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Resisting AI

Resisting AI: An Anti-fascist Approach to Artificial Intelligence is a book on artificial intelligence (AI) by Dan McQuillan, published in 2022 by Bristol University Press. == Content == Resisting AI takes the form of an extended essay, which contrasts optimistic visions about AI's potential by arguing that AI may best be seen as a continuation and reinforcement of bureaucratic forms of discrimination and violence, ultimately fostering authoritarian outcomes. For McQuillan, AI's promise of objective calculability is antithetical to an egalitarian and just society. McQuillan uses the expression "AI violence" to describe how – based on opaque algorithms – various actors can discriminate against categories of people in accessing jobs, loans, medical care, and other benefits. The book suggests that AI has a political resonance with soft eugenic approaches to the valuation of life by modern welfare states, and that AI exhibits eugenic features in its underlying logic, as well as in its technical operations. The parallel is with historical eugenicists achieving saving to the state by sterilizing defectives so the state would not have to care for their offspring. The analysis of McQuillan goes beyond the known critique of AI systems fostering precarious labour markets, addressing "necropolitics", the politics of who is entitled to live, and who to die. Although McQuillan offers a brief history of machine learning at the beginning of the book – with its need for "hidden and undercompensated labour", he is concerned more with the social impacts of AI rather than with its technical aspects. McQuillan sees AI as the continuation of existing bureaucratic systems that already marginalize vulnerable groups – aggravated by the fact that AI systems trained on existing data are likely to reinforce existing discriminations, e.g. in attempting to optimize welfare distribution based on existing data patterns, ultimately creating a system of "self-reinforcing social profiling". In elaborating on the continuation between existing bureaucratic violence and AI, McQuillan connects to Hannah Arendt's concept of the thoughtless bureaucrat in Eichmann in Jerusalem: A Report on the Banality of Evil, which now becomes the algorithm that, lacking intent, cannot be accountable, and is thus endowed with an "algorithmic thoughtlessness". McQuillan defends the "fascist" in the title of the work by arguing that while not all AI is fascist, this emerging technology of control may end up being deployed by fascist or authoritarian regimes. For McQuillan, AI can support the diffusion of states of exception, as a technology impossible to properly regulate and a mechanism for multiplying exceptions more widely. An example of a scenario where AI systems of surveillance could bring discrimination to a new high is the initiative to create LGBT-free zones in Poland. Skeptical of ethical regulations to control the technology, McQuillan suggests people's councils and workers' councils, and other forms of citizens' agency to resist AI. A chapter titled "Post-Machine Learning" makes an appeal for resistance via currents of thought from feminist science (standpoint theory), post-normal science (extended peer communities), and new materialism; McQuillan encourages the reader to question the meaning of "objectivity" and calls for the necessity of alternative ways of knowing. Among the virtuous examples of resistance – possibly to be adopted by the AI workers themselves – McQuillan notes the Lucas Plan of the workers of Lucas Aerospace Corporation, in which a workforce declared redundant took control, reorienting the enterprise toward useful products. McQuillan advocates for what he calls decomputing, an opposition to the sweeping application and expansion of artificial intelligence. Similar to degrowth, the approach criticizes AI as an outgrowth of the systemic issues within capitalist systems. McQuillan argues that a different future is possible, in which distance between people is reduced rather than increased through AI intermediaries. The work of McQuillan warns against "watered-down forms of engagement" with AI, such as citizen juries, which superficially look like democratic deliberation but may actually obscure important decisions about AI that are outside the purview of the engagement situation (McQuillan 2022, 128). In an interview about the book, McQuillan describes himself as an "AI abolitionist". == Reception == The book has been praised for how it "masterfully disassembles AI as an epistemological, social, and political paradigm". On the critical side, a review in the academic journal Justice, Power and Resistance took exception to the "nightmarish visions of Big Brother" offered by McQuillan, and argued that while many elements of AI may pose concern, a critique should not be based on a caricature of what AI is, concluding that McQuillan's work is "less of a theory and more of a Manifesto". Another review notes "a disconnect between the technical aspects of AI and the socio-political analysis McQuillan provides." Although the book was published before the ChatGPT and large language model debate heated up, the book has not lost relevance to the AI discussion. It is noted for suggesting a link between beliefs in artificial intelligence and beliefs in a racialised and gendered visions of intelligence overall, whereby a certain type of rational, measurable intelligence is privileged, leading to "historical notions of hierarchies of being". The blog Reboot praised McQuillan for offering a theory of harm of AI (why AI could end up hurting people and society) that does not just encourage tackling in isolation specific predicted problems with AI-centric systems: bias, non-inclusiveness, exploitativeness, environmental destructiveness, opacity, and non-contestability. For educational policies could also look at AI following the reading of McQuillan: In his book Resisting AI, Dan McQuillan argues that "When we're thinking about the actuality of AI, we can't separate the calculations in the code from the social context of its application" .... McQuillan's particular concern is how many contemporary applications of AI are amplifying existing inequalities and injustices as well as deepening social divisions and instabilities. His book makes a powerful case for anticipating these effects and actively resisting them for the good of societies. Videos and podcasts with an interest in AI and emerging technology have discussed the book.
