AI Face Photo Swap

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  • Sketchpad

    Sketchpad

    Sketchpad (a.k.a. Robot Draftsman) is a computer program written by Ivan Sutherland in 1963 in the course of his PhD thesis, for which he received the Turing Award in 1988, and the Kyoto Prize in 2012. It pioneered human–computer interaction (HCI), and is considered the ancestor of modern computer-aided design (CAD) programs and as a major breakthrough in the development of computer graphics in general. For example, Sketchpad inspired the graphical user interface (GUI) and object-oriented programming. Using the program, Sutherland showed that computer graphics could be used for both artistic and technical purposes and for demonstrating a novel method of human–computer interaction. == History == See History of the graphical user interface for a more detailed discussion of GUI development. == Software == Sketchpad was the earliest program ever to use a complete graphical user interface. The clever way the program organizes its geometric data pioneered the use of master (objects) and occurrences (instances) in computing and pointed forward to object-oriented programming. The main idea was to have master drawings which can be instantiated into many duplicates. When a master drawing is changed, then all instances change also. This was the first known form of an entity component system: for example instead of encapsulating points inside of a line object, the points are stored in a ring buffer as described in pages 48 to 52 of the paper, and the line only points to them. This allowed moving one point to alter all the shapes that use it in a single operation. The structures in Sketchpad were also able to store pointers to functions, to achieve a different behavior depending on the kind of object. In figure 3.8 of the paper, the "instances generic block" stores several "subroutine entries" which are pointers to functions: "display", "howbig" etc. This was an early form of virtual functions. Geometric constraints was another major invention in Sketchpad, letting a user easily constrain geometric properties in the drawing: for instance, the length of a line or the angle between two lines could be fixed. As a trade magazine said, clearly Sutherland "broke new ground in 3D computer modeling and visual simulation, the basis for computer graphics and CAD/CAM". Very few programs can be called precedents for his achievements. Patrick J. Hanratty is sometimes called the "father of CAD/CAM" and wrote PRONTO, a numerical control language at General Electric in 1957, and wrote CAD software while working for General Motors beginning in 1961. Sutherland wrote in his thesis that Bolt, Beranek and Newman had a "similar program" and T-Square was developed by Peter Samson and one or more fellow MIT students in 1962, both for the PDP-1. The Computer History Museum holds program listings for Sketchpad. == Hardware == Sketchpad ran on the MIT Lincoln Laboratory TX-2 (1958) computer at the Massachusetts Institute of Technology (MIT), which had 64k of 36-bit words. The user drew on the computer monitor screen with the recently invented light pen, which relayed information on its position by computing at what time the light from the scanning cathode-ray tube screen is detected. To configure the initial position of the light pen, the word INK was displayed on the screen, which, upon tapping, initialised the program with a white cross to continue keeping track of the pen's movement relative to its prior position. Of the 36 bits available to store each display spot in the display file, 20 gave the coordinates of that spot for the display system and the remaining 16 gave the address of the n-component element responsible for adding that spot to display. The TX-2 was an experimental machine and the hardware changed often (on Wednesdays, according to Sutherland). By 1975, the light pen and the cathode-ray tube with which it had been used had been removed. == Publications == The Sketchpad program was part and parcel of Sutherland's Ph.D. thesis at MIT and peripherally related to the Computer-Aided Design project at that time. Sketchpad: A Man-Machine Graphical Communication System.

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  • CrySyS Lab

    CrySyS Lab

    CrySyS Lab (Hungarian pronunciation: [ˈkriːsis]) is part of the Department of Telecommunications at the Budapest University of Technology and Economics. The name is derived from "Laboratory of Cryptography and System Security", the full Hungarian name is CrySys Adat- és Rendszerbiztonság Laboratórium. == History == CrySyS Lab. was founded in 2003 by a group of security researchers at the Budapest University of Technology and Economics. Currently, it is located in the Infopark Budapest. The heads of the lab were Dr. István Vajda (2003–2010) and Dr. Levente Buttyán (2010-now). Since its establishment, the lab participated in several research and industry projects, including successful EU FP6 and FP7 projects (SeVeCom, a UbiSecSens and WSAN4CIP). == Research results == CrySyS Lab is recognized in research for its contribution to the area of security in wireless embedded systems. In this area, the members of the lab produced 5 books 4 book chapters 21 journal papers 47 conference papers 3 patents 2 Internet Draft The above publications had an impact factor of 30+ and obtained more than 7500 references. Several of these publications appeared in highly cited journals (e.g., IEEE Transactions on Dependable and Secure Systems, IEEE Transactions on Mobile Computing). == Forensics analysis of malware incidents == The laboratory was involved in the forensic analysis of several high-profile targeted attacks. In October 2011, CrySyS Lab discovered the Duqu malware; pursued the analysis of the Duqu malware and as a result of the investigation, identified a dropper file with an MS 0-day kernel exploit inside; and finally released a new open-source Duqu Detector Toolkit to detect Duqu traces and running Duqu instances. In May 2012, the malware analysis team at CrySyS Lab participated in an international collaboration aiming at the analysis of an as yet unknown malware, which they call sKyWIper. At the same time Kaspersky Lab analyzed the malware Flame and Iran National CERT (MAHER) the malware Flamer. Later, they turned out to be the same. Other analysis published by CrySyS Lab include the password analysis of the Hungarian ISP, Elender, and a thorough Hungarian security survey of servers after the publications of the Kaminsky DNS attack.

