Eat App is a global restaurant technology company that provides a cloud-based management platform for restaurants, hotels, and other venues. The platform enables venues to accept online reservations seamlessly, manage tables, and enhance customer relationship management (CRM). It utilizes AI to improve operational efficiency, provides marketing automation, and helps build a comprehensive guestbook. The company also offers a consumer app and website for discovering and booking restaurant tables online. According to the company, the system has seated over 100 million guests, and the number continues to grow. Eat was founded by Nezar Kadhem and David Feuillard in 2015 and has raised $13M to date from Silicon Valley's 500 startups, Middle East Venture Partners (MEVP), Derayah VC, amongst other business angels. The company is currently operational across the world, with offices in Dubai and the United States. == Product overview == === For restaurants === Eat App’s reservation system allows for a digital record of all reservations, all guests that have previously visited the restaurant, as well as analytics on the performance of the restaurant. The table management feature simplifies traditional restaurant operations by providing a live snapshot of current status, seating optimization, and shift management. The CRM and analytics suite gathers and monitors data to build a segmented guestbook for personalized marketing and provides dashboards for data-driven decision-making. Additionally, the review feature makes it easy for restaurants to automatically collect reviews from their guests. Additionally, Eat App includes a chit printer function that seamlessly prints reservation details at host stands and a review management feature that allows restaurants to manage online reviews directly within the platform. == History == In February 2015, Eat App raised $300k from Bahrain-based business angel group TENMOU. In June 2018, Eat raised $1.2 million from Dubai-based Middle East Venture Partners (MEVP). In February 2020, Eat App raised $5 million in a Series B funding round led by 500 Startups, Derayah Venture Fund, and MEVP, with participation from a few angel investors and family members. In February 2021, Eat App launched its technology with The Emaar Hospitality Group, implementing it across over 50 restaurants in Emaar properties and hotels. The cloud-based system runs natively on iPads in each restaurant, providing Emaar staff access to reservations and guest information, and integrates with the U by Emaar loyalty app to personalize service. On September 28, 2022, Eat App announced the closing of an $11 million Series B funding round. The investment was led by Middle East Venture Partners (MEVP), 500 Startups, Derayah Venture Capital, Dallah Albaraka, Ali Zaid Al Quraishi & Brothers Company, and Rasameel Investment Company, with participation from existing investors.
Patch management
Patch management (or patch management policy or patch policy or patch management process) is concerned with the identification, acquisition, distribution, testing and installation of patches to systems. Proper patch management can be a net productivity boost for an organization. Patches can be used to defend against and eliminate potential vulnerabilities of a system, so that no threats may exploit them. Problems can arise during patch management, including buggy patches that either fail to fix their problem or introduce new issues. Patch management tools help orchestrate all of the procedures involved in patch management. == Description == Patch management is defined as a sub-practice of various disciplines including vulnerability management (part of security management), lifecycle management (with further possible sub-classification into application lifecycle management and release management), change management, and systems management. The practice is broadly concerned with the identification, acquisition, distribution, and installation of patches to systems. Some definitions of patch management are as a software-level practice, while others are as a systems-level process: software, drivers, and firmware. == Cost–benefit analysis == While reserving time for patching takes up enterprise resources, there are balancing factors which can make proper patch management into a net productivity boost for an organization. Up-to-date systems often perform more efficiently, less costly, with less errors, less security risks, and better user workflow. Additionally, compliance with changing local and federal regulations are more likely to be satisfied. Patching security