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Malleability (cryptography)

Malleability is a property of some cryptographic algorithms. An encryption algorithm is said to be malleable if it is possible to transform a ciphertext into another ciphertext which decrypts to a related plaintext. That is, given an encryption of a plaintext m {\displaystyle m} , it is possible to generate another ciphertext which decrypts to f ( m ) {\displaystyle f(m)} , for a known function f {\displaystyle f} , without necessarily knowing or learning m {\displaystyle m} . Malleability is often an undesirable property in a general-purpose cryptosystem, since it allows an attacker to modify the contents of a message. For example, suppose that a bank uses a stream cipher to hide its financial information, and a user sends an encrypted message containing, say, "TRANSFER $0000100.00 TO ACCOUNT #199." If an attacker can modify the message on the wire, and can guess the format of the unencrypted message, the attacker could change the amount of the transaction, or the recipient of the funds, e.g. "TRANSFER $0100000.00 TO ACCOUNT #227". Malleability does not refer to the attacker's ability to read the encrypted message. Both before and after tampering, the attacker cannot read the encrypted message. On the other hand, some cryptosystems are malleable by design. In other words, in some circumstances it may be viewed as a feature that anyone can transform an encryption of m {\displaystyle m} into a valid encryption of f ( m ) {\displaystyle f(m)} (for some restricted class of functions f {\displaystyle f} ) without necessarily learning m {\displaystyle m} . Such schemes are known as homomorphic encryption schemes. A cryptosystem may be semantically secure against chosen-plaintext attacks or even non-adaptive chosen-ciphertext attacks (CCA1) while still being malleable. However, security against adaptive chosen-ciphertext attacks (CCA2) is equivalent to non-malleability. == Example malleable cryptosystems == In a stream cipher, the ciphertext is produced by taking the exclusive or of the plaintext and a pseudorandom stream based on a secret key k {\displaystyle k} , as E ( m ) = m ⊕ S ( k ) {\displaystyle E(m)=m\oplus S(k)} . An adversary can construct an encryption of m ⊕ t {\displaystyle m\oplus t} for any t {\displaystyle t} , as E ( m ) ⊕ t = m ⊕ t ⊕ S ( k ) = E ( m ⊕ t ) {\displaystyle E(m)\oplus t=m\oplus t\oplus S(k)=E(m\oplus t)} . In the RSA cryptosystem, a plaintext m {\displaystyle m} is encrypted as E ( m ) = m e mod n {\displaystyle E(m)=m^{e}{\bmod {n}}} , where ( e , n ) {\displaystyle (e,n)} is the public key. Given such a ciphertext, an adversary can construct an encryption of m t {\displaystyle mt} for any t {\displaystyle t} , as E ( m ) ⋅ t e mod n = ( m t ) e mod n = E ( m t ) {\textstyle E(m)\cdot t^{e}{\bmod {n}}=(mt)^{e}{\bmod {n}}=E(mt)} . For this reason, RSA is commonly used together with padding methods such as OAEP or PKCS1. In the ElGamal cryptosystem, a plaintext m {\displaystyle m} is encrypted as E ( m ) = ( g b , m A b ) {\displaystyle E(m)=(g^{b},mA^{b})} , where ( g , A ) {\displaystyle (g,A)} is the public key. Given such a ciphertext ( c 1 , c 2 ) {\displaystyle (c_{1},c_{2})} , an adversary can compute ( c 1 , t ⋅ c 2 ) {\displaystyle (c_{1},t\cdot c_{2})} , which is a valid encryption of t m {\displaystyle tm} , for any t {\displaystyle t} . In contrast, the Cramer-Shoup system (which is based on ElGamal) is not malleable. In the Paillier, ElGamal, and RSA cryptosystems, it is also possible to combine several ciphertexts together in a useful way to produce a related ciphertext. In Paillier, given only the public key and an encryption of m 1 {\displaystyle m_{1}} and m 2 {\displaystyle m_{2}} , one can compute a valid encryption of their sum m 1 + m 2 {\displaystyle m_{1}+m_{2}} . In ElGamal and in RSA, one can combine encryptions of m 1 {\displaystyle m_{1}} and m 2 {\displaystyle m_{2}} to obtain a valid encryption of their product m 1 m 2 {\displaystyle m_{1}m_{2}} . Block ciphers in the cipher block chaining mode of operation, for example, are partly malleable: flipping a bit in a ciphertext block will completely mangle the plaintext it decrypts to, but will result in the same bit being flipped in the plaintext of the next block. This allows an attacker to 'sacrifice' one block of plaintext in order to change some data in the next one, possibly managing to maliciously alter the message. This is essentially the core idea of the padding oracle attack on CBC, which allows the attacker to decrypt almost an entire ciphertext without knowing the key. For this and many other reasons, a message authentication code is required to guard against any method of tampering. == Complete non-malleability == Fischlin, in 2005, defined the notion of complete non-malleability as the ability of the system to remain non-malleable while giving the adversary additional power to choose a new public key which could be a function of the original public key. In other words, the adversary shouldn't be able to come up with a ciphertext whose underlying plaintext is related to the original message through a relation that also takes public keys into account.
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Air Force Network

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

Squeaky Dolphin is a program developed by the Government Communications Headquarters (GCHQ), a British intelligence and security organization, to collect and analyze data from social media networks. The program was first revealed to the general public on NBC on 27 January 2014 based on documents previously leaked by Edward Snowden. == Scope of surveillance == According to a document of the GCHQ dated August 2012, the program enables broad, real-time surveillance of the following items: YouTube video views The Like button on Facebook. Facebook has since then encrypted the data. Blogspot/Blogger visits Twitter, which has however encrypted its communications since this presentation was made The program can be supplemented with commercially available analytic software to determine which videos are popular among residents of specific cities. The dashboard software chosen was made by Splunk. The presentation, which was originally shown to an NSA audience and was made public by the NBC, contains a note saying the program was "Not interested in individuals just broad trends!". However, "according to other Snowden documents" obtained by NBC, in 2010, "GCHQ exploited unencrypted data from Twitter to identify specific users around the world and target them with propaganda."
