awareness campaigns has become increasingly prevalent in recent years. The history of utilizing social media in health campaigns can be traced back to the early 2000s with the rise of platforms such as Facebook, Twitter, and YouTube. == Health campaigns == Health campaigns especially for chronic diseases like cancer and heart diseases are increasingly common on different social media platforms because social media serves as a cost-effective medium for launching and promoting health campaigns. Many organizations and governmental bodies use platforms like Twitter and Instagram to reach a wide audience. This wide outreach gives health campaigns more attention and support while raising awareness of their specific cause. Recently, there have been increasing calls for health organizations to involve the public and consumer groups in their social media health campaigns to ensure their acceptability with the target audience, encouraging use of collaborations and co-design of messages. == Research == When incorporating social media into health research recruitment, there is potential for a greater number of individuals to participate. Social media allows researchers to reach a wide range of participants while also allowing for recruitment 24 hours a day. There are many health organizations with large social media followings to allow them to reach a large amount of individuals. If these organizations pair with researchers and post flyers or make posts about a study they may be able to find the population that they are looking for. Although there are positives to using social media for health research recruitment, looking at the issues is important. Using this method in recruitment may cause competition between companies for the attention of the users. Another important point is that this is dependent on the type of health condition that is being researched. For chronic conditions, there are many organizations and platforms for support while for acute illnesses, there are not as many organizations that would be able to promote these studies and post for outreach. == Patient education == Patients increasingly turn to social media for health communication and health-related information. Online health communities, forums and blogs enable individuals to share their experiences, offer support, and seek advice from peers. Healthcare professionals also use social media to provide valuable insights and address common health concerns. The use of social media for patient education allows individuals to gain more information for their illness or disease along with gaining support from individuals who may be experiencing the same. Many health organizations such as cancer organizations or organizations for chronic health conditions often have social media platforms that allow individuals to connect and even share their own stories. Peer support is beneficial to patients emotionally and even for them to understand their condition and how to cope. Another way that social media allows individuals to gain more information is the improvement of health literacy. Medical jargon can be confusing for individuals especially when they are newly diagnosed with an illness or disease. Social media has been able to create platforms that explain the information that individuals may need when they are newly diagnosed or if they just want to learn more about their illness. Medical conditions can be confusing but using social media may allow for individuals to develop a better understanding in a manner that they understand. When patients have a better understanding of their health there will be a result of better health outcomes. == Misinformation == While social media is a powerful tool for health awareness, it comes with challenges. Misinformation can spread rapidly, potentially leading to incorrect or harmful health practices. Ensuring the accuracy of health-related information on social media is an ongoing concern. Health misinformation can be easily spread through social media to large amounts of individuals which can make this dangerous. Often, critics will question whether health-related information that is shared online is credible. Social media does not require the amount of regulation that could prevent false medical information from being disseminated online. According to The Influencer Effect: Exploring the persuasive communication tactics of social media influencers in the health and wellness industry by Deborah Deutsch, "the information shared is often lacking accepted scientific evidence or is contrary to industry standards, and, at times, deceptive, unethical, and misleading." One example of this was in 2020, when President Donald Trump said in speeches and on Twitter that hydroxychloroquine and chloroquine could be used to treat COVID-19. While these drugs are antimalaria, it was being spread that they could be used for COVID-19. This resulted in increased deaths and individuals falling ill from taking this drug and the misinformation that was spread about this drug. Spreading misinformation regarding health is one of the biggest concerns when using social media for health awareness. When spreading misinformation about health there is an increase in confusion about what is true and what is false regardless of who is saying this information. Along with the confusion of the public, there is a sense of mistrust that is a consequence of misinformation. Individuals are seeing different opinions which leads people to a situation where they do not know who to trust. While health misinformation is one of the largest issues, there are ways to help prevent it. As individuals, it is important to know where you are getting your information from and learn how to identify what is misinformation and avoid the spread of it. == Privacy and ethical issues == The sharing of personal health information on social media raises privacy and ethical concerns. Striking a balance between raising awareness and respecting individuals' privacy remains a delicate issue.

