AI For Business Owners

AI For Business Owners — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

  • Spanish Network of Excellence on Cybersecurity Research

    Spanish Network of Excellence on Cybersecurity Research

    The Spanish Network of Excellence on Cybersecurity Research (RENIC), is a research initiative to promote cybersecurity interests in Spain. == Members == === Board of Directors (2018) === President: Universidad de Málaga Vice president: CSIC Treasurer: Universidad Politécnica de Madrid Secretary: Universidad de Granada Vocals: Tecnalia, Universidad de La Laguna and Universidad de Modragón === Board of Directors (2016) === President: Universidad Carlos III de Madrid Vice president: Universidad Politécnica de Madrid Treasurer: Universidad de Granada Secretary: Universidad de León Vocals: Gradiant, Tecnalia, Universidad de Málaga === Founding Members === Centro Andaluz de Innovación y Tecnologías de la Información y las Comunicaciones (CITIC). Consejo Superior de Investigaciones Científicas (CSIC). Centro Tecnolóxico de Telecomunicaciones de Galicia (Gradiant). Instituto Imdea Software. Instituto Nacional de Ciberseguridad (INCIBE). Mondragón Unibertsitatea. Tecnalia. Universidad Carlos III de Madrid. Universidad Castilla la Mancha. Universidad de Granada. Universidad de la Laguna. Universidad de León. Universidad de Málaga. Universidad de Murcia. Universidad de Vigo. Universidad Internacional de la Rioja. Universidad Politécnica de Madrid. Universidad Rey Juan Carlos. === Members === Consejo Superior de Investigaciones Científicas (CSIC). Centro Tecnolóxico de Telecomunicaciones de Galicia (Gradiant). Instituto Imdea Software. Instituto Nacional de Ciberseguridad (INCIBE). Mondragón Unibertsitatea. Tecnalia. Universidad Carlos III de Madrid. Universidad de Castilla-La Mancha. Universidad de Granada. Universidad de la Laguna. Universidad de León. Universidad de Málaga. Universidad de Murcia. Universidad de Vigo. Universidad Politécnica de Madrid. Universidad Rey Juan Carlos. Universitat Oberta de Catalunya. IKERLAN. === Honorary Members === Centre for the Development of Industrial Technology (CDTI). (2017) Instituto Nacional de Ciberseguridad (INCIBE). (2016) == Initiatives and Participations == RENIC is ECSO member, and is also a member of its board of directors. A collaboration agreement between RENIC and the Innovative Business Cluster on Cybersecurity (AEI Cybersecurity) has been signed. RENIC is pleased to sponsor the Cybersecurity Research National Conferences (JNIC) JNIC2017 edition, organized by Universidad Rey Juan Carlos. RENIC is pleased to announce the publication of the online version of the Catalog and knowledge map of cybersecurity research