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Error level analysis

Error level analysis (ELA) is the analysis of compression artifacts in digital data with lossy compression such as JPEG. == Principles == When used, lossy compression is normally applied uniformly to a set of data, such as an image, resulting in a uniform level of compression artifacts. Alternatively, the data may consist of parts with different levels of compression artifacts. This difference may arise from the different parts having been repeatedly subjected to the same lossy compression a different number of times, or the different parts having been subjected to different kinds of lossy compression. A difference in the level of compression artifacts in different parts of the data may therefore indicate that the data has been edited. In the case of JPEG, even a composite with parts subjected to matching compressions will have a difference in the compression artifacts. In order to make the typically faint compression artifacts more readily visible, the data to be analyzed is subjected to an additional round of lossy compression, this time at a known, uniform level, and the result is subtracted from the original data under investigation. The resulting difference image is then inspected manually for any variation in the level of compression artifacts. In 2007, N. Krawetz denoted this method "error level analysis". Additionally, digital data formats such as JPEG sometimes include metadata describing the specific lossy compression used. If in such data the observed compression artifacts differ from those expected from the given metadata description, then the metadata may not describe the actual compressed data, and thus indicate that the data have been edited. == Limitations == By its nature, data without lossy compression, such as a PNG image, cannot be subjected to error level analysis. Consequently, since editing could have been performed on data without lossy compression with lossy compression applied uniformly to the edited, composite data, the presence of a uniform level of compression artifacts does not rule out editing of the data. Additionally, any non-uniform compression artifacts in a composite may be removed by subjecting the composite to repeated, uniform lossy compression. Also, if the image color space is reduced to 256 colors or less, for example, by conversion to GIF, then error level analysis will generate useless results. More significant, the actual interpretation of the level of compression artifacts in a given segment of the data is subjective, and the determination of whether editing has occurred is therefore not robust. == Controversy == In May 2013, Dr Neal Krawetz used error level analysis on the 2012 World Press Photo of the Year and concluded on his Hacker Factor blog that it was "a composite" with modifications that "fail to adhere to the acceptable journalism standards used by Reuters, Associated Press, Getty Images, National Press Photographer's Association, and other media outlets". The World Press Photo organizers responded by letting two independent experts analyze the image files of the winning photographer and subsequently confirmed the integrity of the files. One of the experts, Hany Farid, said about error level analysis that "It incorrectly labels altered images as original and incorrectly labels original images as altered with the same likelihood". Krawetz responded by clarifying that "It is up to the user to interpret the results. Any errors in identification rest solely on the viewer". In May 2015, the citizen journalism team Bellingcat wrote that error level analysis revealed that the Russian Ministry of Defense had edited satellite images related to the Malaysia Airlines Flight 17 disaster. In a reaction to this, image forensics expert Jens Kriese said about error level analysis: "The method is subjective and not based entirely on science", and that it is "a method used by hobbyists". On his Hacker Factor Blog, the inventor of error level analysis Neal Krawetz criticized both Bellingcat's use of error level analysis as "misinterpreting the results" but also on several points Jens Kriese's "ignorance" regarding error level analysis.
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Factorization of polynomials over finite fields

In mathematics and computer algebra the factorization of a polynomial consists of decomposing it into a product of irreducible factors. This decomposition is theoretically possible and is unique for polynomials with coefficients in any field, but rather strong restrictions on the field of the coefficients are needed to allow the computation of the factorization by means of an algorithm. In practice, algorithms have been designed only for polynomials with coefficients in a finite field, in the field of rationals or in a finitely generated field extension of one of them. All factorization algorithms, including the case of multivariate polynomials over the rational numbers, reduce the problem to this case; see polynomial factorization. It is also used for various applications of finite fields, such as coding theory (cyclic redundancy codes and BCH codes), cryptography (public key cryptography by the means of elliptic curves), and computational number theory. As the reduction of the factorization of multivariate polynomials to that of univariate polynomials does not have any specificity in the case of coefficients in a finite field, only polynomials with one variable are considered in this article. == Background == === Finite field === The theory of finite fields, whose origins can be traced back to the works of Gauss and Galois, has played a part in various branches of mathematics. Due to the applicability of the concept in other topics of mathematics and sciences like computer science there has been a resurgence of interest in finite fields and this is partly due to important applications in coding theory and cryptography. Applications of finite fields introduce some of these developments in cryptography, computer algebra and coding theory. A finite field or Galois field is a field with a finite order (number of elements). The order of a finite field is always a prime or a power of prime. For each prime power q = pr, there exists exactly one finite field with q elements, up to isomorphism. This field is denoted GF(q) or Fq. If p is prime, GF(p) is the prime field of order p; it is the field of residue