vulnerabilities has been one among many competing priorities for organizations, leading to longer periods before patching for some organizations. Equifax was too slow to implement its 2015 patch management plan to be able to mitigate or prevent the 2017 Equifax data breach, leading to scrutiny from regulators. == Relation to security management == Patches can be used to defend against and eliminate potential vulnerabilities of a system, so that no threats may exploit them; therefore, patch management can be considered a sub-discipline of vulnerability management. Every patchable device in a system presents an attack surface that must be secured. === Time plan === Automatic updates are where the patch is applied automatically with little to know actions or planning required. This approach is recommended for many individuals and organizations. Some organizations also have to prioritize which patches to prioritize given limited resources. Patch Tuesday is the most common process when major companies like Microsoft and Adobe release patches on a known date so that companies can plan resources around implementing the patches more quickly. Linux is open-sourced and patches can be released at any time, leading some to rely on mailing lists or other ways to be alerted to updates. === Inventory === Taking an inventory of software and hardware, including versions can make it easier to correlate with bugs or patches as they become known. Taking stock of how much education and support others in an organization need to install their patches can also help for planning how to implement the patch or design systems to begin with. Streamlining the process by using tools that can communicate with each other can also help to reduce the time of exposure to known vulnerabilities. == Challenges == There are a multitude of problems that can arise during patch management. A common issue is buggy patches, which either fail to fix their problem or introduce new issues. Another issue is deployment synchronization, since various subsystems may receive instructions to update at different times. Similarly, the difficulty of patch management across many devices may grow at an uncontrollable rate depending on organizational size. One prominent demonstration of the challenges facing proper patch management was the buggy Falcon Sensor patch by CrowdStrike which caused one of the worst IT outages of all time. == Implementations == A patch management tool (alternatively patch manager, patch management system, patch management software, or centralized patch management) help orchestrate all of the procedures involved in patch management. Tools can be in-house (applied locally by local administrators), or external, as with managed service providers (applied externally by a provider). === Patch management software === Windows Update for Business, System Center Configuration Manager, and Windows Server Update Services offer control over patch deployment, with features enabling testing, scheduling updates, and setting custom configurations on Windows platforms. === Managed service providers === == Regulatory requirements (United States) == Timely patching of software vulnerabilities is a requirement under multiple regulatory frameworks in the United States. The Health Insurance Portability and Accountability Act (HIPAA) Security Rule requires covered entities to protect electronic protected health information by implementing security measures sufficient to reduce risks to a reasonable and appropriate level, which industry guidance has long interpreted to include timely patch management. A proposed new HIPAA Security Rule would make patch management requirements explicit, mandating that covered entities and business associates deploy security patches and updates within a defined risk-based timeline and maintain written procedures for prioritizing, testing, and applying patches to systems that store, process, or transmit ePHI. The 2025 proposal continues to receive industry pushback as of December 2025. HIPAA was last updated in 2013. The Payment Card Industry Data Security Standard (PCI DSS) requires organizations to protect system components from known vulnerabilities by installing applicable security patches within one month of release for critical patches. The Cybersecurity and Infrastructure Security Agency (CISA) maintains a Known Exploited Vulnerabilities (KEV) catalog that compels U.S. federal agencies to remediate listed vulnerabilities within specified timelines. Agencies are typically required to patch within 3 weeks, though some vulnerabilities must be fixed within 24 hours.