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  • Short Weather Cipher

    Short Weather Cipher

    The Short Weather Cipher (German: Wetterkurzschlüssel, abbreviated WKS), also known as the weather short signal book, was a cipher, presented as a codebook, that was used by the radio telegraphists aboard U-boats of the German Navy (Kriegsmarine) during World War II. It was used to condense weather reports into a short 7-letter message, which was enciphered by using the naval Enigma and transmitted by radiomen to intercept stations on shore, where it was deciphered by Enigma and the 7-letter weather report was reconstructed. == History == During World War II, during various times, different versions of the cipher were in operation. The first issue carried the codename Weimar. It was replaced by the edition Eisenach on 20 January 1942. On 10 March 1943, the third edition of the weather key, bearing the codename Naumburg, entered into force. On May 9, 1941, during Operation Primrose, the operation to occupy Åndalsnes and create a diversion south of Trondheim in Norway as part of the Norwegian Campaign, an intact Naval Enigma (M3) cipher machine, a copy of the "Weimar" version of the short weather cipher and a copy of the short signal book (German: Kurzsignalbuch or Kurzsignale for short) was recovered from the submarine U-110, that was captured in the North Atlantic east of Cape Farewell, Greenland. This enabled the cryptanalysts in Bletchley Park to break the encryption of the M3 and to decipher the German submarine radio messages. The Short Weather Cipher was critical in the cryptanalysis of the Naval Enigma M4 and yielded excellent cribs. On 30 October 1942, a copy of the Wetterkurzschlüssel, the short weather cipher, and of the short signal book, the Kurzsignale, were recovered as part of a daring raid on the U-boat U-559, when three Royal Navy sailors, Lieutenant Anthony Fasson, Able Seaman Colin Grazier and NAAFI canteen assistant Tommy Brown, then boarded the abandoned submarine, and recovered the documents after a 90-minute search. They reached the Government Code and Cypher at Bletchley Park after a three-week delay, on 24 November 1942. The documents which cost the lives of Fasson and Grazier proved to be particularly important in breaking the Naval Enigma M4. The version of the short weather cipher recovered was the Eisenach version. Unlike the first version Weimar, the Eisenach did not list the 26 rotor positions that were indicated by a letter, to be used in enciphering weather reports. Thus, Hut 8 cryptanalysts thought that all four rotors were used to encipher weather reports. Testing on the Bombes began to surface weather kisses (identical messages in two cryptosystems). On 13 December 1942, a crib obtained using the Short Weather Cipher gave a key with the Naval Enigma M4 rotatable Umkehrwalze (reversing roller or reflector) in the neutral position, making it equivalent to a standard Enigma and thus making B-Dienst messages potentially breakable on existing bombes. Hut 8 learned that the 4-letter indicators for regular U-boat messages were the same as 3-letter indicators for weather messages the same day, except for one extra letter. This meant that once the key was found for a weather message on any day, the fourth rotor had to be only tested in 26 positions to find the full 4-letter key. By the end of the day on Sunday 13 December, Rodger Winn of the Submarine Tracking Room at Bletchley Park knew that Shark Enigma Cipher was broken. When the third edition of the short signal book was introduced on 10 March 1943, Hut 8 was immediately deprived of cribs. However, by the 19 March, cribs were again being used by Hut 8 personnel, using the method of employing short signal sighting reports. These were reports made by U-boats when contact was made with Kurzsignalheft code book. Hut 8 managed to solve Shark for 90 out of 112 days before the end of June. Kurzsignalheft short sighting reports also used M4 in M3 mode. By the end of June, four-rotor bombes had entered service at Bletchley Park, and by August had been introduced by the US Navy. From September onwards, Shark was generally solved within 24 hours. == Operation == The U-boat encoded weather reports using the Short Weather Cipher, before being enciphered on the Naval Enigma. The shore patrol of the Kriegsmarine, deciphered the message and decoded it, then forwarding it to a central meteorological station, which rebroadcast the data as ship synoptics, after enciphering it with additive tables using a cipher, which was called Germet 3 by Hut 8 personnel. The short weather cipher coded weather reports using a polyphonic single-letter code with X missing. A = +28° ◦ B = +27° ◦ C = +26° ◦ D = +25° ◦ . . . ◦ W = +6° ◦ Y= +5° ◦ Z = +4° ◦ A = +3° ◦ B = +2° ◦ C = +1° ◦ D = 0° ◦ E =−1° ◦ F =−2° ◦ . . . ◦ Z = −21° ◦ In a similar way, water temperature, atmospheric pressure, humidity, wind direction, wind velocity, visibility, degree of cloudiness, geographic latitude, and geographic longitude had to be coded in a prescribed order with the weather report consisted of a single short word. Based on the approximate knowledge of the position of the submarine, the Kriegsmarine telegraphist who received the message could translate the letter "S", according to the above table, which could mean 10 °C or −15 °C, back to the correct temperature. Similarly, the direction and the type of swell was also coded with only a single letter: ----------------------------------------------------- Direction from which | Type of swell the swell comes | low | middle high | high | ----------------------------------------------------- N | a | i | q | NE | b | j | r | E | c | k | s | SE | d | l | t | S | e | m | u | SW | f | n | v | W | g | o | w | NW | h | p | x | No swelling | | | | y Intermittent | | | | z As an example of the cipher, a weather report for 68° North latitude, 20° West longitude (north of Iceland) with atmospheric pressure 972 millibars, temperature minus 5 °C, wind northwest Force 6 (on the Beaufort scale), 3/10 cirrus cloud cover, visibility 5 nautical miles, would be coded as MZNFPED. == Publications == Bauer, Arthur O. (1997), Funkpeilung als alliierte Waffe gegen deutsche U-Boote 1939–1945 [Direction finding as Allied weapon against German submarines from 1939 to 1945] (in German), Diemen, NL: Selbstverlag, ISBN 978-3-00-002142-8 Bauer, Friedrich L. (2007), Decrypted Secrets. Methods and Maxims of Cryptology (4., rev. and extended ed.), Berlin Heidelberg New York: Springer, ISBN 978-3-540-24502-5 Pfeiffer, Paul N. (October 1998), "Breaking the German Weather Ciphers in the Mediterranean Detachment, 849th Signal Intelligence Service", Cryptologia, 22 (4): 354–369, doi:10.1080/0161-119891886975, ISSN 0161-1194 Ulbricht, Heinz (2005), Die Chiffriermaschine Enigma – Trügerische Sicherheit. Ein Beitrag zur Geschichte der Nachrichtendienste [The Enigma cipher machine – Deceptive security. A contribution to the history of the intelligence services], Dissertation, Fachbereich Mathematik und Informatik, Technische Universität Braunschweig (in German)

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  • Link encryption

    Link encryption

    Link encryption is an approach to communications security that encrypts and decrypts all network traffic at each network routing point (e.g. network switch, or node through which it passes) until arrival at its final destination. This repeated decryption and encryption is necessary to allow the routing information contained in each transmission to be read and employed further to direct the transmission toward its destination, before which it is re-encrypted. This contrasts with end-to-end encryption where internal information, but not the header/routing information, is encrypted by the sender at the point of origin and only decrypted by the intended recipient. Link encryption offers two main advantages: encryption is automatic so there is less opportunity for human error. if the communications link operates continuously and carries an unvarying level of traffic, link encryption defeats traffic analysis. On the other hand, end-to-end encryption ensures only the intended recipient has access to the plaintext. Link encryption can be used with end-to-end systems by superencrypting the messages. Bulk encryption refers to encrypting a large number of circuits at once, after they have been multiplexed.

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  • Quantexa

    Quantexa

    Quantexa is a UK-based software company that develops artificial intelligence-based applications for data analytics and decision-making. The company was founded in 2016 and is headquartered in London, with operations in North America, Europe, and the Asia-Pacific region. As of 2025, Quantexa reported a valuation of $2.6 billion and provides services to organizations in over 70 countries. Investors include Warburg Pincus, HSBC, and the Ontario Teachers’ Pension Plan. == History == Quantexa was founded in London in 2016 by several co-founders, including Jamie Hutton, Richard Seewald, Imam Hoque, Felix Hoddinott, and Vishal Marria, who also serves as the company's chief executive officer. The company was established to develop tools intended to address limitations in traditional data analysis methods, particularly those related to identifying hidden connections across large datasets. The name "Quantexa" is derived from the company's focus on quantitative methods and data analysis. In 2023, Quantexa acquired Dublin-based AI firm Aylien. In April 2023, the company completed a Series E funding round, raising $129 million at a valuation of approximately $1.8 billion, marking its entry into "unicorn" status. In October 2024, the company reported annual recurring revenue (ARR) exceeding $100 million. In early 2025, Quantexa participated in the World Economic Forum's Unicorn Program, which supports high-growth technology companies. In March 2025, Quantexa completed a Series F funding round of $175 million, led by Teachers' Venture Growth, the venture arm of the Ontario Teachers' Pension Plan. That August, the company was reported to be considering a 2026 IPO. The company formed a partnership with Zurich in October 2025, the first insurer to add its AI-based Decision Intelligence platform to enhance fraud detection.

