In cryptography, an initialization vector (IV) or starting variable is an input to a cryptographic primitive being used to provide the initial state. The IV is typically required to be random or pseudorandom, but sometimes an IV only needs to be unpredictable or unique. Randomization is crucial for some encryption schemes to achieve semantic security, a property whereby repeated usage of the scheme under the same key does not allow an attacker to infer relationships between (potentially similar) segments of the encrypted message. For block ciphers, the use of an IV is described by the modes of operation. Some cryptographic primitives require the IV only to be non-repeating, and the required randomness is derived internally. In this case, the IV is commonly called a nonce (a number used only once), and the primitives (e.g. CBC) are considered stateful rather than randomized. This is because an IV need not be explicitly forwarded to a recipient but may be derived from a common state updated at both sender and receiver side. (In practice, a short nonce is still transmitted along with the message to consider message loss.) An example of stateful encryption schemes is the counter mode of operation, which has a sequence number for a nonce. The IV size depends on the cryptographic primitive used; for block ciphers it is generally the cipher's block-size. In encryption schemes, the unpredictable part of the IV has at best the same size as the key to compensate for time/memory/data tradeoff attacks. When the IV is chosen at random, the probability of collisions due to the birthday problem must be taken into account. Traditional stream ciphers such as RC4 do not support an explicit IV as input, and a custom solution for incorporating an IV into the cipher's key or internal state is needed. Some designs realized in practice are known to be insecure; the WEP protocol is a notable example, and is prone to related-IV attacks. == Motivation == A block cipher is one of the most basic primitives in cryptography, and frequently used for data encryption. However, by itself, it can only be used to encode a data block of a predefined size, called the block size. For example, a single invocation of the AES algorithm transforms a 128-bit plaintext block into a ciphertext block of 128 bits in size. The key, which is given as one input to the cipher, defines the mapping between plaintext and ciphertext. If data of arbitrary length is to be encrypted, a simple strategy is to split the data into blocks each matching the cipher's block size, and encrypt each block separately using the same key. This method is not secure as equal plaintext blocks get transformed into equal ciphertexts, and a third party observing the encrypted data may easily determine its content even when not knowing the encryption key. To hide patterns in encrypted data while avoiding the re-issuing of a new key after each block cipher invocation, a method is needed to randomize the input data. In 1980, the NIST published a national standard document designated Federal Information Processing Standard (FIPS) PUB 81, which specified four so-called block cipher modes of operation, each describing a different solution for encrypting a set of input blocks. The first mode implements the simple strategy described above, and was specified as the electronic codebook (ECB) mode. In contrast, each of the other modes describe a process where ciphertext from one block encryption step gets intermixed with the data from the next encryption step. To initiate this process, an additional input value is required to be mixed with the first block, and which is referred to as an initialization vector. For example, the cipher-block chaining (CBC) mode requires an unpredictable value, of size equal to the cipher's block size, as additional input. This unpredictable value is added to the first plaintext block before subsequent encryption. In turn, the ciphertext produced in the first encryption step is added to the second plaintext block, and so on. The ultimate goal for encryption schemes is to provide semantic security: by this property, it is practically impossible for an attacker to draw any knowledge from observed ciphertext. It can be shown that each of the three additional modes specified by the NIST are semantically secure under so-called chosen-plaintext attacks. == Properties == Properties of an IV depend on the cryptographic scheme used. A basic requirement is uniqueness, which means that no IV may be reused under the same key. For block ciphers, repeated IV values devolve the encryption scheme into electronic codebook mode: equal IV and equal plaintext result in equal ciphertext. In stream cipher encryption uniqueness is crucially important as plaintext may be trivially recovered otherwise. Example: Stream ciphers encrypt plaintext P to ciphertext C by deriving a key stream K from a given key and IV and computing C as C = P xor K. Assume that an attacker has observed two messages C1 and C2 both encrypted with the same