Ciphertext
In cryptography, ciphertext or cyphertext is the result of encryption performed on plaintext using an algorithm, called a cipher. Ciphertext is also known as encrypted or encoded information because it contains a form of the original plaintext that is unreadable by a human or computer without the proper cipher to decrypt it. This process prevents the loss of sensitive information via hacking. Decryption, the inverse of encryption, is the process of turning ciphertext into readable plaintext. Ciphertext is not to be confused with codetext, because the latter is a result of a code, not a cipher. == Conceptual underpinnings == Let m {\displaystyle m\!} be the plaintext message that Alice wants to secretly transmit to Bob and let E k {\displaystyle E_{k}\!} be the encryption cipher, where k {\displaystyle _{k}\!} is a cryptographic key. Alice must first transform the plaintext into ciphertext, c {\displaystyle c\!} , in order to securely send the message to Bob, as follows: c = E k ( m ) . {\displaystyle c=E_{k}(m).\!} In a symmetric-key system, Bob knows Alice's encryption key. Once the message is encrypted, Alice can safely transmit it to Bob (assuming no one else knows the key). In order to read Alice's message, Bob must decrypt the ciphertext using E k − 1 {\displaystyle {E_{k}}^{-1}\!} which is known as the decryption cipher, D k : {\displaystyle D_{k}:\!} D k ( c ) = D k ( E k ( m ) ) = m . {\displaystyle D_{k}(c)=D_{k}(E_{k}(m))=m.\!} Alternatively, in a non-symmetric key system, everyone, not just Alice and Bob, knows the encryption key; but the decryption key cannot be inferred from the encryption key. Only Bob knows the decryption key D k , {\displaystyle D_{k},} and decryption proceeds as D k ( c ) = m . {\displaystyle D_{k}(c)=m.} == Types of ciphers == The history of cryptography began thousands of years ago. Cryptography uses a variety of different types of encryption. Earlier algorithms were performed by hand and are substantially different from modern algorithms, which are generally executed by a machine. === Historical ciphers === Historical pen and paper ciphers used in the past are sometimes known as classical ciphers. They include: Substitution cipher: the units of plaintext are replaced with ciphertext (e.g., Caesar cipher and one-time pad) Polyalphabetic substitution cipher: a substitution cipher using multiple substitution alphabets (e.g., Vigenère cipher and Enigma machine) Polygraphic substitution cipher: the unit of substitution is a sequence of two or more letters rather than just one (e.g., Playfair cipher) Transposition cipher: the ciphertext is a permutation of the plaintext (e.g., rail fence cipher) Historical ciphers are not generally used as a standalone encryption technique because they are quite easy to crack. Many of the classical ciphers, with the exception of the one-time pad, can be cracked using brute force. === Modern ciphers === Modern ciphers are more secure than classical ciphers and are designed to withstand a wide range of attacks. An attacker should not be able to find the key used in a modern cipher, even if they know any specifics about the plaintext and its corresponding ciphertext. Modern encryption methods can be divided into the following categories: Private-key cryptography (symmetric key algorithm): one shared key is used for encryption and decryption Public-key cryptography (asymmetric key algorithm): two different keys are used for encryption and decryption In a symmetric key algorithm (e.g., DES, AES), the sender and receiver have a shared key established in advance: the sender uses the shared key to perform encryption; the receiver uses the shared key to perform decryption. Symmetric key algorithms can either be block ciphers or stream ciphers. Block ciphers operate on fixed-length groups of bits, called blocks, with an unvarying transformation. Stream ciphers encrypt plaintext digits one at a time on a continuous stream of data, with the transformation of successive digits varying during the encryption process. In an asymmetric key algorithm (e.g., RSA), there are two different keys: a public key and a private key. The public key is published, thereby allowing any sender to perform encryption. The private key is kept secret by the receiver, thereby allowing only the receiver to correctly perform decryption. == Cryptanalysis == Cryptanalysis (also referred to as codebreaking or cracking the code) is the study of applying various methodologies to obtain the meaning of encrypted information, without