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  • Backdoor (computing)

    Backdoor (computing)

    A backdoor is a typically covert method of bypassing normal authentication or encryption in a computer, product, embedded device (e.g. a home router), or its embodiment (e.g. part of a cryptosystem, algorithm, chipset, or even a "homunculus computer"—a tiny computer-within-a-computer such as that found in Intel's AMT technology). Backdoors are most often used for securing remote access to a computer, or obtaining access to plaintext in cryptosystems. From there it may be used to gain access to privileged information like passwords, corrupt or delete data on hard drives, or transfer information within compromised networks. In the United States, the 1994 Communications Assistance for Law Enforcement Act forces internet providers to provide backdoors for government authorities. In 2024, the U.S. government realized that China had been tapping communications in the U.S. using that infrastructure for months, or perhaps longer; China recorded presidential candidate campaign office phone calls—including employees of the then-vice president of the nation, and of the candidates themselves. A backdoor may take the form of a hidden part of a program, a separate program (e.g. Back Orifice may subvert the system through a rootkit), code in the firmware of the hardware, or parts of an operating system such as Windows, for example, device drivers. Trojan horses can be used to create vulnerabilities in a device. A Trojan horse may appear to be an entirely legitimate program, but when executed, it triggers an activity that may install a backdoor. Although some are secretly installed, other backdoors are deliberate and widely known. These kinds of backdoors have "legitimate" uses such as providing the manufacturer with a way to restore user passwords. Many systems that store information within the cloud fail to create accurate security measures. If many systems are connected within the cloud, hackers can gain access to all other platforms through the most vulnerable system. Default passwords (or other default credentials) can function as backdoors if they are not changed by the user. Some debugging features can also act as backdoors if they are not removed in the release version. In 1993, the United States government attempted to deploy an encryption system, the Clipper chip, with an explicit backdoor for law enforcement and national security access. The chip was unsuccessful. Recent proposals to counter backdoors include creating a database of backdoors' triggers and then using neural networks to detect them. == Overview == The threat of backdoors surfaced when multiuser and networked operating systems became widely adopted. Petersen and Turn discussed computer subversion in a paper published in the proceedings of the 1967 AFIPS Conference. They noted a class of active infiltration attacks that use "trapdoor" entry points into the system to bypass security facilities and permit direct access to data. The use of the word trapdoor here clearly coincides with more recent definitions of a backdoor. However, since the advent of public key cryptography the term trapdoor has acquired a different meaning (see: Trapdoor function), and thus the term "backdoor" is now preferred, only after the term trapdoor went out of use. More generally, such security breaches were discussed at length in a RAND Corporation task force report published under DARPA sponsorship by J.P. Anderson and D.J. Edwards in 1970. While initially targeting the computer vision domain, backdoor attacks have expanded to encompass various other domains, including text, audio, ML-based computer-aided design, and ML-based wireless signal classification. Additionally, vulnerabilities in backdoors have been demonstrated in deep generative models, reinforcement learning (e.g., AI GO), and deep graph models. These broad-ranging potential risks have prompted concerns from national security agencies regarding their potentially disastrous consequences. A backdoor in a login system might take the form of a hard coded user and password combination which gives access to the system. An example of this sort of backdoor was used as a plot device in the 1983 film WarGames, in which the architect of the "WOPR" computer system had inserted a hardcoded password-less account which gave the user access to the system, and to undocumented parts of the system (in particular, a video game-like simulation mode and direct interaction with the artificial intelligence). Although the number of backdoors in systems using proprietary software (software whose source code is not publicly available) is not widely credited, they are nevertheless frequently exposed. Programmers have even succeeded in secretly installing large amounts of benign code as Easter eggs in programs, although such cases may involve official forbearance, if not actual permission. == Examples == === Worms === Many computer worms, such as Sobig and Mydoom, install a backdoor on the affected computer (generally a PC on broadband running Microsoft Windows and Microsoft Outlook). Such backdoors appear to be installed so that spammers can send junk e-mail from the infected machines. Others, such as the Sony/BMG rootkit, placed secretly on millions of music CDs through late 2005, are intended as DRM measures—and, in that case, as data-gathering agents, since both surreptitious programs they installed routinely contacted central servers. A sophisticated attempt to plant a backdoor in the Linux kernel, exposed in November 2003, added a small and subtle code change by subverting the revision control system. In this case, a two-line change appeared to check root access permissions of a caller to the sys_wait4 function, but because it used assignment = instead of equality checking ==, it actually granted permissions to the system. This difference is easily overlooked, and could even be interpreted as an accidental typographical error, rather than an intentional attack. In January 2014, a backdoor was discovered in certain Samsung Android products, like the Galaxy devices. The Samsung proprietary Android versions are fitted with a backdoor that provides remote access to the data stored on the device. In particular, the Samsung Android software that is in charge of handling the communications with the modem, using the Samsung IPC protocol, implements a class of requests known as remote file server (RFS) commands, that allows the backdoor operator to perform via modem remote I/O operations on the device hard disk or other storage. As the modem is running Samsung proprietary Android software, it is likely that it offers over-the-air remote control that could then be used to issue the RFS commands and thus to access the file system on the device. === Object code backdoors === Harder to detect backdoors involve modifying object code, rather than source code—object code is much harder to inspect, as it is designed to be machine-readable, not human-readable. These backdoors can be inserted either directly in the on-disk object code, or inserted at some point during compilation, assembly linking, or loading—in the latter case the backdoor never appears on disk, only in memory. Object code backdoors are difficult to detect by inspection of the object code, but are easily detected by simply checking for changes (differences), notably in length or in checksum, and in some cases can be detected or analyzed by disassembling the object code. Further, object code backdoors can be removed (assuming source code is available) by simply recompiling from source on a trusted system. Thus for such backdoors to avoid detection, all extant copies of a binary must be subverted, and any validation checksums must also be compromised, and source must be unavailable, to prevent recompilation. Alternatively, these other tools (length checks, diff, checksumming, disassemblers) can themselves be compromised to conceal the backdoor, for example detecting that the subverted binary is being checksummed and returning the expected value, not the actual value. To conceal these further subversions, the tools must also conceal the changes in themselves—for example, a subverted checksummer must also detect if it is checksumming itself (or other subverted tools) and return false values. This leads to extensive changes in the system and tools being needed to conceal a single change. As object code can be regenerated by recompiling (reassembling, relinking) the original source code, making a persistent object code backdoor (without modifying source code) requires subverting the compiler itself—so that when it detects that it is compiling the program under attack it inserts the backdoor—or alternatively the assembler, linker, or loader. As this requires subverting the compiler, this in turn can be fixed by recompiling the compiler, removing the backdoor insertion code. This defense can in turn be subverted by putting a source meta-backdoor in the compiler, so that when it detects that it is compiling itself

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  • Verifiable secret sharing