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  LakeFS

  lakeFS is an open-source data version control system for managing data stored in object storage. It provides Git-like operations such as branching, committing, merging, and reverting for large-scale data stored in systems including Amazon S3, Azure Blob Storage, and Google Cloud Storage, as well as other S3-compatible object storage platforms. lakeFS is used in data engineering and machine learning workflows to manage changes to data, support reproducibility, and enable data governance across data lakes. The software is available as an open-source project, as well as in enterprise and managed service offerings, including lakeFS Cloud. == History == lakeFS was created in 2020 by Einat Orr and Oz Katz at Treeverse. Its first public release, version 0.8.1, appeared in August 2020 and introduced Git-style operations with support for Amazon S3. In 2021, Treeverse raised $23 million in a Series A funding round led by Dell Technologies Capital, Norwest Venture Partners, and Zeev Ventures. The same year, lakeFS was included in InfoWorld’s Best of Open Source Software (Bossie) awards. In June 2022, Treeverse introduced lakeFS Cloud, a managed service providing hosted lakeFS deployments for cloud-based data lakes. Version 1.0 was released in October 2023, adding integrations with platforms such as Databricks and Apache Iceberg, as well as support for orchestration tools including Apache Airflow. Public case studies and conference materials have described usage of lakeFS by organizations such as Microsoft, Volvo, and NASA. In July 2025, Treeverse announced an additional $20 million in growth funding to support further development of lakeFS. In November 2025, Treeverse announced the acquisition of the open-source data version control project DVC. == Software == === Overview === lakeFS provides Git-like operations such as branching, committing, merging, and reverting for datasets stored in object storage. These operations are used to manage changes to data, test modifications in isolation, reproduce specific data states, and recover from errors or unintended updates. === Architecture === lakeFS operates as a metadata layer on top of object storage systems such as Amazon S3, Azure Blob Storage, and Google Cloud Storage. It stores repository metadata describing commits, branches, and tags, enabling versioned views of data without copying underlying objects. The system provides access through multiple interfaces, including a web user interface, command-line tools, a REST API, and software development kits. It is designed to integrate with existing data engineering and machine learning workflows, and can be deployed either in self-hosted environments or as a managed service. === Functions === lakeFS provides version control functionality for data stored in object storage–based data lakes. Core features include: Atomic commits and version tracking for datasets, supporting reproducibility and auditability. Branching and merging mechanisms that allow isolated development and testing without duplicating data. Configurable hooks that can validate data or trigger external processes during commit and merge operations. The ability to revert repositories to earlier states to recover from data errors or failed changes. Recording of commit history and associated metadata for lineage tracking. Support for managing data across multiple object storage systems, including Amazon S3, Azure Blob Storage, Google Cloud Storage, and MinIO. Use of fixed data versions to reproduce experiments and machine learning model training. === Integrations === Coverage of lakeFS has described integrations with platforms such as Databricks and Apache Iceberg, as well as support for environments including Red Hat OpenShift. Additional materials describe its use with Trino, including validation of data changes prior to merging in versioned data workflows, as well as compatibility with orchestration tools such as Apache Airflow.

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  Backup

  In information technology, a backup, or data backup is a copy of computer data taken and stored elsewhere so that it may be used to restore the original after a data loss event. The verb form, referring to the process of doing so, is "back up", whereas the noun and adjective form is "backup". Backups can be used to recover data after its loss from data deletion or corruption, or to recover data from an earlier time. Backups provide a simple form of IT disaster recovery; however not all backup systems are able to reconstitute a computer system or other complex configuration such as a computer cluster, active directory server, or database server. A backup system contains at least one copy of all data considered worth saving. The data storage requirements can be large. An information repository model may be used to provide structure to this storage. There are different types of data storage devices used for copying backups of data that is already in secondary storage onto archive files. There are also different ways these devices can be arranged to provide geographic dispersion, data security, and portability. Data is selected, extracted, and manipulated for storage. The process can include methods for dealing with live data, including open files, as well as compression, encryption, and de-duplication. Additional techniques apply to enterprise client-server backup. Backup schemes may include dry runs that validate the reliability of the data being