    Read more →
  • White-box cryptography

    White-box cryptography

    In cryptography, the white-box model refers to an extreme attack scenario, in which an adversary has full unrestricted access to a cryptographic implementation, most commonly of a block cipher such as the Advanced Encryption Standard (AES). A variety of security goals may be posed (see the section below), the most fundamental being "unbreakability", requiring that any (bounded) attacker should not be able to extract the secret key hardcoded in the implementation, while at the same time the implementation must be fully functional. In contrast, the black-box model only provides an oracle access to the analyzed cryptographic primitive (in the form of encryption and/or decryption queries). There is also a model in-between, the so-called gray-box model, which corresponds to additional information leakage from the implementation, more commonly referred to as side-channel leakage. White-box cryptography is a practice and study of techniques for designing and attacking white-box implementations. It has many applications, including digital rights management (DRM), pay television, protection of cryptographic keys in the presence of malware, mobile payments and cryptocurrency wallets. Examples of DRM systems employing white-box implementations include CSS and Widevine. White-box cryptography is closely related to the more general notions of obfuscation, in particular, to Black-box obfuscation, proven to be impossible, and to Indistinguishability obfuscation, constructed recently under well-founded assumptions but so far being infeasible to implement in practice. As of January 2023, there are no publicly known unbroken white-box designs of standard symmetric encryption schemes. On the other hand, there exist many unbroken white-box implementations of dedicated block ciphers designed specifically to achieve incompressibility (see § Security goals). == Security goals == Depending on the application, different security goals may be required from a white-box implementation. Specifically, for symmetric-key algorithms the following are distinguished: Unbreakability is the most fundamental goal requiring that a bounded attacker should not be able to recover the secret key embedded in the white-box implementation. Without this requirement, all other security goals are unreachable since a successful attacker can simply use a reference implementation of the encryption scheme together with the extracted key. One-wayness requires that a white-box implementation of an encryption scheme can not be used by a bounded attacker to decrypt ciphertexts. This requirement essentially turns a symmetric encryption scheme into a public-key encryption scheme, where the white-box implementation plays the role of the public key associated to the embedded secret key. This idea was proposed already in the famous work of Diffie and Hellman in 1976 as a potential public-key encryption candidate. Code lifting security is an informal requirement on the context, in which the white-box program is being executed. It demands that an attacker can not extract a functional copy of the program. This goal is particularly relevant in the DRM setting. Code obfuscation techniques are often used to achieve this goal. A commonly used technique is to compose the white-box implementation with so-called external encodings. These are lightweight secret encodings that modify the function computed by the white-box part of an application. It is required that their effect is canceled in other parts of the application in an obscure way, using code obfuscation techniques. Alternatively, the canceling counterparts can be applied on a remote server. Incompressibility requires that an attacker can not significantly compress a given white-box implementation. This can be seen as a way to achieve code lifting security (see above), since exfiltrating a large program from a constrained device (for example, an embedded or a mobile device) can be time-consuming and may be easy to detect by a firewall. Examples of incompressible designs include SPACE cipher, SPNbox, WhiteKey and WhiteBlock. These ciphers use large lookup tables that can be pseudorandomly generated from a secret master key. Although this makes the recovery of the master key hard, the lookup tables themselves play the role of an equivalent secret key. Thus, unbreakability is achieved only partially. Traceability (Traitor tracing) requires that each distributed white-box implementation contains a digital watermark allowing identification of the guilty user in case the white-box program is being leaked and distributed publicly. == History == The white-box model with initial attempts of white-box DES and AES implementations were first proposed by Chow, Eisen, Johnson and van Oorshot in 2003. The designs were based on representing the cipher as a network of lookup tables and obfuscating the tables by composing them with small (4- or 8-bit) random encodings. Such protection satisfied a property that each single obfuscated table individually does not contain any information about the secret key. Therefore, a potential attacker has to combine several tables in their analysis. The first two schemes were broken in 2004 by Billet, Gilbert, and Ech-Chatbi using structural cryptanalysis. The attack was subsequently called "the BGE attack". The numerous consequent design attempts (2005-2022) were quickly broken by practical dedicated attacks. In 2016, Bos, Hubain, Michiels and Teuwen showed that an adaptation of standard side-channel power analysis attacks can be used to efficiently and fully automatically break most existing white-box designs. This result created a new research direction about generic attacks (correlation-based, algebraic, fault injection) and protections against them. == Competitions == Four editions of the WhibOx contest were held in 2017, 2019, 2021 and 2024 respectively. These competitions invited white-box designers both from academia and industry to submit their implementation in the form of (possibly obfuscated) C code. At the same time, everyone could attempt to attack these programs and recover the embedded secret key. Each of these competitions lasted for about 4-5 months. WhibOx 2017 / CHES 2017 Capture the Flag Challenge targeted the standard AES block cipher. Among 94 submitted implementations, all were broken during the competition, with the strongest one staying unbroken for 28 days. WhibOx 2019 / CHES 2019 Capture the Flag Challenge again targeted the AES block cipher. Among 27 submitted implementations, 3 programs stayed unbroken throughout the competition, but were broken after 51 days since the publication. WhibOx 2021 / CHES 2021 Capture the Flag Challenge changed the target to ECDSA, a digital signature scheme based on elliptic curves. Among 97 submitted implementations, all were broken within at most 2 days. WhibOx 2024 / CHES 2024 Capture the Flag Challenge again targeted ECDSA. Among 47 submitted implementations, all were broken during the competition, with the strongest one staying unbroken for almost 5 days.