    Verifiable secret sharing

    In cryptography, a secret sharing scheme is verifiable if auxiliary information is included that allows players to verify their shares as consistent. More formally, verifiable secret sharing ensures that even if the dealer is malicious there is a well-defined secret that the players can later reconstruct. (In standard secret sharing, the dealer is assumed to be honest.) The concept of verifiable secret sharing (VSS) was first introduced in 1985 by Benny Chor, Shafi Goldwasser, Silvio Micali and Baruch Awerbuch. In a VSS protocol a distinguished player who wants to share the secret is referred to as the dealer. The protocol consists of two phases: a sharing phase and a reconstruction phase. Sharing: Initially the dealer holds secret as input and each player holds an independent random input. The sharing phase may consist of several rounds. At each round each player can privately send messages to other players and can also broadcast a message. Each message sent or broadcast by a player is determined by its input, its random input and messages received from other players in previous rounds. Reconstruction: In this phase each player provides its entire view from the sharing phase and a reconstruction function is applied and is taken as the protocol's output. An alternative definition given by Oded Goldreich defines VSS as a secure multi-party protocol for computing the randomized functionality corresponding to some (non-verifiable) secret sharing scheme. This definition is stronger than that of the other definitions and is very convenient to use in the context of general secure multi-party computation. Verifiable secret sharing is important for secure multiparty computation. Multiparty computation is typically accomplished by making secret shares of the inputs, and manipulating the shares to compute some function. To handle "active" adversaries (that is, adversaries that corrupt nodes and then make them deviate from the protocol), the secret sharing scheme needs to be verifiable to prevent the deviating nodes from throwing off the protocol. == Feldman's scheme == A commonly used example of a simple VSS scheme is the protocol by Paul Feldman, which is based on Shamir's secret sharing scheme combined with any encryption scheme which satisfies a specific homomorphic property (that is not necessarily satisfied by all homomorphic encryption schemes). The following description gives the general idea, but is not secure as written. (Note, in particular, that the published value gs leaks information about the dealer's secret s.) First, a cyclic group G of prime order q, along with a generator g of G, is chosen publicly as a system parameter. The group G must be chosen such that computing discrete logarithms is hard in this group. (Typically, one takes an order-q subgroup of (Z/pZ)×, where q is a prime dividing p − 1.) The dealer then computes (and keeps secret) a random polynomial P of degree t with coefficients in Zq, such that P(0) = s, where s is the secret. Each of the n share holders will receive a value P(1), ..., P(n) modulo q. Any t + 1 share holders can recover the secret s by using polynomial interpolation modulo q, but any set of at most t share holders cannot. (In fact, at this point any set of at most t share holders has no information about s.) So far, this is exactly Shamir's scheme. To make these shares verifiable, the dealer distributes commitments to the coefficients of P modulo q. If P(x) = s + a1x + ... + atxt, then the commitments that must be given are: c0 = gs, c1 = ga1, ... ct = gat. Once these are given, any party can verify their share. For instance, to verify that v = P(i) modulo q, party i can check that g v = c 0 c 1 i c 2 i 2 ⋯ c t i t = ∏ j = 0 t c j i j = ∏ j = 0 t g a j i j = g ∑ j = 0 t a j i j = g P ( i ) {\displaystyle g^{v}=c_{0}c_{1}^{i}c_{2}^{i^{2}}\cdots c_{t}^{i^{t}}=\prod _{j=0}^{t}c_{j}^{i^{j}}=\prod _{j=0}^{t}g^{a_{j}i^{j}}=g^{\sum _{j=0}^{t}a_{j}i^{j}}=g^{P(i)}} . This scheme is, at best, secure against computationally bounded adversaries, namely the intractability of computing discrete logarithms. Pedersen proposed later a scheme where no information about the secret is revealed even with a dealer with unlimited computing power. == Baghery's hash-based scheme == A recent line of research has proposed a unified framework, for building practical VSS schemes that do not necessarily require homomorphic commitments —a key requirement in traditional constructions such as Feldman's and Pedersen's schemes. The framework allows instantiations with different commitment schemes, including post-quantum secure options such as hash-based commitments. This offers a flexible and efficient approach to build VSS schemes, in which the verifiability of shares is decoupled from the need for homomorphic commitments, which are often tied to assumptions like the Discrete Logarithm (DL) problem, known to be insecure against quantum adversaries. One instantiation of the new framework uses hash-based commitments and a random oracle to construct a hash-based VSS scheme based on Shamir's secret sharing. === Protocol Overview === Sharing Phase: Given a secure hash-based commitment scheme C {\displaystyle {\mathcal {C}}} and a hash function H {\displaystyle {\mathcal {H}}} (modeled as a random oracle), to share a secret value s {\displaystyle s} among n {\displaystyle n} parties with threshold t {\displaystyle t} , the dealer acts as follows: Following Shamir sharing, the dealer samples a random degree- t {\displaystyle t} polynomial P ( X ) {\displaystyle P(X)} over a filed or ring, with P ( 0 ) = s {\displaystyle P(0)=s} . Each of the n {\displaystyle n} parties will receive a value v i = P ( i ) {\displaystyle v_{i}=P(i)} modulo q {\displaystyle q} as a share. To prove the validity of the shares, the dealer acts as follows: Samples another random degree- t {\displaystyle t} polynomial R ( X ) {\displaystyle R(X)} and n {\displaystyle n} random values γ 1 , … , γ n {\displaystyle \gamma _{1},\dots ,\gamma _{n}} from the same filed or ring. Computes a set of commitments c i = C ( P ( i ) , R ( i ) , γ i ) {\displaystyle c_{i}={\mathcal {C}}(P(i),R(i),\gamma _{i})} for i = 1 , 2 , … , n {\displaystyle i=1,2,\dots ,n} . Note that, the additional randomness γ i {\displaystyle \gamma _{i}} is used when the secret s {\displaystyle s} does not have sufficient entropy, but it can be omitted when sharing a uniformly random secret. Each of the n {\displaystyle n} parties will also receive a value γ i {\displaystyle \gamma _{i}} modulo q {\displaystyle q} as a share. Calculates a challenge value d {\displaystyle d} via a hash function d = H ( c 1 , … , c n ) {\displaystyle d={\mathcal {H}}(c_{1},\dots ,c_{n})} and then computes a polynomial Z ( X ) = R ( X ) + d ⋅ P ( X ) {\displaystyle Z(X)=R(X)+d\cdot P(X)} . Broadcasts the commitments c 1 , … , c n {\displaystyle c_{1},\dots ,c_{n}} along with Z ( X ) {\displaystyle Z(X)} as the proof and privately sends ( v i , γ i ) {\displaystyle (v_{i},\gamma _{i})} as the individual share to party i {\displaystyle i} . Verification Phase: Given an individual share ( v i , γ i ) {\displaystyle (v_{i},\gamma _{i})} and a proof ( c 1 , … , c n , Z ( X ) ) {\displaystyle (c_{1},\dots ,c_{n},Z(X))} , party i {\displaystyle i} verifies the correctness of it as below: Checks that Z ( X ) {\displaystyle Z(X)} is a valid (up to) degree- t {\displaystyle t} polynomial. Recomputes the challenge value d = H ( c 1 , … , c n ) {\displaystyle d={\mathcal {H}}(c_{1},\dots ,c_{n})} , and verifies the commitment equation c i = C ( v i , Z ( i ) − d v i , γ i ) {\displaystyle c_{i}={\mathcal {C}}(v_{i},Z(i)-dv_{i},\gamma _{i})} . If the verification fails, similar to Feldman’s and Pedersen’s schemes, the party raises a complaint. If too many complaints (more than t {\displaystyle t} ) are raised, the dealer is disqualified. In case of a complaint, the dealer can publicly reveal the disputed share to allow global verification. Honest parties can then collectively agree to either continue or disqualify the dealer. This scheme supports the sharing of both low-entropy and high-entropy secrets. Moreover, since it relies solely on secure hash functions for commitments and on a (quantum) random oracle, it plausibly achieves security even against quantum adversaries. Additionally, by using only lightweight cryptographic primitives, the scheme is considerably more efficient in practice compared to traditional VSS constructions based on number-theoretic assumptions. == Benaloh's scheme == Once n shares are distributed to their holders, each holder should be able to verify that all shares are collectively t-consistent (i.e., any subset t of n shares will yield the same, correct, polynomial without exposing the secret). In Shamir's secret sharing scheme the shares s 1 , s 2 , . . . , s n {\displaystyle s_{1},s_{2},...,s_{n}} are t-consistent if and only if the interpolation of the points ( 1 , s 1 ) , ( 2 , s 2 ) , . . . , (