backed up. There are limitations and human factors involved in any backup scheme. == Storage == A backup strategy requires an information repository, "a secondary storage space for data" that aggregates backups of data "sources". The repository could be as simple as a list of all backup media (DVDs, etc.) and the dates produced, or could include a computerized index, catalog, or relational database. === 3-2-1 Backup Rule === The backup data needs to be stored, requiring a backup rotation scheme, which is a system of backing up data to computer media that limits the number of backups of different dates retained separately, by appropriate re-use of the data storage media by overwriting of backups no longer needed. The scheme determines how and when each piece of removable storage is used for a backup operation and how long it is retained once it has backup data stored on it. The 3-2-1 rule can aid in the backup process. It states that there should be at least 3 copies of the data, stored on 2 different types of storage media, and one copy should be kept offsite, in a remote location (this can include cloud storage). 2 or more different media should be used to eliminate data loss due to similar reasons (for example, optical discs may tolerate being underwater while LTO tapes may not, and SSDs cannot fail due to head crashes or damaged spindle motors since they do not have any moving parts, unlike hard drives). An offsite copy protects against fire, theft of physical media (such as tapes or discs) and natural disasters like floods and earthquakes. Physically protected hard drives are an alternative to an offsite copy, but they have limitations like only being able to resist fire for a limited period of time, so an offsite copy still remains as the ideal choice. Because there is no perfect storage, many backup experts recommend maintaining a second copy on a local physical device, even if the data is also backed up offsite. === Backup methods === ==== Unstructured ==== An unstructured repository may simply be a stack of tapes, DVD-Rs or external HDDs with minimal information about what was backed up and when. This method is the easiest to implement, but unlikely to achieve a high level of recoverability as it lacks automation. ==== Full only/System imaging ==== A repository using this backup method contains complete source data copies taken at one or more specific points in time. Copying system images, this method is frequently used by computer technicians to record known good configurations. However, imaging is generally more useful as a way of deploying a standard configuration to many systems rather than as a tool for making ongoing backups of diverse systems. ==== Incremental ==== An incremental backup stores data changed since a reference point in time. Duplicate copies of unchanged data are not copied. Typically a full backup of all files is made once or at infrequent intervals, serving as the reference point for an incremental repository. Subsequently, a number of incremental backups are made after successive time periods. Restores begin with the last full backup and then apply the incrementals. Some backup systems can create a synthetic full backup from a series of incrementals, thus providing the equivalent of frequently doing a full backup. When done to modify a single archive file, this speeds restores of recent versions of files. ==== Near-CDP ==== Continuous Data Protection (CDP) refers to a backup that instantly saves a copy of every change made to the data. This allows restoration of data to any point in time and is the most comprehensive and advanced data protection. Near-CDP backup applications—often marketed as "CDP"—automatically take incremental backups at a specific interval, for example every 15 minutes, one hour, or 24 hours. They can therefore only allow restores to an interval boundary. Near-CDP backup applications use journaling and are typically based on periodic "snapshots", read-only copies of the data frozen at a particular point in time. Near-CDP (except for Apple Time Machine) intent-logs every change on the host system, often by saving byte or block-level differences rather than file-level differences. This backup method differs from simple disk mirroring in that it enables a roll-back of the log and thus a restoration of old images of data. Intent-logging allows precautions for the consistency of live data, protecting self-consistent files but requiring applications "be quiesced and made ready for backup." Near-CDP is more practicable for ordinary personal backup applications, as opposed to true CDP, which must be run in conjunction with a virtual machine or equivalent and is therefore generally used in enterprise client-server backups. Software may create copies of individual files such as written documents, multimedia projects, or user preferences, to prevent failed write events caused by power outages, operating system crashes, or exhausted disk space, from causing data loss. A common implementation is an appended ".bak" extension to the file name. ==== Reverse incremental ==== A Reverse incremental backup method stores a recent archive file "mirror" of the source data and a series of differences between the "mirror" in its current state and its previous states. A reverse incremental backup method starts with a non-image full backup. After the full backup is performed, the system periodically synchronizes the full backup with the live copy, while storing the data necessary to reconstruct older versions. This can either