    Read more →
  • Social media surgery

    Social media surgery

    A social media surgery is a gathering at which volunteer "surgeons" with expertise in using web tools, chiefly social media, offer free advice in using such tools, to representatives ("patients") of non-profit organisations, charities, community groups and activists, with "no boring speeches or jargon". The idea was conceived by Pete Ashton, with Nick Booth of Podnosh Ltd, who ran the first such surgery in Birmingham, England, on 15 October 2008. In July 2009, a spin-off surgery (dubbed the "Social media mob") started in Mosman, Australia, and in January 2010, the first spin-off surgery in Africa was held. On 16 February 2012, it was announced that the Social Media Surgery movement had won "the Prime Minister’s Big Society Award". Prime Minister David Cameron said: This is an excellent initiative - such a simple idea and yet so effective. The popularity of these surgeries and the fact that they have inspired so many others across the country to follow in their footsteps, is testament to its brilliance. Congratulations to Nick and all the volunteers who have shared their time and expertise to help so many local groups make the most of the internet to support their community. A great example of the Big Society in action. The scheme also won the 2013 Adult Learners' Week "BBC Learning Through Technology Award".

    Read more →
  • Conditional disclosure of secrets

    Conditional disclosure of secrets

    Conditional disclosure of secrets (CDS) is a primitive, studied in information-theoretic cryptography, that allows distributed, non-communicating parties to coordinate the release of information to a third party. CDS was initially introduced for use in the context of private information retrieval, and has been related to communication complexity and non-local quantum computation. == Definition of conditional disclosure of secrets == The conditional disclosure of secrets setting involves three players; Alice, Bob and the referee. Alice receives an input x ∈ { 0 , 1 } n {\displaystyle x\in \{0,1\}^{n}} and a secret z ∈ { 0 , 1 } {\displaystyle z\in \{0,1\}} , and Bob receives a string y ∈ { 0 , 1 } n {\displaystyle y\in \{0,1\}^{n}} . A choice of Boolean function f : { 0 , 1 } 2 n → { 0 , 1 } {\displaystyle f:\{0,1\}^{2n}\rightarrow \{0,1\}} is fixed in advance and known to all players. Alice and Bob cannot communicate with one another, but share a string of random bits which we label r {\displaystyle r} . Alice and Bob compute messages m A = m A ( x , z , r ) {\displaystyle m_{A}=m_{A}(x,z,r)} and m B = m B ( y , r ) {\displaystyle m_{B}=m_{B}(y,r)} , which they send to the referee. The referee knows ( x , y ) {\displaystyle (x,y)} . A CDS protocol consists of the encoding maps applied by Alice and Bob. A protocol is said to be ϵ {\displaystyle \epsilon } -correct if, for all ( x , y ) ∈ f − 1 ( 1 ) {\displaystyle (x,y)\in f^{-1}(1)} , the referee can determine z {\displaystyle z} with probability 1 − ϵ {\displaystyle 1-\epsilon } . A protocol is said to be δ {\displaystyle \delta } -secure if, for all ( x , y ) ∈ f − 1 ( 0 ) {\displaystyle (x,y)\in f^{-1}(0)} the distribution of the messages is δ {\displaystyle \delta } close to a simulator distribution (in total variation distance), where the simulator distribution is independent of z {\displaystyle z} . The communication complexity of a CDS protocol P is the total number of bits of message sent by Alice and Bob. The CDS communication cost of a function, C D S ϵ , δ ( f ) {\displaystyle CDS_{\epsilon ,\delta }(f)} is the minimal communication cost of an ϵ {\displaystyle \epsilon } -correct, δ {\displaystyle \delta } secure protocol that implements f {\displaystyle f} . The randomness complexity and randomness cost of implementing a function in the CDS model are defined similarly, but consider the number of bits of shared random bits held by Alice and Bob. == Basic properties of the primitive == === Amplification === Supposing we have an ϵ {\displaystyle \epsilon } -correct and δ {\displaystyle \delta } -secure CDS protocol, it is known that we can find a new protocol which reduces the correctness and privacy errors at the expense of an increased communication and randomness cost. More specifically, the following theorem has been