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  • Data lake

    Data lake

    A data lake is a system or repository of data stored in its natural/raw format, usually object blobs or files. A data lake is usually a single store of data including raw copies of source system data, sensor data, social data etc., and transformed data used for tasks such as reporting, visualization, advanced analytics, and machine learning. A data lake can include structured data from relational databases (rows and columns), semi-structured data (CSV, logs, XML, JSON), unstructured data (emails, documents, PDFs), and binary data (images, audio, video). A data lake can be established on premises (within an organization's data centers) or in the cloud (using cloud services). == Background == James Dixon, then chief technology officer at Pentaho, coined the term by 2011 to contrast it with data mart, which is a smaller repository of interesting attributes derived from raw data. In promoting data lakes, he argued that data marts have several inherent problems, such as information siloing. PricewaterhouseCoopers (PwC) said that data lakes could "put an end to data silos". In their study on data lakes, they noted that enterprises were "starting to extract and place data for analytics into a single, Hadoop-based repository." == Examples == Many companies use cloud storage services such as Google Cloud Storage and Amazon S3 or a distributed file system such as Apache Hadoop distributed file system (HDFS). There is a gradual academic interest in the concept of data lakes. For example, Personal DataLake at Cardiff University is a new type of data lake which aims at managing big data of individual users by providing a single point of collecting, organizing, and sharing personal data. Early data lakes, such as Hadoop 1.0, had limited capabilities because it only supported batch-oriented processing (Map Reduce). Interacting with it required expertise in Java, map reduce and higher-level tools like Apache Pig, Apache Spark and Apache Hive (which were also originally batch-oriented). == Criticism == Poorly managed data lakes have been facetiously called data swamps. In June 2015, David Needle characterized "so-called data lakes" as "one of the more controversial ways to manage big data". PwC was also careful to note in their research that not all data lake initiatives are successful. They quote Sean Martin, CTO of Cambridge Semantics: We see customers creating big data graveyards, dumping everything into Hadoop distributed file system (HDFS) and hoping to do something with it down the road. But then they just lose track of what’s there. The main challenge is not creating a data lake, but taking advantage of the opportunities it presents. They describe companies that build successful data lakes as gradually maturing their lake as they figure out which data and metadata are important to the organization. Another criticism is that the term data lake is used with many different meanings. It may be used to refer to, for example: any tools or data management practices that are not data warehouses; a particular technology for implementation; a raw data reservoir; a hub for ETL offload; or a central hub for self-service analytics. While critiques of data lakes are warranted, in many cases they apply to other data projects as well. For example, the definition of data warehouse is also changeable, and not all data warehouse efforts have been successful. In response to various critiques, McKinsey noted that the data lake should be viewed as a service model for delivering business value within the enterprise, not a technology outcome. == Data lakehouses == Data lakehouses are a hybrid approach that can ingest a variety of raw data formats like a data lake, while also providing ACID transactions and enforced data quality like a data warehouse.

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  • Zeuthen strategy

    Zeuthen strategy

    The Zeuthen strategy in cognitive science is a negotiation strategy used by some artificial agents. Its purpose is to measure the willingness to risk conflict. An agent will be more willing to risk conflict if it does not have much to lose in case that the negotiation fails. In contrast, an agent is less willing to risk conflict when it has more to lose. The value of a deal is expressed in its utility. An agent has much to lose when the difference between the utility of its current proposal and the conflict deal is high. When both agents use the monotonic concession protocol, the Zeuthen strategy leads them to agree upon a deal in the negotiation set. This set consists of all conflict free deals, which are individually rational and Pareto optimal, and the conflict deal, which maximizes the Nash product. The strategy was introduced in 1930 by the Danish economist Frederik Zeuthen. == Three key questions == The Zeuthen strategy answers three open questions that arise when using the monotonic concession protocol, namely: Which deal should be proposed at first? On any given round, who should concede? In case of a concession, how much should the agent concede? The answer to the first question is that any agent should start with its most preferred deal, because that deal has the highest utility for that agent. The second answer is that the agent with the smallest value of Risk(i,t) concedes, because the agent with the lowest utility for the conflict deal profits most from avoiding conflict. To the third question, the Zeuthen strategy suggests that the conceding agent should concede just enough raise its value of Risk(i,t) just above that of the other agent. This prevents the conceding agent to have to concede again in the next round. == Risk == Risk ( i , t ) = { 1 U i ( δ ( i , t ) ) = 0 U i ( δ ( i , t ) ) − U i ( δ ( j , t ) ) U i ( δ ( i , t ) ) otherwise {\displaystyle {\text{Risk}}(i,t)={\begin{cases}1&U_{i}(\delta (i,t))=0\\{\frac {U_{i}(\delta (i,t))-U_{i}(\delta (j,t))}{U_{i}(\delta (i,t))}}&{\text{otherwise}}\end{cases}}} Risk(i,t) is a measurement of agent i's willingness to risk conflict. The risk function formalizes the notion that an agent's willingness to risk conflict is the ratio of the utility that agent would lose by accepting the other agent's proposal to the utility that agent would lose by causing a conflict. Agent i is said to be using a rational negotiation strategy if at any step t + 1 that agent i sticks to his last proposal, Risk(i,t) > Risk(j,t). == Sufficient concession == If agent i makes a sufficient concession in the next step, then, assuming that agent j is using a rational negotiation strategy, if agent j does not concede in the next step, he must do so in the step after that. The set of all sufficient concessions of agent i at step t is denoted SC(i, t). == Minimal sufficient concession == δ ′ = arg ⁡ max δ ∈ S C ( A , t ) { U A ( δ ) } {\displaystyle \delta '=\arg \max _{\delta \in {SC(A,t)}}\{U_{A}(\delta )\}} is the minimal sufficient concession of agent A in step t. Agent A begins the negotiation by proposing δ ( A , 0 ) = arg ⁡ max δ ∈ N S U A ( δ ) {\displaystyle \delta (A,0)=\arg \max _{\delta \in {NS}}U_{A}(\delta )} and will make the minimal sufficient concession in step t + 1 if and only if Risk(A,t) ≤ Risk(B,t). Theorem If both agents are using Zeuthen strategies, then they will agree on δ = arg ⁡ max δ ′ ∈ N S { π ( δ ′ ) } , {\displaystyle \delta =\arg \max _{\delta '\in {NS}}\{\pi (\delta ')\},} that is, the deal which maximizes the Nash product. Proof Let δA = δ(A,t). Let δB = δ(B,t). According to the Zeuthen strategy, agent A will concede at step t {\displaystyle t} if and only if R i s k ( A , t ) ≤ R i s k ( B , t ) . {\displaystyle Risk(A,t)\leq Risk(B,t).} That is, if and only if U A ( δ A ) − U A ( δ B ) U A ( δ A ) ≤ U B ( δ B ) − U B ( δ A ) U B ( δ B ) {\displaystyle {\frac {U_{A}(\delta _{A})-U_{A}(\delta _{B})}{U_{A}(\delta _{A})}}\leq {\frac {U_{B}(\delta _{B})-U_{B}(\delta _{A})}{U_{B}(\delta _{B})}}} U B ( δ B ) ( U A ( δ A ) − U A ( δ B ) ) ≤ U A ( δ A ) ( U B ( δ B ) − U B ( δ A ) ) {\displaystyle U_{B}(\delta _{B})(U_{A}(\delta _{A})-U_{A}(\delta _{B}))\leq U_{A}(\delta _{A})(U_{B}(\delta _{B})-U_{B}(\delta _{A}))} U A ( δ A ) U B ( δ B ) − U A ( δ B ) U B ( δ B ) ≤ U A ( δ A ) U B ( δ B ) − U A ( δ A ) U B ( δ A ) {\displaystyle U_{A}(\delta _{A})U_{B}(\delta _{B})-U_{A}(\delta _{B})U_{B}(\delta _{B})\leq U_{A}(\delta _{A})U_{B}(\delta _{B})-U_{A}(\delta _{A})U_{B}(\delta _{A})} − U A ( δ B ) U B ( δ B ) ≤ − U A ( δ A ) U B ( δ A ) {\displaystyle -U_{A}(\delta _{B})U_{B}(\delta _{B})\leq -U_{A}(\delta _{A})U_{B}(\delta _{A})} U A ( δ A ) U B ( δ A ) ≤ U A ( δ B ) U B ( δ B ) {\displaystyle U_{A}(\delta _{A})U_{B}(\delta _{A})\leq U_{A}(\delta _{B})U_{B}(\delta _{B})} π ( δ A ) ≤ π ( δ B ) {\displaystyle \pi (\delta _{A})\leq \pi (\delta _{B})} Thus, Agent A will concede if and only if δ A {\displaystyle \delta _{A}} does not yield the larger product of utilities. Therefore, the Zeuthen strategy guarantees a final agreement that maximizes the Nash Product.