having access to the cipher required to correctly decrypt the information. This typically involves gaining an understanding of the system design and determining the cipher. Cryptanalysts can follow one or more attack models to crack a cipher, depending upon what information is available and the type of cipher being analyzed. Ciphertext is generally the most easily obtained part of a cryptosystem and therefore is an important part of cryptanalysis. === Attack models === Ciphertext-only: the cryptanalyst has access only to a collection of ciphertexts or code texts. This is the weakest attack model because the cryptanalyst has limited information. Modern ciphers rarely fail under this attack. Known-plaintext: the attacker has a set of ciphertexts to which they know the corresponding plaintext Chosen-plaintext attack: the attacker can obtain the ciphertexts corresponding to an arbitrary set of plaintexts of their own choosing Batch chosen-plaintext attack: where the cryptanalyst chooses all plaintexts before any of them are encrypted. This is often the meaning of an unqualified use of "chosen-plaintext attack". Adaptive chosen-plaintext attack: where the cryptanalyst makes a series of interactive queries, choosing subsequent plaintexts based on the information from the previous encryptions. Chosen-ciphertext attack: the attacker can obtain the plaintexts corresponding to an arbitrary set of ciphertexts of their own choosing Adaptive chosen-ciphertext attack Indifferent chosen-ciphertext attack Related-key attack: similar to a chosen-plaintext attack, except the attacker can obtain ciphertexts encrypted under two different keys. The keys are unknown, but the relationship between them is known (e.g., two keys that differ in the one bit). == Famous ciphertexts == The Babington Plot ciphers The Shugborough inscription The Zimmermann Telegram The Magic Words are Squeamish Ossifrage The cryptogram in "The Gold-Bug" Beale ciphers Kryptos Zodiac Killer ciphers
Hybrid argument (cryptography)
In cryptography, the hybrid argument is a proof technique used to show that two distributions are computationally indistinguishable. == History == Hybrid arguments had their origin in a papers by Andrew Yao in 1982 and Shafi Goldwasser and Silvio Micali in 1983. == Formal description == Formally, to show two distributions D1 and D2 are computationally indistinguishable, we can define a sequence of hybrid distributions D1 := H0, H1, ..., Ht =: D2 where t is polynomial in the security parameter n. Define the advantage of any probabilistic efficient (polynomial-bounded time) algorithm A as A d v H i , H i + 1 d i s t ( A ) := | Pr [ x ← $ H i : A ( x ) = 1 ] − Pr [ x ← $ H i + 1 : A ( x ) = 1 ] | , {\displaystyle {\mathsf {Adv}}_{H_{i},H_{i+1}}^{\mathsf {dist}}(\mathbf {A} ):=\left|\Pr[x{\stackrel {\$}{\gets }}H_{i}:\mathbf {A} (x)=1]-\Pr[x{\stackrel {\$}{\gets }}H_{i+1}:\mathbf {A} (x)=1]\right|,} where the dollar symbol ($) denotes that we sample an element from the distribution at random. By triangle inequality, it is clear that for any probabilistic polynomial time algorithm A, A d v D 1 , D 2 d i s t ( A ) ≤ ∑ i = 0 t − 1 A d v H i , H i + 1 d i s t ( A ) . {\displaystyle {\mathsf {Adv}}_{D_{1},D_{2}}^{\mathsf {dist}}(\mathbf {A} )\leq \sum _{i=0}^{t-1}{\mathsf {Adv}}_{H_{i},H_{i+1}}^{\mathsf {dist}}(\mathbf {A} ).} Thus there must exist some k s.t. 0 ≤ k < t(n) and A d v H k , H k + 1 d i s t ( A ) ≥ A d v D 1 , D 2 d i s t ( A ) / t ( n ) . {\displaystyle {\mathsf {Adv}}_{H_{k},H_{k+1}}^{\mathsf {dist}}(\mathbf {A} )\geq {\mathsf {Adv}}_{D_{1},D_{2}}^{\mathsf {dist}}(\mathbf {A} )/t(n).} Since t is polynomial-bounded, for any such algorithm A, if we can show that it has a fixed negligible advantage function ε(n) between distributions Hi and Hi+1 for every i, so in particular, ϵ ( n ) ≥ A d v H k , H k + 1 d i s t ( A ) ≥ A d v D 1 , D 2 d i s t ( A ) / t ( n ) , {\displaystyle \epsilon (n)\geq {\mathsf {Adv}}_{H_{k},H_{k+1}}^{\mathsf {dist}}(\mathbf {A} )\geq {\mathsf {Adv}}_{D_{1},D_{2}}^{\mathsf {dist}}(\mathbf {A} )/t(n),} then it immediately follows that its advantage to distinguish the distributions D1 = H0 and D2 = Ht must also be negligible. == Applications == The hybrid argument is extensively used in cryptography. Some simple proofs using hybrid arguments are: If one cannot efficiently predict the next bit of the output of some number generator, then this generator is a pseudorandom number generator (PRG). We can securely expand a PRG with 1-bit output into a PRG with n-bit output.