classes modulo p, and its p elements are denoted 0, 1, ..., p−1. Thus a = b in GF(p) means the same as a ≡ b (mod p). === Irreducible polynomials === Let F be a finite field. As for general fields, a non-constant polynomial f in F[x] is said to be irreducible over F if it is not the product of two polynomials of positive degree. A polynomial of positive degree that is not irreducible over F is called reducible over F. Irreducible polynomials allow us to construct the finite fields of non-prime order. In fact, for a prime power q, let Fq be the finite field with q elements, unique up to isomorphism. A polynomial f of degree n greater than one, which is irreducible over Fq, defines a field extension of degree n which is isomorphic to the field with qn elements: the elements of this extension are the polynomials of degree lower than n; addition, subtraction and multiplication by an element of Fq are those of the polynomials; the product of two elements is the remainder of the division by f of their product as polynomials; the inverse of an element may be computed by the extended GCD algorithm (see Arithmetic of algebraic extensions). It follows that, to compute in a finite field of non prime order, one needs to generate an irreducible polynomial. For this, the common method is to take a polynomial at random and test it for irreducibility. For sake of efficiency of the multiplication in the field, it is usual to search for polynomials of the shape xn + ax + b. Irreducible polynomials over finite fields are also useful for pseudorandom number generators using feedback shift registers and discrete logarithm over F2n. The number of irreducible monic polynomials of degree n over Fq is the number of aperiodic necklaces, given by Moreau's necklace-counting function Mq(n). The closely related necklace function Nq(n) counts monic polynomials of degree n which are primary (a power of an irreducible); or alternatively irreducible polynomials of all degrees d which divide n. === Example === The polynomial P = x4 + 1 is irreducible over Q but not over any finite field. On any field extension of F2, P = (x + 1)4. On every other finite field, at least one of −1, 2 and −2 is a square, because the product of two non-squares is a square and so we have If − 1 = a 2 , {\displaystyle -1=a^{2},} then P = ( x 2 + a ) ( x 2 − a ) . {\displaystyle P=(x^{2}+a)(x^{2}-a).} If 2 = b 2 , {\displaystyle 2=b^{2},} then P = ( x 2 + b x + 1 ) ( x 2 − b x + 1 ) . {\displaystyle P=(x^{2}+bx+1)(x^{2}-bx+1).} If − 2 = c 2 , {\displaystyle -2=c^{2},} then P = ( x 2 + c x − 1 ) ( x 2 − c x − 1 ) . {\displaystyle P=(x^{2}+cx-1)(x^{2}-cx-1).} === Complexity === Polynomial factoring algorithms use basic polynomial operations such as products, divisions, gcd, powers of one polynomial modulo another, etc. A multiplication of two polynomials of degree at most n can be done in O(n2) operations in Fq using "classical" arithmetic, or in O(nlog(n) log(log(n)) ) operations in Fq using "fast" arithmetic. A Euclidean division (division with remainder) can be performed within the same time bounds. The cost of a polynomial greatest common divisor between two polynomials of degree at most n can be taken as O(n2) operations in Fq using classical methods, or as O(nlog2(n) log(log(n)) ) operations in Fq using fast methods. For polynomials h, g of degree at most n, the exponentiation hq mod g can be done with O(log(q)) polynomial products, using exponentiation by squaring method, that is O(n2log(q)) operations in Fq using classical methods, or O(nlog(q)log(n) log(log(n))) operations in Fq using fast methods. In the algorithms that follow, the complexities are expressed in terms of number of arithmetic operations in Fq, using classical algorithms for the arithmetic of polynomials. == Factoring algorithms == Many algorithms for factoring polynomials over finite fields include the following three stages: Square-free factorization Distinct-degree factorization Equal-degree factorization An important exception is Berlekamp's algorithm, which combines stages 2 and 3. === Berlekamp's algorithm === Berlekamp's algorithm is historically important as being the first factorization algorithm which works well in practice. However, it contains a loop on the elements of the ground field, which implies that it is practicable only over small finite fields. For a fixed ground field, its time complexity is polynomial, but, for general ground fields, the complexity is exponential in the size of the ground field. === Square-free factorization === The algorithm determines a square-free factorization for polynomials whose coefficients come from the finite field Fq of order q = pm with p a prime. This algorithm firstly determines the derivative and then computes the gcd of the polynomial and its derivative. If it is not one then the gcd is again divided into the original polynomial, provided that the derivative is not zero (a case that exists for non-constant polynomials defined over finite fields). This algorithm uses the fact that, if the derivative of a polynomial is zero, then it is a polynomial in xp, which is, if the coefficients belong to Fp, the pth power of the polynomial obtained by substituting x by x1/p. If the coefficients do not belong to Fp, the pth root of a polynomial with zero derivative is obtained by the same substitution on x, completed by applying the inverse of the Frobenius automorphism to the coefficients. This algorithm works also over a field of characteristic zero, with the only difference that it never enters in the blocks of instructions where pth roots are computed. However, in this case, Yun's algorithm is much more efficient because it computes the greatest common divisors of polynomials of lower degrees. A consequence is that, when factoring a polynomial over the integers, the algorithm which follows is not used: one first computes the square-free factorization over the integers, and to factor the resulting polynomials, one chooses a p such that they remain square-free modulo p. Algorithm: SFF (Square-Free Factorization) Input: A monic polynomial f in Fq[x] where q = pm Output: Square-free factorization of f R ← 1 # Make w be the product (without multiplicity) of all factors of f that have # multiplicity not divisible by p c ← gcd(f, f′) w ← f/c # Step 1: Identify all factors in w i ← 1 while w ≠ 1 do y ← gcd(w, c) fac ← w / y R ← R · faci w ← y; c ← c / y; i ← i + 1 end while # c is now the product (with multiplicity) of the remaining factors of f # Step 2: Identify all remaining factors using recursion # Note that these are the factors of f that have multiplicity divisible by p if c ≠ 1 then c ← c1/p R ← R·SFF(c)p end if Output(R) The idea is to identify the product of all irreducible factors of f with the same multiplicity. This is done in two steps. The first step uses the formal d