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  • Data thinking

    Data thinking

    Data Thinking is a framework that integrates data science with the design process. It combines computational thinking, statistical thinking, and domain-specific knowledge to guide the development of data-driven solutions in product development. The framework is used to explore, design, develop, and validate solutions, with a focus on user experience and data analytics, including data collection and interpretation The framework aims to apply data literacy and inform decision-making through data-driven insights. == Major components == According to "Computational thinking in the era of data science": Data thinking involves understanding that solutions require both data-driven and domain-knowledge-driven rules. Data thinking evaluates whether data accurately represents real-life scenarios and improves data collection where necessary. The framework highlights the importance of preserving domain-specific meaning during data analysis. Data thinking incorporates statistical and logical analysis to identify patterns and irregularities. Data thinking involves testing solutions in real-life contexts and iteratively improving models based on new data. The process requires evaluating problems from multiple abstraction levels and understanding the potential for biases in generalizations. == Major phases == === Strategic context and risk analysis === Analyzing the broader digital strategy and assessing risks and opportunities is a common step before beginning a project. Techniques like coolhunting, trend analysis, and scenario planning can be used to assist with this. === Ideation and exploration === In this phase, focus areas are identified, and use cases are developed by integrating organizational goals, user needs, and data requirements. Design thinking methods, such as personas and customer journey mapping, are applied. === Prototyping === A proof of concept is created to test feasibility and refine solutions through iterative evaluation to optimize for effective performance. === Implementation and monitoring === Solutions are tested and monitored for performance and continual improvement. == Implementing Data Thinking == The following resources explain more about data thinking and its applications: "Data Thinking: Framework for data-based solutions" by StackFuel "What is Data Thinking? A modern approach to designing a data strategy" by Mantel Group "Data Science Thinking" by SpringerLink These sources provide detailed insights into the methodology, phases, and benefits of adopting Data Thinking in organizational processes.

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  • Signals intelligence

    Signals intelligence

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

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  • MDS matrix

    MDS matrix

    An MDS matrix (maximum distance separable) is a matrix representing a function with certain diffusion properties that have useful applications in cryptography. Technically, an m × n {\displaystyle m\times n} matrix A {\displaystyle A} over a finite field K {\displaystyle K} is an MDS matrix if it is the transformation matrix of a linear transformation f ( x ) = A x {\displaystyle f(x)=Ax} from K n {\displaystyle K^{n}} to K m {\displaystyle K^{m}} such that no two different ( m + n ) {\displaystyle (m+n)} -tuples of the form ( x , f ( x ) ) {\displaystyle (x,f(x))} coincide in n {\displaystyle n} or more components. Equivalently, the set of all ( m + n ) {\displaystyle (m+n)} -tuples ( x , f ( x ) ) {\displaystyle (x,f(x))} is an MDS code, i.e., a linear code that reaches the Singleton bound. Let A ~ = ( I n A ) {\displaystyle {\tilde {A}}={\begin{pmatrix}\mathrm {I} _{n}\\\hline \mathrm {A} \end{pmatrix}}} be the matrix obtained by joining the identity matrix I n {\displaystyle \mathrm {I} _{n}} to A {\displaystyle A} . Then a necessary and sufficient condition for a matrix A {\displaystyle A} to be MDS is that every possible n × n {\displaystyle n\times n} submatrix obtained by removing m {\displaystyle m} rows from A ~ {\displaystyle {\tilde {A}}} is non-singular. This is also equivalent to the following: all the sub-determinants of the matrix A {\displaystyle A} are non-zero. Then a binary matrix A {\displaystyle A} (namely over the field with two elements) is never MDS unless it has only one row or only one column with all components 1 {\displaystyle 1} . Reed–Solomon codes have the MDS property and are frequently used to obtain the MDS matrices used in cryptographic algorithms. Serge Vaudenay suggested using MDS matrices in cryptographic primitives to produce what he called multipermutations, not-necessarily linear functions with this same property. These functions have what he called perfect diffusion: changing t {\displaystyle t} of the inputs changes at least m − t + 1 {\displaystyle m-t+1} of the outputs. He showed how to exploit imperfect diffusion to cryptanalyze functions that are not multipermutations. MDS matrices are used for diffusion in such block ciphers as AES, SHARK, Square, Twofish, Anubis, KHAZAD, Manta, Hierocrypt, Kalyna, Camellia and HADESMiMC, and in the stream cipher MUGI and the cryptographic hash function Whirlpool, Poseidon.