key and IV. Then knowledge of either P1 or P2 reveals the other plaintext since C1 xor C2 = (P1 xor K) xor (P2 xor K) = P1 xor P2. Many schemes require the IV to be unpredictable by an adversary. This is effected by selecting the IV at random or pseudo-randomly. In such schemes, the chance of a duplicate IV is negligible, but the effect of the birthday problem must be considered. As for the uniqueness requirement, a predictable IV may allow recovery of (partial) plaintext. Example: Consider a scenario where a legitimate party called Alice encrypts messages using the cipher-block chaining mode. Consider further that there is an adversary called Eve that can observe these encryptions and is able to forward plaintext messages to Alice for encryption (in other words, Eve is capable of a chosen-plaintext attack). Now assume that Alice has sent a message consisting of an initialization vector IV1 and starting with a ciphertext block CAlice. Let further PAlice denote the first plaintext block of Alice's message, let E denote encryption, and let PEve be Eve's guess for the first plaintext block. Now, if Eve can determine the initialization vector IV2 of the next message she will be able to test her guess by forwarding a plaintext message to Alice starting with (IV2 xor IV1 xor PEve); if her guess was correct this plaintext block will get encrypted to CAlice by Alice. This is because of the following simple observation: CAlice = E(IV1 xor PAlice) = E(IV2 xor (IV2 xor IV1 xor PAlice)). Depending on whether the IV for a cryptographic scheme must be random or only unique the scheme is either called randomized or stateful. While randomized schemes always require the IV chosen by a sender to be forwarded to receivers, stateful schemes allow sender and receiver to share a common IV state, which is updated in a predefined way at both sides. == Block ciphers == Block cipher processing of data is usually described as a mode of operation. Modes are primarily defined for encryption as well as authentication, though newer designs exist that combine both security solutions in so-called authenticated encryption modes. While encryption and authenticated encryption modes usually take an IV matching the cipher's block size, authentication modes are commonly realized as deterministic algorithms, and the IV is set to zero or some other fixed value. == Stream ciphers == In stream ciphers, IVs are loaded into the keyed internal secret state of the cipher, after which a number of cipher rounds are executed prior to releasing the first bit of output. For performance reasons, designers of stream ciphers try to keep that number of rounds as small as possible, but because determining the minimal secure number of rounds for stream ciphers is not a trivial task, and considering other issues such as entropy loss, unique to each cipher construction, related-IVs and other IV-related attacks are a known security issue for stream ciphers, which makes IV loading in stream ciphers a serious concern and a subject of ongoing research. == WEP IV == The 802.11 encryption algorithm called WEP (short for Wired Equivalent Privacy) used a short, 24-bit IV, leading to reused IVs with the same key, which led to it being easily cracked. Packet injection allowed for WEP to be cracked in times as short as several seconds. This ultimately led to the deprecation of WEP. == SSL 2.0 IV == In cipher-block chaining mode (CBC mode), the IV need not be secret, but must be unpredictable (In particular, for any given plaintext, it must not be possible to predict the IV that will be associated to the plaintext in advance of the generation of the IV.) at encryption time. Additionally for the output feedback mode (OFB mode), the IV must be unique. In particular, the (previously) common practice of re-using the last ciphertext block of a message as the IV for the next message is insecure (for example, this method was used by SSL 2.0). If an attacker knows
Google Clips
Google Clips is a discontinued miniature clip-on camera device developed by Google. == History == It was announced on October 4, 2017 and went on sale on January 27, 2018. Google Clips automatically captured video clips (without audio) at moments its machine learning algorithms determined to be interesting or relevant. An indicator flashed when the camera was looking for scenes to capture. Google Clips' artificial intelligence (AI) could learn the faces of people to take photographs with certain people, and could automatically set lighting and framing. It had 16 GB of storage built-in storage and could record clips for up to 3 hours. This camera was originally priced at US$249 in the United States. It was withdrawn from sale on October 15, 2019, but supported until the end of December 2021. == Reception == The Independent wrote that Google Clips is "an impressive little device, but one that also has the potential to feel very creepy." According to The Verge's generally negative review, "it didn't capture anything special" over two weeks of testing.