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Data profiling

Data profiling is the process of examining the data available from an existing information source (e.g. a database or a file) and collecting statistics or informative summaries about that data. The purpose of these statistics may be to: Find out whether existing data can be easily used for other purposes Improve the ability to search data by tagging it with keywords, descriptions, or assigning it to a category Assess data quality, including whether the data conforms to particular standards or patterns Assess the risk involved in integrating data in new applications, including the challenges of joins Discover metadata of the source database, including value patterns and distributions, key candidates, foreign-key candidates, and functional dependencies Assess whether known metadata accurately describes the actual values in the source database Understanding data challenges early in any data intensive project, so that late project surprises are avoided. Finding data problems late in the project can lead to delays and cost overruns. Have an enterprise view of all data, for uses such as master data management, where key data is needed, or data governance for improving data quality. == Introduction == Data profiling refers to the analysis of information for use in a data warehouse in order to clarify the structure, content, relationships, and derivation rules of the data. Profiling helps to not only understand anomalies and assess data quality, but also to discover, register, and assess enterprise metadata. The result of the analysis is used to determine the suitability of the candidate source systems, usually giving the basis for an early go/no-go decision, and also to identify problems for later solution design. == How data profiling is conducted == Data profiling utilizes methods of descriptive statistics such as minimum, maximum, mean, mode, percentile, standard deviation, frequency, variation, aggregates such as count and sum, and additional metadata information obtained during data profiling such as data type, length, discrete values, uniqueness, occurrence of null values, typical string patterns, and abstract type recognition. The metadata can then be used to discover problems such as illegal values, misspellings, missing values, varying value representation, and duplicates. Different analyses are performed for different structural levels. E.g. single columns could be profiled individually to get an understanding of frequency distribution of different values, type, and use of each column. Embedded value dependencies can be exposed in a cross-columns analysis. Finally, overlapping value sets possibly representing foreign key relationships between entities can be explored in an inter-table analysis. Normally, purpose-built tools are used for data profiling to ease the process. The computational complexity increases when going from single column, to single table, to cross-table structural profiling. Therefore, performance is an evaluation criterion for profiling tools. == When is data profiling conducted? == According to Kimball, data profiling is performed several times and with varying intensity throughout the data warehouse developing process. A light profiling assessment should be undertaken immediately after candidate source systems have been identified and DW/BI business requirements have been satisfied. The purpose of this initial analysis is to clarify at an early stage if the correct data is available at the appropriate detail level and that anomalies can be handled subsequently. If this is not the case the project may be terminated. Additionally, more in-depth profiling is done prior to the dimensional modeling process in order assess what is required to convert data into a dimensional model. Detailed profiling extends into the ETL system design process in order to determine the appropriate data to extract and which filters to apply to the data set. Additionally, data profiling may be conducted in the data warehouse development process after data has been loaded into staging, the data marts, etc. Conducting data at these stages helps ensure that data cleaning and transformations have been done correctly and in compliance of requirements. == Benefits and examples == Data profiling can improve data quality, shorten the implementation cycle of major projects, and improve users' understanding of data. Discovering business knowledge embedded in data itself is one of the significant benefits derived from data profiling. It can improve data accuracy in corporate databases.
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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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Olio (app)

Olio is a mobile app for sharing by giving away, getting, borrowing or lending things in your community for free, aiming to reduce household and food waste. It does this by connecting neighbours with spare food or household items to others nearby who wish to pick up those items. The food must be edible; it can be raw or cooked, sealed or open. Non-food items often listed on Olio include books, clothes and furniture. Those donating surplus food can be individuals or companies such as food retailers, restaurants, corporate canteens, food photographers etc., and donations can take place on an ad-hoc or recurrent basis. For example, some supermarket chains in the UK, including Tesco, the Midcounties Co-operative, Morrisons, Sainsbury's and Iceland have piloted Olio as an 'online food bank' to donate food and to reduce their waste. In March 2022, Olio partnered with Pandamart in Singapore. First launched in early 2015 by Tessa Clarke and Saasha Celestial-One, by October 2017 the company had raised $2.2 million in funding. Olio subsequently performed a series A funding round of $6 million in 2018 and a Series B of $43 million. Notable investors include Accel, Octopus Ventures and VNV Global. The Olio app had around 7 million registered users as of May 2023.