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  • Visual servoing

    Visual servoing

    Visual servoing, also known as vision-based robot control and abbreviated VS, is a technique which uses feedback information extracted from a vision sensor (visual feedback) to control the motion of a robot. One of the earliest papers that talks about visual servoing was from the SRI International Labs in 1979. == Visual servoing taxonomy == There are two fundamental configurations of the robot end-effector (hand) and the camera: Eye-in-hand, or end-point open-loop control, where the camera is attached to the moving hand and observing the relative position of the target. Eye-to-hand, or end-point closed-loop control, where the camera is fixed in the world and observing the target and the motion of the hand. Visual Servoing control techniques are broadly classified into the following types: Image-based (IBVS) Position/pose-based (PBVS) Hybrid approach IBVS was proposed by Weiss and Sanderson. The control law is based on the error between current and desired features on the image plane, and does not involve any estimate of the pose of the target. The features may be the coordinates of visual features, lines or moments of regions. IBVS has difficulties with motions very large rotations, which has come to be called camera retreat. PBVS is a model-based technique (with a single camera). This is because the pose of the object of interest is estimated with respect to the camera and then a command is issued to the robot controller, which in turn controls the robot. In this case the image features are extracted as well, but are additionally used to estimate 3D information (pose of the object in Cartesian space), hence it is servoing in 3D. Hybrid approaches use some combination of the 2D and 3D servoing. There have been a few different approaches to hybrid servoing 2-1/2-D Servoing Motion partition-based Partitioned DOF Based == Survey == The following description of the prior work is divided into 3 parts Survey of existing visual servoing methods. Various features used and their impacts on visual servoing. Error and stability analysis of visual servoing schemes. === Survey of existing visual servoing methods === Visual servo systems, also called servoing, have been around since the early 1980s , although the term visual servo itself was only coined in 1987. Visual Servoing is, in essence, a method for robot control where the sensor used is a camera (visual sensor). Servoing consists primarily of two techniques, one involves using information from the image to directly control the degrees of freedom (DOF) of the robot, thus referred to as Image Based Visual Servoing (IBVS). While the other involves the geometric interpretation of the information extracted from the camera, such as estimating the pose of the target and parameters of the camera (assuming some basic model of the target is known). Other servoing classifications exist based on the variations in each component of a servoing system , e.g. the location of the camera, the two kinds are eye-in-hand and hand–eye configurations. Based on the control loop, the two kinds are end-point-open-loop and end-point-closed-loop. Based on whether the control is applied to the joints (or DOF) directly or as a position command to a robot controller the two types are direct servoing and dynamic look-and-move. Being one of the earliest works the authors proposed a hierarchical visual servo scheme applied to image-based servoing. The technique relies on the assumption that a good set of features can be extracted from the object of interest (e.g. edges, corners and centroids) and used as a partial model along with global models of the scene and robot. The control strategy is applied to a simulation of a two and three DOF robot arm. Feddema et al. introduced the idea of generating task trajectory with respect to the feature velocity. This is to ensure that the sensors are not rendered ineffective (stopping the feedback) for any the robot motions. The authors assume that the objects are known a priori (e.g. CAD model) and all the features can be extracted from the object. The work by Espiau et al. discusses some of the basic questions in visual servoing. The discussions concentrate on modeling of the interaction matrix, camera, visual features (points, lines, etc..). In an adaptive servoing system was proposed with a look-and-move servoing architecture. The method used optical flow along with SSD to provide a confidence metric and a stochastic controller with Kalman filtering for the control scheme. The system assumes (in the examples) that the plane of the camera and the plane of the features are parallel., discusses an approach of velocity control using the Jacobian relationship s˙ = Jv˙ . In addition the author uses Kalman filtering, assuming that the extracted position of the target have inherent errors (sensor errors). A model of the target velocity is developed and used as a feed-forward input in the control loop. Also, mentions the importance of looking into kinematic discrepancy, dynamic effects, repeatability, settling time oscillations and lag in response. Corke poses a set of very critical questions on visual servoing and tries to elaborate on their implications. The paper primarily focuses the dynamics of visual servoing. The author tries to address problems like lag and stability, while also talking about feed-forward paths in the control loop. The paper also, tries to seek justification for trajectory generation, methodology of axis control and development of performance metrics. Chaumette in provides good insight into the two major problems with IBVS. One, servoing to a local minima and second, reaching a Jacobian singularity. The author show that image points alone do not make good features due to the occurrence of singularities. The paper continues, by discussing the possible additional checks to prevent singularities namely, condition numbers of J_s and Jˆ+_s, to check the null space of ˆ J_s and J^T_s . One main point that the author highlights is the relation between local minima and unrealizable image feature motions. Over the years many hybrid techniques have been developed. These involve computing partial/complete pose from Epipolar Geometry using multiple views or multiple cameras. The values are obtained by direct estimation or through a learning or a statistical scheme. While others have used a switching approach that changes between image-based and position-based on a Lyapnov function. The early hybrid techniques that used a combination of image-based and pose-based (2D and 3D information) approaches for servoing required either a full or partial model of the object in order to extract the pose information and used a variety of techniques to extract the motion information from the image. used an affine motion model from the image motion in addition to a rough polyhedral CAD model to extract the object pose with respect to the camera to be able to servo onto the object (on the lines of PBVS). 2-1/2-D visual servoing developed by Malis et al. is a well known technique that breaks down the information required for servoing into an organized fashion which decouples rotations and translations. The papers assume that the desired pose is known a priori. The rotational information is obtained from partial pose estimation, a homography, (essentially 3D information) giving an axis of rotation and the angle (by computing the eigenvalues and eigenvectors of the homography). The translational information is obtained from the image directly by tracking a set of feature points. The only conditions being that the feature points being tracked never leave the field of view and that a depth estimate be predetermined by some off-line technique. 2-1/2-D servoing has been shown to be more stable than the techniques that preceded it. Another interesting observation with this formulation is that the authors claim that the visual Jacobian will have no singularities during the motions. The hybrid technique developed by Corke and Hutchinson, popularly called portioned approach partitions the visual (or image) Jacobian into motions (both rotations and translations) relating X and Y axes and motions related to the Z axis. outlines the technique, to break out columns of the visual Jacobian that correspond to the Z axis translation and rotation (namely, the third and sixth columns). The partitioned approach is shown to handle the Chaumette Conundrum discussed in. This technique requires a good depth estimate in order to function properly. outlines a hybrid approach where the servoing task is split into two, namely main and secondary. The main task is keep the features of interest within the field of view. While the secondary task is to mark a fixation point and use it as a reference to bring the camera to the desired pose. The technique does need a depth estimate from an off-line procedure. The paper discusses two examples for which depth estimates are obtained from robot odometry and by assuming that all