be done using hard links—as Apple Time Machine does, or using binary diffs. ==== Differential ==== A differential backup saves only the data that has changed since the last full backup. This means a maximum of two backups from the repository are used to restore the data. However, as time from the last full backup (and thus the accumulated changes in data) increases, so does the time to perform the differential backup. Restoring an entire system requires starting from the most recent full backup and then applying just the last differential backup. A differential backup copies files that have been created or changed since the last full backup, regardless of whether any other differential backups have been made since, whereas an incremental backup copies files that have been created or changed since the most recent backup of any type (full or incremental). Changes in files may be detected through a more recent date/time of last modification file attribute, and/or changes in file size. Other variations of incremental backup include multi-level incrementals and block-level incrementals that compare parts of files instead of just entire files. === Storage media === Regardless of the repository model that is used, the data has to be copied onto an archive file data storage medium. The medium used is also referred to as the type of backup destination. ==== Magnetic tape ==== Magnetic tape was for a long time the most commonly used medium for bulk data storage, backup, archiving, and interchange. It was previously a less expensive option, but this is no longer the case for smaller amounts of data. Tape is a sequential access medium, so the rate of continuously writing or reading data can be very fast. While tape media itself has a low cost per space, tape drives are typically dozens of times as expensive as hard disk drives and optical drives. Tape media are generally rotated on a schedule so at least one set is off-site in case something should happe

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  Shorty Awards

  The Shorty Awards (also known as "The Shortys") are awards for outstanding and innovative work in digital and social media content by brands, advertising agencies, and creators. The awards, which generally focus on short-term content, honor achievements in content creation on Twitter, Facebook, YouTube, Instagram, TikTok, Twitch, and other social networking sites. The Shorty Awards began in 2008 and initially recognized achievements by independent creators on Twitter, with the first formal awards ceremony occurring in February 2009. Since then, the awards, which are now awarded each spring, have shifted their focus to recognize content across numerous platforms. Entrant work is judged on the merits of excellence in creativity, strategy, and engagement by the Real Time Academy, a group of industry professionals selected by the Shorty Awards on the basis of their professional reputations, industry knowledge, and personal achievements (which may include previous Shorty wins). An additional public voting component, known as Audience Honor Voting, is also used to select Shorty Awards contenders. Notable Shorty Award winners include Malala Yousafzai, Trevor Noah, Michelle Obama, Conan O’Brien, Lady Gaga, Bill Nye, Jacob Reed, and Lizzo. Brands and organizations such as Chipotle, Duolingo, Marvel Studios, HBO, Red Bull, Airbnb, Nestle, BMW, UNICEF and the Human Rights Campaign have also been awarded. The Shorty Awards also produces an annual award program called The Shorty Impact Awards, a competition dedicated to showcasing digital and social media-based projects by brands, agencies, and organizations that seek to make the world a better place. == List of ceremonies == == 1st Shorty Awards == The awards were created in 2008 by tech entrepreneurs Greg Galant, Adam Varga, and Lee Semel of Sawhorse Media. They invited Twitter account holders to nominate the best Twitter users in general categories such as humor, news, food, and design. Winners were chosen by more than 30,000 Twitter users during the voting period. The founders of Twitter first heard about the awards after the contest had gotten underway and expressed support for it. The first Shorty Awards ceremony was held on February 11, 2009, at the Galapagos Art Space in Brooklyn, New York. Approximately 300 people attended the event. The event was hosted by CNN anchor Rick Sanchez and featured appearances by prominent Twitter users MC Hammer and Gary Vaynerchuk and a video appearance by Shaquille O'Neal. The awards, in 26 categories, were voted on by Twitter users. == 2nd Shorty Awards == Voting for the second Shorty Awards opened in January 2010 in 26 official categories. A Real-Time Photo of the Year category was added to the list of official categories for the first time, recognizing the best photo posted to services such as Twitpic, Yfrog, or Facebook. The second Shorty Awards competition introduced a panel of judges called the Real-Time Academy of Short Form Arts & Sciences whose members were Craig Newmark, David Pogue, Kurt Andersen, Caterina Fake, Joi Ito, Frank Moss, Alberto Ibargüen, Sreenath Sreenivasan, MC Hammer, Alyssa Milano and Jimmy Wales. After public nominations determined the finalists, the academy decided on the winners. Winners were announced at a ceremony held in the Times Center in The New York Times building in Manhattan that was also streamed online. The ceremony was hosted by CNN anchor Rick Sanchez, who presented awards in the official categories as well as the newly