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Stegomalware

Stegomalware is a form of malicious software that leverages steganography techniques to conceal its code, configuration data, or command-and-control (C&C) communications within seemingly benign digital media such as images, audio files, videos, documents, or network traffic. It typically embeds encrypted or obfuscated payloads into digital media and only extracts and executes them at runtime, which makes traditional signature-based and sandbox-based detection significantly more difficult. Stegomalware has been observed in attacks ranging from advanced persistent threats (APTs) to financially motivated cybercrime, and is now the subject of dedicated academic surveys, research projects, and international law-enforcement initiatives. The key distinction between stegomalware and traditional obfuscated malware lies in the encoding location. After obfuscation, malicious code remains present within the executable and can theoretically be discovered through static analysis. In contrast, stegomalware hides the payload entirely within a cover medium (image, audio, etc.), remaining invisible until the malware dynamically extracts and executes it at runtime. == History == The term stegomalware was formally introduced by researchers Águila, Laskov, and others in the context of mobile malware and presented at the Inscrypt (Information Security and Cryptology) conference in 2014. This marked the first academic formalization of the concept, though earlier work had already identified that botnets and mobile malware could use steganography and covert channels for command-and-control communication over probabilistically unobservable channels. Since its introduction, stegomalware has evolved from a theoretical concern to a documented threat. In 2011, the APT operation known as "Operation Shady RAT" became one of the first documented cases of stegomalware in the wild, using digital images to hide Internet Protocol addresses and command-and-control server addresses. The same year, the Duqu malware (targeting industrial manufacturers) embedded victim data into JPEG image files before exfiltration, making the data transfer virtually undetectable to network-level security tools. From 2014 onwards, stegomalware became more prevalent in organized cybercrime and advanced persistent threat campaigns. Notable examples include Zeus/Zbot, which masked configuration data in images; Gatak/Stegoloader, which hid shellcode in PNG files; TeslaCrypt, which embedded C&C commands in JPEGs; and Cerber, which concealed ransomware payloads within images. By the 2010s, stegomalware had become established as a preferred evasion technique for espionage, financial theft, and ransomware distribution campaigns. Recent surveys (2020–2025) document that stegomalware has increasingly been exploited by adversaries targeting banks, enterprises, government agencies, educational institutions, and internet users via malvertising campaigns. The technique is now considered a sophisticated method of attack worthy of dedicated international law-enforcement attention. == Technical Characteristics and Definitions == Stegomalware operates through a three-component architecture: Stegotext (R): An innocent-looking digital asset (image, audio file, etc.) into which the malicious payload is embedded. Secret key (sk): A key used by the embedding and extraction algorithms, typically hardcoded into the malware. Payload (p): The actual malicious code, configuration data, or C&C commands hidden within the stegotext. The malware extracts the payload at runtime using the secret key and either executes it directly or uses it to download additional stages of the attack. Stegomalware can be classified into several types based on deployment method: Type 0 (Autonomous): Both the stegotext and extraction algorithm are embedded within the malware application itself. The malicious payload is extracted and executed locally without external communication. Type I (Update): The stegotext and secret key are downloaded from a remote server at runtime; only the extraction algorithm is included in the malware. This variant is more flexible, allowing attackers to push updated payloads. Type II (External Algorithm): Neither the stegotext nor the extraction algorithm are distributed with the malware; both are fetched from an attacker-controlled infrastructure, providing maximum flexibility and evasion. == Steganography techniques == === Spatial domain methods === Stegomalware predominantly uses steganographic methods designed for images, as images are the most common cover medium in the wild. The most basic spatial domain technique is Least Significant Bit (LSB) substitution, which replaces the least significant bits of pixel color values with payload bits. While simple and easy to implement, LSB is also relatively easy to detect through statistical analysis. More sophisticated spatial domain techniques include: HUGO (High Undetectable steGO) (2010): Minimizes detectable distortion by distributing the payload across multiple pixels, achieving embedding capacity with reduced statistical footprint. WOW (Wavelet Obtained Weights) (2012): Embeds data preferentially in textured regions of images where modifications are less perceptually noticeable. UNIWARD (Universal Wavelet Relative Distortion) (2014): Uses a universal distortion function applicable to multiple image formats, balancing payload capacity with undetectability. HILL (2014): Applies high-pass and low-pass filters to identify robust embedding regions. MiPOD (Minimizing the Power of Optimal Detector) (2016): Designed to minimize the power of theoretical optimal steganalysis detectors. === Transform domain methods === Transform domain techniques convert images into the frequency domain (e.g., using DCT or DWT) before embedding, allowing for more robust hiding in JPEG and other compressed formats: Embedding in DCT coefficients (used in JPEG compression) Embedding in DWT coefficients (used in lossless formats) Spread spectrum techniques, which distribute the payload across many frequency components Transform domain methods are generally more resistant to noise, compression, and image transformations than spatial methods. === Generative adversarial network (GAN) methods === Recent advances in machine learning have introduced GAN-based steganography, where a generative model produces stego images that minimize detectable artifacts: SGAN (Steganographic GAN) (2017): First GAN applied to steganography, using a generator, discriminator, and steganalysis network. ASDL-GAN (2017): Performs automatic steganographic distortion learning at the pixel level. SteganoGAN (2019): Improves upon earlier GAN models, achieving higher embedding capacity and robustness. HiGAN (Hiding Images GAN) (2020): Enables hiding one image within another while maintaining visual plausibility. GAN-based approaches are more resilient to standard steganalysis attacks but remain an emerging threat requiring further research. == Notable malware campaigns == Stegomalware has been documented in numerous high-profile cyber attacks and campaigns. Notable examples include: Operation Shady RAT (2011): Used digital images to hide command-and-control server addresses in targeted espionage. Duqu (2011): Embedded victim data into JPEG files to exfiltrate industrial control system information. Zeus/Zbot (2014): Masked banking configuration data inside JPEG files exploited via malvertising. Gatak/Stegoloader (2015): Hid shellcode in PNG files for software licensing attacks and bot command execution. TeslaCrypt (2015): Embedded C&C commands and ransomware keys in JPEG images. Cerber (2016): Concealed executable ransomware code in JPEG files distributed via phishing. DNSChanger (2016): Embedded malicious code in PNG files for DNS hijacking campaigns. Sundown Exploit Kit (2017): Distributed exploit code in PNG files via malvertising. AdGholas (2017): Used JPEG steganography to distribute ransomware via malvertising. Synccrypt (2017): Hidden ransomware components in JPEG-steganographic encrypted archives. ZeroT/PlugX (2017): Hid Remote Access Trojan payloads in BMP files for espionage. Loki Bot (2018): Concealed malware installers in JPEG and video files. Waterbug (APT28) (2019): Injected malicious DLLs into WAV audio files. Shlayer (macOS adware) (2019): Hid malicious URLs in JPEG files via malvertising. === Attack vectors === The most common attack vectors for stegomalware include: Phishing emails with malicious attachments or links Malvertising campaigns using malicious banner advertisements Exploit kits through compromised or malicious websites Legitimate application vulnerabilities (e.g., watering-hole attacks) Fake software distribution (cracked software, keygen tools) === Exploitation stages === Stegomalware typically serves one or more roles in attack lifecycles: Payload delivery: Stego images contain full executable code or shellcode. C&C communication: Hidden data contains server addresses or command instructio
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Voice inversion

Voice inversion scrambling is an analog method of obscuring the content of a transmission. It is sometimes used in public service radio, automobile racing, cordless telephones and the Family Radio Service. Without a descrambler, the transmission makes the speaker "sound like Donald Duck". Despite the term, the technique operates on the passband of the information and so can be applied to any information being transmitted. == Forms and details == There are various forms of voice inversion which offer differing levels of security. Overall, voice inversion scrambling offers little true security as software and even hobbyist kits are available from kit makers for scrambling and descrambling. The cadence of the speech is not changed. It is often easy to guess what is happening in the conversation by listening for other audio cues like questions, short responses and other language cadences. In the simplest form of voice inversion, the frequency p {\displaystyle p} of each component is replaced with s − p {\displaystyle s-p} , where s {\displaystyle s} is the frequency of a carrier wave. This can be done by amplitude modulating the speech signal with the carrier, then applying a low-pass filter to select the lower sideband. This will make the low tones of the voice sound like high ones and vice versa. This process also occurs naturally if a radio receiver is tuned to a single sideband transmission but set to decode the wrong sideband. There are more advanced forms of voice inversion which are more complex and require more effort to descramble. One method is to use a random code to choose the carrier frequency and then change this code in real time. This is called Rolling Code voice inversion and one can often hear the "ticks" in the transmission which signal the changing of the inversion point. Another method is split band voice inversion. This is where the band is split and then each band is inverted separately. A rolling code can also be added to this method for variable split band inversion (VSB). Common carrier frequencies are: 2.632 kHz, 2.718 kHz, 2.868 kHz, 3.023 kHz, 3.107 kHz, 3.196 kHz, 3.333 kHz, 3.339 kHz, 3.496 kHz, 3.729 kHz and 4.096 kHz. Voice inversion offers no security at all and software is available to restore the original voice, which is why it is no longer used to protect conversations today. However, voice inversion is still found in low-end Chinese walkie talkies.
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Contrast set learning

Contrast set learning is a form of association rule learning that seeks to identify meaningful differences between separate groups by reverse-engineering the key predictors that identify for each particular group. For example, given a set of attributes for a pool of students (labeled by degree type), a contrast set learner would identify the contrasting features between students seeking bachelor's degrees and those working toward PhD degrees. == Overview == A common practice in data mining is to classify, to look at the attributes of an object or situation and make a guess at what category the observed item belongs to. As new evidence is examined (typically by feeding a training set to a learning algorithm), these guesses are refined and improved. Contrast set learning works in the opposite direction. While classifiers read a collection of data and collect information that is used to place new data into a series of discrete categories, contrast set learning takes the category that an item belongs to and attempts to reverse engineer the statistical evidence that identifies an item as a member of a class. That is, contrast set learners seek rules associating attribute values with changes to the class distribution. They seek to identify the key predictors that contrast one classification from another. For example, an aerospace engineer might record data on test launches of a new rocket. Measurements would be taken at regular intervals throughout the launch, noting factors such as the trajectory of the rocket, operating temperatures, external pressures, and so on. If the rocket launch fails after a number of successful tests, the engineer could use contrast set learning to distinguish between the successful and failed tests. A contrast set learner will produce a set of association rules that, when applied, will indicate the key predictors of each failed tests versus the successful ones (the temperature was too high, the wind pressure was too high, etc.). Contrast set learning is a form of association rule learning. Association rule learners typically offer rules linking attributes commonly occurring together in a training set (for instance, people who are enrolled in four-year programs and take a full course load tend to also live near campus). Instead of finding rules that describe the current situation, contrast set learners seek rules that differ meaningfully in their distribution across groups (and thus, can be used as predictors for those groups). For example, a contrast set learner could ask, “What are the key identifiers of a person with a bachelor's degree or a person with a PhD, and how do people with PhD's and bachelor’s degrees differ?” Standard classifier algorithms, such as C4.5, have no concept of class importance (that is, they do not know if a class is "good" or "bad"). Such learners cannot bias or filter their predictions towards certain desired classes. As the goal of contrast set learning is to discover meaningful differences between groups, it is useful to be able to target the learned rules towards certain classifications. Several contrast set learners, such as MINWAL or the family of TAR algorithms, assign weights to each class in order to focus the learned theories toward outcomes that are of interest to a particular audience. Thus, contrast set learning can be thought of as a form of weighted class learning. === Example: Supermarket Purchases === The differences between standard classification, association rule learning, and contrast set learning can be illustrated with a simple supermarket metaphor. In the following small dataset, each row is a supermarket transaction and each "1" indicates that the item was purchased (a "0" indicates that the item was not purchased): Given this