Star Awards for Social Media Award
The Star Awards for Social Media Award was an award presented annually from 2014 to 2016 at the Star Awards, where Mediacorp of Singapore recognises entertainers under their employment with awards for artistic and technical merit for outstanding performances of the year. == History == The category was introduced in 2014, at the 20th Star Awards ceremony; Jeanette Aw received the award and it is given in honour of a Mediacorp artiste with the most social media engagement. The results are based on the calculations from three international social media analysis systems; artistes must be active on at least one of the following platforms in order to qualify: Facebook, Twitter and Instagram. Since its inception, the award has been given to two artistes. Carrie Wong is the most recent and final winner in this category. Since the ceremony held in 2016, Aw remains as the only artiste to win in this category twice, surpassing Wong who has one win. The award was discontinued from 2017 onwards as the popularity element of the award is already represented in the Top 10 Most Popular Male Artistes and Top 10 Most Popular Female Artistes awards. == Recipients ==
VLLM
vLLM is an open-source software framework for inference and serving of large language models and related multimodal models. Originally developed at the University of California, Berkeley's Sky Computing Lab, the project is centered on PagedAttention, a memory-management method for transformer key–value caches, and supports features such as continuous batching, distributed inference, quantization, and OpenAI-compatible APIs. According to a project maintainer, the "v" in vLLM originally referred to "virtual", inspired by virtual memory. == History == vLLM was introduced in 2023 by researchers affiliated with the Sky Computing Lab at UC Berkeley. Its core ideas were described in the 2023 paper Efficient Memory Management for Large Language Model Serving with PagedAttention, which presented the system as a high-throughput and memory-efficient serving engine for large language models. In 2025, the PyTorch Foundation announced that vLLM had become a Foundation-hosted project. PyTorch's project page states that the University of California, Berkeley contributed vLLM to the Linux Foundation in July 2024. In January 2026, TechCrunch reported that the creators of vLLM had launched the startup Inferact to commercialize the project, raising $150 million in seed funding. == Architecture == According to its 2023 paper, vLLM was designed to improve the efficiency of large language model serving by reducing memory waste in the key–value cache used during transformer inference. The paper introduced PagedAttention, an algorithm inspired by virtual memory and paging techniques in operating systems, and described vLLM as using block-level memory management and request scheduling to increase throughput while maintaining similar latency. The project documentation and repository describe support for continuous batching, chunked prefill, speculative decoding, prefix caching, quantization, and multiple forms of distributed inference and serving. PyTorch has described vLLM as a high-throughput, memory-efficient inference and serving engine that supports a range of hardware back ends, including NVIDIA and AMD GPUs, Google TPUs, AWS Trainium, and Intel processors.