HYPO CBR
HYPO is a computer program, an expert system, that models reasoning with cases and hypotheticals in the legal domain. It is the first of its kind and the most sophisticated of the case-based legal reasoners, which was designed by Kevin Ashley for his Ph.D dissertation in 1987 at the University of Massachusetts Amherst under the supervision of Edwina Rissland. HYPO's design represents a hybrid generalization/comparative evaluation method appropriate for a domain with a weak analytical theory and applies to tasks that rarely involve just one right answer. The domain covers US trade secret law, and is substantially a common law domain. Since Anglo-American common law operates under the doctrine of precedent, the definitive way of interpreting problems is of necessity and case-based. Thus, HYPO did not involve the analysis of a statute, as required by the Prolog program. Rissland and Ashley (1987) envisioned HYPO as employing the key tasks performed by lawyers when analyzing case law for precedence to generate arguments for the prosecution or the defence. HYPO was a successful example of a general category of legal expert systems (LESs), it applies artificial intelligence (A.I.) techniques to the domain of legal reasoning in patent law, implementing a case-based reasoning (CBR) system, in contrast to rule based systems like MYCIN, or mixed-paradigm systems integrating CBR with rule-based or model-based reasoning like IKBALS II. A legal case-based reasoning essentially reasons from prior tried cases, comparing the contextual information in the current input case with that of cases previously tried and entered into the system. As noted by Ashley and Rissland (1988) CBR is used to "... capture expertise in domains where rules are ill-defined, incomplete or inconsistent". The HYPO project set out to model the creation of hypotheticals in law, where no case matches well enough. HYPO uses hypotheticals for a variety of tasks necessary for good interpretation: "to redefine old situations in terms of new dimensions, to create new standard cases when an appropriate one doesn’t exist, to explore and test the limits of a concept, to refocus a case by excluding some issues and to organize or cluster cases". Hypotheticals can include facts that support two conflicting lines of reasoning. So, it makes and responds to arguments from competing viewpoints about who should win the dispute. HYPO use heuristics such as making a case weaker or stronger, making a case extreme, enabling a near-miss, disabling a near-hit to generate hypotheticals in the context of an argument by using the dimensions mechanism. Dimensions have a range of values, along which the supportive strength that may shift from one side to the other. What differentiated this expert system from others was its facility not only to return a primary to best-case response but to return near-best-fit responses also. == Components == Legal knowledge in HYPO is contained in: the case-knowledge-base (CKB) and the library of dimensions. The CKB contains HYPO's base of known cases that are highly structured objects and sub-objects both real and hypothetical in the area of trade secret law. Each case is represented as a hierarchical set of frames whose slots are important facets of the case (e.g. Plaintiff, defendant, secret knowledge, employer/employee data).Ashley’s HYPO system used a database of thirty cases in the area indexed by thirteen dimensions. A key mechanism in HYPO is a dimension i.e. a mechanism to allow retrieval from the CKB, in order to represent legal cases. Ashley's dimensions are composed of (i) prerequisites, which are a set of factual predicates that must be satisfied for the dimension to apply (ii) focal slots, which accommodate one or two of the dimension's prerequisites designated as being indicative of the case's strength along that dimension and (iii) range information, which tells how a change in focal slot value effects the strength of a party's case along a given dimension. Dimensions focus attention on important aspects of cases. In HYPO's domain of misappropriation of trade secrets the dimension called “secrets voluntary disclosed” captures the idea that the more disclosures the plaintiff has made of his/her putative secret, the less convincing is his/her argument that the defendant is responsible for letting the secret. HYPO, like any other CBR system has also the following components: Similarity/relevancy metrics: that is, standards by which to evaluate the closeness of cases, judge their relevancy to the instant case, and select “most on point” cases. Half-Order Theory of the Application Domain: that is, hierarchies and taxonomies of knowledge, especially regarding the application domain. Precedent-based argumentation abilities: that is, capabilities to generate and evaluate precedent-based arguments. Knowledge to generate hypotheticals: that is, the ability to generate hypothetical cases to deal with various circumstances, like testing the validity of an interpretation or argument by providing gedanken experiments such as test cases or to fill in a weak CKB. == Functions == HYPO's method of creating an argument and justifying a solution or position has several steps. HYPO begins its processing with the current fact situation (cfs) which is direct input by the user into HYPO's representation framework. Once the user inputs the case, HYPO begins its legal analysis. The cfc is analyzed for relevant factors. Based on these factors HYPO selects the relevant cases and produces a case-analysis-record that records which dimensions apply to the cfc and which nearly apply (i.e. are "near misses"). The combined list of applicable and near miss dimensions is called the D-list. At this point the fact gathered module may request additional information from the user in order to draw a