data, Association rule learning may discover that customers that buy onions and potatoes together are likely to also purchase hamburger meat. Classification may discover that customers that bought onions, potatoes, and hamburger meats were purchasing items for a cookout. Contrast set learning may discover that the major difference between customers shopping for a cookout and those shopping for an anniversary dinner are that customers acquiring items for a cookout purchase onions, potatoes, and hamburger meat (and do not purchase foie gras or champagne). == Treatment learning == Treatment learning is a form of weighted contrast-set learning that takes a single desirable group and contrasts it against the remaining undesirable groups (the level of desirability is represented by weighted classes). The resulting "treatment" suggests a set of rules that, when applied, will lead to the desired outcome. Treatment learning differs from standard contrast set learning through the following constraints: Rather than seeking the differences between all groups, treatment learning specifies a particular group to focus on, applies a weight to this desired grouping, and lumps the remaining groups into one "undesired" category. Treatment learning has a stated focus on minimal theories. In practice, treatment are limited to a maximum of four constraints (i.e., rather than stating all of the reasons that a rocket differs from a skateboard, a treatment learner will state one to four major differences that predict for rockets at a high level of statistical significance). This focus on simplicity is an important goal for treatment learners. Treatment learning seeks the smallest change that has the greatest impact on the class distribution. Conceptually, treatment learners explore all possible subsets of the range of values for all attributes. Such a search is often infeasible in practice, so treatment learning often focuses instead on quickly pruning and ignoring attribute ranges that, when applied, lead to a class distribution where the desired class is in the minority. === Example: Boston housing data === The following example demonstrates the output of the treatment learner TAR3 on a dataset of housing data from the city of Boston (a nontrivial public dataset with over 500 examples). In this dataset, a number of factors are collected for each house, and each house is classified according to its quality (low, medium-low, medium-high, and high). The desired class is set to "high", and all other classes are lumped together as undesirable. The output of the treatment learner is as follows: Baseline class distribution: low: 29% medlow: 29% medhigh: 21% high: 21% Suggested Treatment: [PTRATIO=[12.6..16), RM=[6.7..9.78)] New class distribution: low: 0% medlow: 0% medhigh: 3% high: 97% With no applied treatments (rules), the desired class represents only 21% of the class distribution. However, if one filters the data set for houses with 6.7 to 9.78 rooms and a neighborhood parent-teacher ratio of 12.6 to 16, then 97% of the remaining examples fall into the desired class (high-quality houses). == Algorithms == There are a number of algorithms that perform contrast set learning. The following subsections describe two examples. === STUCCO === The STUCCO contrast set learner treats the task of learning from contrast sets as a tree search problem where the root node of the tree is an empty contrast set. Children are added by specializing the set with additional items picked through a canonical ordering of attributes (to avoid visiting the same nodes twice). Children are formed by appending terms that follow all existing terms in a given ordering. The formed tree is searched in a breadth-first manner. Given the nodes at each level, the dataset is scanned and the support is counted for each group. Each node is then examined to determine if it is significant and large, if it should be pruned, and if new children should be generated. After all significant contrast sets are located, a post-processor selects a subset to show to the user - the low order, simpler results are shown first, followed by the higher order results which are "surprising and significantly different." The support calculation comes from testing a null hypothesis that the contrast set support is equal across all groups (i.e., that contrast set support is independent of group membership). The support count for each group is a frequency value that can be analyzed in a contingency table where each row represents the truth value of the contrast set and each column variable indicates the group membership frequency. If there is a difference in proportions between the contrast set frequencies and those of the null hypothesis, the algorithm must then determine if the differences in proportions represent a relation between variables or if it can be attributed to random causes. This can be determined through a chi-square test comparing the observed frequency count to the expected count. Nodes are pruned from the tree when all specializations of the node can never lead to a significant and large contrast set. The decision to prune is based on: The minimum deviation size: The maximum difference between the support
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Abiquo Enterprise Edition

Abiquo Hybrid Cloud Management Platform is a web-based cloud computing software platform developed by Abiquo. Written entirely in Java, it is used to build, integrate and manage public and private clouds in homogeneous environments. Users can deploy and manage servers, storage system and network and virtual devices. It also supports LDAP integration. == Hypervisors == Abiquo supports five hypervisor systems. VMware ESXi Microsoft Hyper-V Citrix XenServer Oracle VM Server for x86 KVM From version 3.1, it also supports multiple public cloud providers: Amazon AWS Rackspace Google Compute Engine HP Cloud ElasticHosts DigitalOcean Abiquo version 3.2 added: Microsoft Azure Abiquo version 3.4 added: Support for Docker hosts, adding multi-tenant networking, storage management and private registry management for Docker SoftLayer CloudSigma Later versions continued to add features including autoscaling on any cloud, integration to VMware NSX and OpenStack Neutron for software defined networking, guest config with cloud-init and integrated monitoring driving guest automation. == Storage services == Abiquo supports any vendor for hypervisor storage, and also supports tiered storage pools, enabling storage-as-a-service from specific vendors and technologies including: NFS Generic iSCSI NetApp Nexenta == SAAS version == In April 2014 Abiquo launched Abiquo anyCloud, a SAAS version of the Abiquo Hybrid Cloud Platform software. This version lets users manage public cloud resources from: Amazon AWS Microsoft Azure IBM SoftLayer DigitalOcean Rackspace Open Cloud (an OpenStack cloud) HP Public Cloud (an OpenStack cloud) Google Compute Engine ElasticHosts Additional security and process features include workflow, to have an enterprise administrator electronically sign off on changes, an audit trail of activity and the ability to share cloud accounts among and enterprise team in a secure way. == Reviews and awards == Finalist for the 2015 Cloud Awards Finalist for the 2015 UK Cloud Awards in the category Cloud Management Product of the Year EMA Radar for Private Cloud platforms 2013 Global Telecoms Business Innovation Summit and Awards 2013 (with Interoute) EuroCloud UK Awards
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Social media use in health awareness

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

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