Visual cryptography
Visual cryptography is a cryptographic technique which allows visual information (pictures, text, etc.) to be encrypted in such a way that the decrypted information appears as a visual image. One of the best-known techniques has been credited to Moni Naor and Adi Shamir, who developed it in 1994. They demonstrated a visual secret sharing scheme, where a binary image was broken up into n shares so that only someone with all n shares could decrypt the image, while any n − 1 shares revealed no information about the original image. Each share was printed on a separate transparency, and decryption was performed by overlaying the shares. When all n shares were overlaid, the original image would appear. There are several generalizations of the basic scheme including k-out-of-n visual cryptography, and using opaque sheets but illuminating them by multiple sets of identical illumination patterns under the recording of only one single-pixel detector, which exposed the image. Using a similar idea, transparencies can be used to implement a one-time pad encryption, where one transparency is a shared random pad, and another transparency acts as the ciphertext. Normally, there is an expansion of space requirement in visual cryptography. But if one of the two shares is structured recursively, the efficiency of visual cryptography can be increased to 100%. Some antecedents of visual cryptography are in patents from the 1960s. Other antecedents are in the work on perception and secure communication. Visual cryptography can be used to protect biometric templates in which decryption does not require any complex computations. == Example == In this example, the binary image has been split into two component images. Each component image has a pair of pixels for every pixel in the original image. These pixel pairs are shaded black or white according to the following rule: if the original image pixel was black, the pixel pairs in the component images must be complementary; randomly shade one ■□, and the other □■. When these complementary pairs are overlapped, they will appear dark gray. On the other hand, if the original image pixel was white, the pixel pairs in the component images must match: both ■□ or both □■. When these matching pairs are overlapped, they will appear light gray. So, when the two component images are superimposed, the original image appears. However, without the other component, a component image reveals no information about the original image; it is indistinguishable from a random pattern of ■□ / □■ pairs. Moreover, if you have one component image, you can use the shading rules above to produce a counterfeit component image that combines with it to produce any image at all. == (2, n) visual cryptography sharing case == Sharing a secret with an arbitrary number of people, n, such that at least 2 of them are required to decode the secret is one form of the visual secret sharing scheme presented by Moni Naor and Adi Shamir in 1994. In this scheme we have a secret image which is encoded into n shares printed on transparencies. The shares appear random and contain no decipherable information about the underlying secret image, however if any 2 of the shares are stacked on top of one another the secret image becomes decipherable by the human eye. Every pixel from the secret image is encoded into multiple subpixels in each share image using a matrix to determine the color of the pixels. In the (2, n) case, a white pixel in the secret image is encoded using a matrix from the following set, where each row gives the subpixel pattern for one of the components: {all permutations of the columns of} : C 0 = [ 1 0 . . . 0 1 0 . . . 0 . . . 1 0 . . . 0 ] . {\displaystyle \mathbf {C_{0}=} {\begin{bmatrix}1&0&...&0\\1&0&...&0\\...\\1&0&...&0\end{bmatrix}}.} While a black pixel in the secret image is encoded using a matrix from the following set: {all permutations of the columns of} : C 1 = [ 1 0 . . . 0 0 1 . . . 0 . . . 0 0 . . . 1 ] . {\displaystyle \mathbf {C_{1}=} {\begin{bmatrix}1&0&...&0\\0&1&...&0\\...\\0&0&...&1\end{bmatrix}}.} For instance in the (2,2) sharing case (the secret is split into 2 shares and both shares are required to decode the secret) we use complementary matrices to share a black pixel and identical matrices to share a white pixel. Stacking the shares we have all the subpixels associated with the black pixel now black while 50% of the subpixels associated with the white pixel remain white. == Cheating the (2, n) visual secret sharing scheme == Horng et al. proposed a method that allows n − 1 colluding parties to cheat an honest party in visual cryptography. They take advantage of knowing the underlying distribution of the pixels in the shares to create new shares that combine with existing shares to form a new secret message of the cheaters choosing. We know that 2 shares are enough to decode the secret image using the human visual system. But examining two shares also gives some information about the 3rd share. For instance, colluding participants may examine their shares to determine when they both have black pixels and use that information to determine that another participant will also have a black pixel in that location. Knowing where black pixels exist in another party's share allows them to create a new share that will combine with the predicted share to form a new secret message. In this way a set of colluding parties that have enough shares to access the secret code can cheat other honest parties. == Visual steganography == 2×2 subpixels can also encode a binary image in each component image. For example, each white pixel of each component image could be represented by two black subpixels, while each black pixel represented by three black subpixels. When overlaid, each white pixel of the secret image is represented by three black subpixels, while each black pixel is represented by all four subpixels black. Each corresponding pixel in the component images is randomly rotated to avoid orientation leaking information about the secret image. == In popular culture == In "Do Not Forsake Me Oh My Darling", a 1967 episode of TV series The Prisoner, the protagonist uses a visual cryptography overlay of multiple transparencies to reveal a secret message – the location of a scientist friend who had gone into hiding.