added Real-Time Photo of the Year and a special humanitarian award. == 3rd Shorty Awards == The nomination period for the third annual Shorty Awards opened in January 2011 and ran through February 11, 2011, except for new categories that had extended nomination deadlines. There were 30 official categories and five special categories. In addition to Real-Time Photo of the Year, for the first time the awards accepted nominations for Foursquare Mayor of the Year, Foursquare Location of the Year, Microblog of the Year on Tumblr, and a Connecting People award. The awards also introduced new Shorty Industry Awards to recognize the best uses of social media by brands and agencies. Winners were announced at a ceremony on March 28, 2011, hosted by Aasif Mandvi in the Times Center. Other Shorty Awards presenters were scheduled to include Kiefer Sutherland, Jerry Stiller, Anne Meara, Stephen Wallem, Miss USA Rima Fakih, and Miss Teen USA Kamie Crawford. == 4th Shorty Awards == The 4th Annual Shorty Awards featured Ricky Gervais and Tiffani Thiessen. 1.6 million tweeted nominations were made across all the categories to honor the top users on Twitter, Facebook, Tumblr, Foursquare, YouTube and other internet platforms. == 5th Shorty Awards == The 5th Annual Shorty Awards ceremony featured Felicia Day, James Urbaniak, Kristian Nairn, Hannibal Buress, Carrie Keagan, Chris Hardwick, David Karp and Coco Rocha. 2.4 million tweeted nominations were made across all the categories to honor the top users on Twitter, Facebook, Tumblr, Foursquare, YouTube and other internet sites. == 6th Shorty Awards == The ceremony took place on April 7, 2014, at the New York TimesCenter and was hosted by Comedian Natasha Leggero. The show included appearances by Patton Oswalt, Jamie Oliver, Kristen Bell, Jerry Seinfeld, Moshe Kasher, Julie Klausner, Erin Brady, Guy Kawasaki, Matt Walsh, Retta, Us the Duo, Big Boi, Gilbert Gottfried, Thomas Middleditch, Billie Jean King and Leandra Medine. Winners included Jerry Seinfeld and Will Ferrell. == 7th Shorty Awards == The Seventh Annual Shorty Awards was hosted by comedian Rachel Dratch and took place on April 20, 2015, at The Times Center in NYC. The Real-Time Academy, the judging body of the Shortys, tripled in size for the 7th annual Awards and included Alton Brown, Mamrie Hart, Nikki Glaser, OK Go, The Fine Bros, Debbie Sterling, Dan Savage, Deena Varshavskaya and Palmer Luckey. Panic! at the Disco was the musical guest at the ceremony. On-stage presenters included Kevin Jonas, Bill Nye, Bella Thorne, Wyclef Jean, Emily Kinney and Tyler Oakley. == 8th Shorty Awards == The Eighth Annual Shorty Awards were held in NYC at the TimesCenter on April 11, 2016. They were hosted by YouTuber, Writer and Comedian Mamrie Hart with musical performances from Nico & Vinz. Winners of the night included Bill Wurtz, DJ Khaled, Misty Copeland, Casey Neistat, Dwayne Johnson, Hannah Hart, Troye Sivan, Baddie Winkle, Kevin Hart, Taraji P. Henson, King Bach, and Zach King. == 9th Shorty Awards == The Ninth Annual Shorty Awards were held in NYC at the PlayStation Theater on April 23, 2017. They were hosted by two-time Emmy Award winner Tony Hale with a musical performance by Lizzo. Winners of the night included Bill Nye, Shay Mitchell, Doug the Pug, Gigi Gorgeous, Simone Biles, Mara Wilson, Gaten Matarazzo and Chrissy Teigen. == 10th Shorty Awards == The 10th Annual Shorty Awards, took place on April 15, 2018, at the PlayStation Theater, New York City. The ceremony was hosted by actress, singer, and songwriter Keke Palmer with a musical performance by Betty Who. == 11th Shorty Awards == The 11th Annual Shorty Awards were held on May 5, 2019, in New York City at the PlayStation Theater. The ceremony was hosted by American actress and comedian Kathy Griffin, with a musical performance by Tank and the Bangas. == 12th Shorty Awards == The 12th Annual Shorty Awards were held on May 3, 2020. Due to the COVID-19 pandemic, the ceremony took place online for the first time, with presenters and award winners filming from their own homes. The ceremony was hosted by actor J.B. Smoove and featured a remixed performance of Trap Queen by Fetty Wap. Award winners included Jack Stauber, Supercar Blondie, Rose and Rosie, and Greta Thunberg. == 13th Shorty Awards == The 13th Annual Shorty Awards took place from April 26 to May 14, 2021. The ceremony was hosted on different social media platforms, such as Instagram and Clubhouse, to create a more tailored experience. Winners were announced from May 11 to May 14, with 10 winners being revealed each hour from 1 to 4 p.m. EST on the Shorty Awards Instagram account. == 14th Shorty Awards == The 14th Annual Shorty Awards were held virtually on May 15, 2022, honoring the best in social media and digital content. Hosted by Jay Shetty, the event recognized influencers, brands, and organizations across various categories, celebrating excellence in digital storytelling and innovative online campaigns. Notable winners included Tabitha Brown for her food content and the D'Amelio Family for their contributions to family and parenting content. The event highlighted the role of digital media in connecting and inspiring audiences during challenging times. == 15th Shorty Awards == The 15th Annual Shorty Awards celebrated the best in social media and digital content on May 24, 2023, at Tribeca 360° in New York City. Hosted by Jay Pharoah, the event honored creators, brands, and organizations ac

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