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  • BitFunnel

    BitFunnel

    BitFunnel is the search engine indexing algorithm and a set of components used in the Bing search engine, which were made open source in 2016. BitFunnel uses bit-sliced signatures instead of an inverted index in an attempt to reduce operations cost. == History == Progress on the implementation of BitFunnel was made public in early 2016, with the expectation that there would be a usable implementation later that year. In September 2016, the source code was made available via GitHub. A paper discussing the BitFunnel algorithm and implementation was released as through the Special Interest Group on Information Retrieval of the Association for Computing Machinery in 2017 and won the Best Paper Award. == Components == BitFunnel consists of three major components: BitFunnel – the text search/retrieval system itself WorkBench – a tool for preparing text for use in BitFunnel NativeJIT – a software component that takes expressions that use C data structures and transforms them into highly optimized assembly code == Algorithm == === Initial problem and solution overview === The BitFunnel paper describes the "matching problem", which occurs when an algorithm must identify documents through the usage of keywords. The goal of the problem is to identify a set of matches given a corpus to search and a query of keyword terms to match against. This problem is commonly solved through inverted indexes, where each searchable item is maintained with a map of keywords. In contrast, BitFunnel represents each searchable item through a signature. A signature is a sequence of bits which describe a Bloom filter of the searchable terms in a given searchable item. The bloom filter is constructed through hashing through several bit positions. === Theoretical implementation of bit-string signatures === The signature of a document (D) can be described as the logical-or of its term signatures: S D → = ⋃ t ∈ D S t → {\displaystyle {\overrightarrow {S_{D}}}=\bigcup _{t\in D}{\overrightarrow {S_{t}}}} Similarly, a query for a document (Q) can be defined as a union: S Q → = ⋃ t ∈ Q S t → {\displaystyle {\overrightarrow {S_{Q}}}=\bigcup _{t\in Q}{\overrightarrow {S_{t}}}} Additionally, a document D is a member of the set M' when the following condition is satisfied: S Q → ∩ S D → = S Q → {\displaystyle {\overrightarrow {S_{Q}}}\cap {\overrightarrow {S_{D}}}={\overrightarrow {S_{Q}}}} This knowledge is then combined to produce a formula where M' is identified by documents which match the query signature: M ′ = { D ∈ C ∣ S Q → ∩ S D → = S Q → } {\displaystyle M'=\left\{D\in C\mid {\overrightarrow {S_{Q}}}\cap {\overrightarrow {S_{D}}}={\overrightarrow {S_{Q}}}\right\}} These steps and their proofs are discussed in the 2017 paper. === Pseudocode for bit-string signatures === This algorithm is described in the 2017 paper. M ′ = ∅ foreach D ∈ C do if S D → ∩ S Q → = S Q → then M ′ = M ′ ∪ { D } endif endfor {\displaystyle {\begin{array}{l}M'=\emptyset \\{\texttt {foreach}}\ D\in C\ {\texttt {do}}\\\qquad {\texttt {if}}\ {\overrightarrow {S_{D}}}\cap {\overrightarrow {S_{Q}}}={\overrightarrow {S_{Q}}}\ {\texttt {then}}\\\qquad \qquad M'=M'\cup \{D\}\\\qquad {\texttt {endif}}\\{\texttt {endfor}}\end{array}}}

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  • PGP word list

    PGP word list

    The PGP Word List ("Pretty Good Privacy word list", also called a biometric word list for reasons explained below) is a list of words for conveying data bytes in a clear unambiguous way via a voice channel. They are analogous in purpose to the NATO phonetic alphabet, except that a longer list of words is used, each word corresponding to one of the 256 distinct numeric byte values. == History and structure == The PGP Word List was designed in 1995 by Patrick Juola, a computational linguist, and Philip Zimmermann, creator of PGP. The words were carefully chosen for their phonetic distinctiveness, using genetic algorithms to select lists of words that had optimum separations in phoneme space. The candidate word lists were randomly drawn from Grady Ward's Moby Pronunciator list as raw material for the search, successively refined by the genetic algorithms. The automated search converged to an optimized solution in about 40 hours on a DEC Alpha, a particularly fast machine in that era. The Zimmermann–Juola list was originally designed to be used in PGPfone, a secure VoIP application, to allow the two parties to verbally compare a short authentication string to detect a man-in-the-middle attack (MiTM). It was called a biometric word list because the authentication depended on the two human users recognizing each other's distinct voices as they read and compared the words over the voice channel, binding the identity of the speaker with the words, which helped protect against the MiTM attack. The list can be used in many other situations where a biometric binding of identity is not needed, so calling it a biometric word list may be imprecise. Later, it was used in PGP to compare and verify PGP public key fingerprints over a voice channel. This is known in PGP applications as the "biometric" representation. When it was applied to PGP, the list of words was further refined, with contributions by Jon Callas. More recently, it has been used in Zfone and the ZRTP protocol, the successor to PGPfone. The list is actually composed of two lists, each containing 256 phonetically distinct words, in which each word represents a different byte value between 0 and 255. Two lists are used because reading aloud long random sequences of human words usually risks three kinds of errors: 1) transposition of two consecutive words, 2) duplicate words, or 3) omitted words. To detect all three kinds of errors, the two lists are used alternately for the even-offset bytes and the odd-offset bytes in the byte sequence. Each byte value is actually represented by two different words, depending on whether that byte appears at an odd or an even offset from the beginning of the byte sequence. The two lists are readily distinguished by the number of syllables; the odd list has words of three syllables, the even list has two. The two lists have a maximum word length of 11 and 9 letters, respectively. Using a two-list scheme was suggested by Zhahai Stewart. == Examples == Each byte in a bytestring is encoded as a single word. A sequence of bytes is rendered in network byte order, from left to right. For example, the leftmost (i.e. byte 0) is considered "even" and is encoded using the PGP Even Word table. The next byte to the right (i.e. byte 1) is considered "odd" and is encoded using the PGP Odd Word table. This process repeats until all bytes are encoded. Thus, "E582" produces "topmost Istanbul", whereas "82E5" produces "miser travesty". A PGP public key fingerprint that displayed in hexadecimal as E582 94F2 E9A2 2748 6E8B 061B 31CC 528F D7FA 3F19 would display in PGP Words (the "biometric" fingerprint) as topmost Istanbul Pluto vagabond treadmill Pacific brackish dictator goldfish Medusa afflict bravado chatter revolver Dupont midsummer stopwatch whimsical cowbell bottomless The order of bytes in a bytestring depends on endianness. == Other word lists for data == There are several other word lists for conveying data in a clear unambiguous way via a voice channel: the NATO phonetic alphabet maps individual letters and digits to individual words the S/KEY system maps 64 bit numbers to 6 short words of 1 to 4 characters each from a publicly accessible 2048-word dictionary. The same dictionary is used in RFC 1760 and RFC 2289. the Diceware system maps five base-6 random digits (almost 13 bits of entropy) to a word from a dictionary of 7,776 distinct words. the Electronic Frontier Foundation has published a set of improved word lists based on the same concept FIPS 181: Automated Password Generator converts random numbers into somewhat pronounceable "words". mnemonic encoding converts 32 bits of data into 3 words from a vocabulary of 1626 words. what3words encodes geographic coordinates in 3 dictionary words. the BIP39 standard permits encoding a cryptographic key of fixed size (128 or 256 bits, usually the unencrypted master key of a Cryptocurrency wallet) into a short sequence of readable words known as the seed phrase, for the purpose of storing the key offline. This is used in cryptocurrencies such as Bitcoin or Monero. Like the PGP word list, the Bytewords standard maps each possible byte to a word. There is only one list, rather than two. The words are uniformly four letters long and can be uniquely identified by their first and last letters

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