legal conclusion. Once all the facts are in the case-positioner module it uses the case-analysis record to create the claim lattice. This is a technique that organizes the relevant retrieved cases from the point of view of the cfc and makes it easy for HYPO to ascertain the most-on point cases (mopc) and to least on-point-cases. HYPO's arguments are 3ply, leading to the construction of the skeleton of an argument: it makes a point for one side, drawing the analogy between the problem and the precedent, responds with an argument for the opponent side, endeavoring to differentiate the cited case and citing other cases as counterarguments. Then it makes a final rebuttal, attempting to differentiate the counterarguments. The claim lattice also enables the HYPO-generator module to produce legally hypotheticals. With its use of dimension-based heuristics, the HYPO-generator does a heuristic search of the space of all possible cases. Lastly, the Explanation module expands upon the argument skeleton and provides explanation and justification for the different lines of analysis and cases found by HYPO. == An intelligent legal tutoring system == Legal expert systems are specifically designed to teach an area of law and are useful for pedagogical purposes. Ashley's work was mainly concerned to build tools to help students understand legal reasoning. Explanation and argument are the bases of the case method used in many professional schools in the U.S., first introduced by the Dean of the Harvard Law School, Christopher Columbus Langdell in 1870. The case method focuses on close readings of cases and principles; it involves students in pointed Socratic dialogue and makes strong use of hypotheticals (hypos). Thus, CATO (Aleven 1997) was a research project to device and test an intelligent, case-based tutorial program for teaching law students how to argue with cases implementing the HYPO program. Within the tutor system, Ashley and Aleven (1991) proposed to leverage an understanding of legal reasoning against the standard case-based tutoring methodology. What makes this tutoring system stand out is the additional levels of abstraction involved in its results. The system presents exercises, including the facts of a problem and a set of on-line cases and instructions to make, or respond to, a legal argument about the problem. The student/user will have a set of tools to analyze the problem and fashion an answer comparing it to other cases. Instead of simply generating precedent cases, the system works to interpret student responses, comparing them against a list of possibilities and responding to student entries, for example, by citing counterexamples, and providing feedback on a student's problem solving activities with explanations of correctness or giving further hints as to what may be wrong with evaluating a student's ability to perform legal reasoning and argument, examples and follow-up assignments by employing HYPO's model of case-based structure. == HYPO’s progeny == The quality of HYPO's results speak for themselves, in that a number of sequent legal reasoning systems are either directly based upon H
Blocks world
The blocks world is a planning domain in artificial intelligence. It consists of a set of wooden blocks of various shapes and colors sitting on a table. The goal is to build one or more vertical stacks of blocks. Only one block may be moved at a time: it may either be placed on the table or placed atop another block. Because of this, any blocks that are, at a given time, under another block cannot be moved. Moreover, some kinds of blocks cannot have other blocks stacked on top of them. The simplicity of this toy world lends itself readily to classical symbolic artificial intelligence approaches, in which the world is modeled as a set of abstract symbols which may be reasoned about. == Motivation == Artificial Intelligence can be researched in theory and with practical applications. The problem with most practical applications is that the engineers don't know how to program an AI system. Instead of rejecting the challenge at all the idea is to invent an easy to solve domain which is called a toy problem. Toy problems were invented with the aim to program an AI which can solve it. The blocks world domain is an example of a toy problem. Its major advantage over more realistic AI applications is that many algorithms and software programs are available which can handle the situation. This allows comparing different theories against each other. In its basic form, the blocks world problem consists of cubes of the same size which have all the color black. A mechanical robot arm has to pick and place the cubes. More complicated derivatives of the problem consist of cubes of different sizes, shapes and colors. From an algorithmic perspective, blocks world is an NP-hard search and planning problem. The task is to bring the system from an initial state into a goal state. Automated planning and scheduling problems are usually described in the Planning Domain Definition Language (PDDL) notation which is an AI planning language for symbolic manipulation tasks. If something was formulated in the PDDL notation, it is called a domain. Therefore, the task of stacking blocks is a blocks world domain which stands in contrast to other planning problems like the dock worker robot domain and the monkey and banana problem. == Theses/projects which took place in a blocks world == Terry Winograd's SHRDLU Patrick Winston's Learning Structural Descriptions from Examples and Copy Demo Gerald Jay Sussman's Sussman anomaly Decision problem (Gupta and Nau, 1992): Given a starting Blocks World, an ending Blocks World, and an integer L > 0, is there a way to move the blocks to change the starting position to the ending position with L or less steps? This decision problem is NP-hard.
SF8
SF8 (Korean: 에스 에프 에잇) is a South Korean science fiction anthology television series. It is a movie-drama crossover project between MBC, the Directors Guild of Korea, the OTT platform Wavve and the production company Soo Film. The director's cuts of all episodes were released on Wavve on July 10, 2020 while MBC TV aired one episode a week from August 14 to October 9, 2020. The series has been regarded as a Korean equivalent of the British series Black Mirror as they have the same format and similar themes, though Min Kyu-dong believes that SF8 is more diversified since eight different filmmakers were involved in the project. SF8 was screened at the 24th Bucheon International Fantastic Film Festival. == Synopsis == SF8 revolves around people who dream of a perfect society. It tackles the themes of artificial intelligence, augmented reality, virtual reality, robots, games, fantasy, horror, superpowers and disasters. == Episodes == Short summaries adapted from BiFan. == Production == === Development === Min Kyu-dong, creator of the series, said that "sci-fi movies were the driving force behind many movie directors' dreams. Unfortunately, due to the relatively high budget and narrow market limitations, various works were not able to be produced." He had been working on this project for two years before he partnered with Wavve and MBC. He also took charge of casting the actors, which lasted for a year. During a press conference held at CGV Yongsan I'Park Mall in Seoul on July 8, 2020, Min Kyu-dong said that all the episodes were produced with an equal amount of budget and that the overall budget was lower than one of a small commercial film. Roh Deok, who co-wrote and directed the "Manxin" episode, mentioned that "while commercial film productions [...] inevitably limit the directors' freedom as a creator, [they] had more independence in production" and "although there were physical limits, [he] thinks [they] went through the process of discovering what [they] can do inside those boundaries." === Filming === Eight directors from the Directors Guild of Korea (DGK) each directed an episode from the series. Filming began on February 21, 2020 with Jang Cheol-soo's "White Crow" and ended on May 7 with Kim Ui-seok's "Empty Body". Filming was completed within 10 filming sessions for each episode. === Credits === Credits adapted from BiFan. == Release == The director's cut was released on the OTT platform Wavve on July 10, 2020 and the original episodes were aired on MBC TV from August 14 to October 9.
Object model
In computing, object model has two related but distinct meanings: The properties of objects in general in a specific computer programming language, technology, notation or methodology that uses them. Examples are the object models of Java, the Component Object Model (COM), or Object-Modeling Technique (OMT). Such object models are usually defined using concepts such as class, generic function, message, inheritance, polymorphism, and encapsulation. There is an extensive literature on formalized object models as a subset of the formal semantics of programming languages. A collection of objects or classes through which a program can examine and manipulate some specific parts of its world. In other words, the object-oriented interface to some service or system. Such an interface is said to be the object model of the represented service or system. For example, the Document Object Model (DOM) is a collection of objects that represent a page in a web browser, used by script programs to examine and dynamically change the page. There is a Microsoft Excel object model [1] for controlling Microsoft Excel from another program, and the ASCOM Telescope Driver is an object model for controlling an astronomical telescope. == Features == An object model consists of the following important features: === Object reference === Objects can be accessed via object references. To invoke a method in an object, the object reference and method name are given, together with any arguments. === Interfaces === An interface provides a definition of the signature of a set of methods without specifying their implementation. An object will provide a particular interface if its class contains code that implement the method of that interface. An interface also defines types that can be used to declare the type of variables or parameters and return values of methods. === Actions === An action in object-oriented programming (OOP) is initiated by an object invoking a method in another object. An invocation can include additional information needed to carry out the method. The receiver executes the appropriate method and then returns control to the invoking object, sometimes supplying a result. === Exceptions === Programs can encounter various errors and unexpected conditions of varying seriousness. During the execution of the method many different problems may be discovered. Exceptions provide a clean way to deal with error conditions without complicating the code. A block of code may be defined to throw an exception whenever particular unexpected conditions or errors arise. This means that control passes to another block of code that catches the exception.
Pippit
Pippit (Chinese: 小云雀; pinyin: Xiǎoyúnquè) is an artificial intelligence content creation platform developed by the Chinese technology company ByteDance. The platform, powered by CapCut leverages multimodal AI technology to streamline professional-grade video and image production, specifically targeting small and medium-sized enterprisesand social media creators. == History == In May 2025, ByteDance officially launched Pippit, which is positioned as an AI video and picture creation tool. In early 2026, Pippit underwent a major architectural overhaul with the integration of the Dreamina seedance 2.0. This technical milestone introduced the "Short Drama Agent" functionality, which enables the end-to-end conversion of scripts up to 100,000 words into fully rendered video productions.