proven Theorem (Amplification). A CDS protocol for f which supports a single-bit secret with privacy and correctness error of 1/3 can be transformed into a new CDS protocol with privacy and correctness error of 2 − Ω ( k ) {\displaystyle 2^{-\Omega (k)}} and communication/randomness complexity which are larger than those of the original protocol by a multiplicative factor of O(k). In fact, somewhat more than the above theorem is true in that the size of the secret can also be made to be of length k {\displaystyle k} , while simultaneously reducing the correctness and privacy errors as above. The proof involves first encoding the secret z {\displaystyle z} into a secret sharing scheme, and then running the original CDS protocol on each share of the resulting scheme. === Closure === If a CDS protocol for a function f {\displaystyle f} is known, then certain simple modifications of f {\displaystyle f} have CDS protocols with similar efficiency. The simplest case is to consider a CDS protocol for function f {\displaystyle f} and ask for a similarly efficient protocol for the negation of f {\displaystyle f} , labelled ¬ f {\displaystyle \neg f} . This is addressed by the following theorem Theorem (CDS is closed under complement). Suppose that f has a CDS protocol with randomness cost of ρ {\displaystyle \rho } bits, communication complexity of t {\displaystyle t} bits, and privacy and correctness errors δ = ϵ = 2 − k {\displaystyle \delta =\epsilon =2^{-k}} . Then ¬ f {\displaystyle \neg f} has a CDS scheme with similar privacy and correctness errors, and randomness and communication complexity of O ( k 3 ρ 2 t + k 3 ρ 3 ) {\displaystyle O(k^{3}\rho ^{2}t+k^{3}\rho ^{3})} . The cost of a CDS protocol is also closed under formula's, in the following sense. Consider two functions f 1 {\displaystyle f_{1}} and f 2 {\displaystyle f_{2}} . Then, the communication and randomness costs of f 1 ∧ f 2 {\displaystyle f_{1}\wedge f_{2}} as well as f 1 ∨ f 2 {\displaystyle f_{1}\vee f_{2}} are not much larger than the sum of the costs for f 1 {\displaystyle f_{1}} and f 2 {\displaystyle f_{2}} . See Applebaum et al. for a precise statement. == Upper and lower bounds on communication cost == Given a function f {\displaystyle f} we would like to understand the communication and randomness costs to implement f {\displaystyle f} in the CDS setting. Towards understanding this, protocols for implementing CDS have been developed (which give an upper bound on the cost) and lower bound strategies have been developed. For most functions, there is a large gap between the known upper and lower bound, so understanding the cost of CDS remains largely an open problem. This section presents some of what is known so far about the cost of CDS. === Secret sharing based upper bound === A subject with a close relationship to CDS is secret sharing. Secret sharing constructions provide an upper bound on the cost of CDS protocols. A secret sharing scheme encodes a secret, s {\displaystyle s} into a set of shares S 1 , . . . , S n {\displaystyle S_{1},...,S_{n}} . Associated to any secret sharing scheme is an access structure, which consists of a set of authorized sets A = A 1 , . . . , A k {\displaystyle {\mathcal {A}}={A_{1},...,A_{k}}} with A i ⊆ { S 1 , . . . , S n } {\displaystyle A_{i}\subseteq \{S_{1},...,S_{n}\}} . The authorized sets are those subsets of the A i {\displaystyle A_{i}} from which it is possible to recover the secret recorded into the scheme. A succinct way to describe an access structure is in terms of a function f A : { 0 , 1 } n → { 0 , 1 } {\displaystyle f_{\mathcal {A}}:\{0,1\}^{n}\rightarrow \{0,1\}} . Each subset of the shares K [ x ] ⊂ { S 1 , . . . , S n } {\displaystyle K[x]\subset \{S_{1},...,S_{n}\}} is labelled by a string x ∈ { 0 , 1 } n {\displaystyle x\in \{0,1\}^{n}} such that x i = 1 {\displaystyle x_{i}=1} if and only if S i ∈ K {\displaystyle S_{i}\in K} . Then we define f A {\displaystyle f_{\mathcal {A}}} to be such that f A ( x ) = 1 {\displaystyle f_{\mathcal {A}}(x)=1} if and only if K [ x ] ∈ A {\displaystyle K[x]\in {\mathcal {A}}} . In words, the function f A {\displaystyle f_{\mathcal {A}}} is 1 when given an authorized subset as input, and 0 otherwise. A basic result in the theory of secret sharing is that an access structure A {\displaystyle {\mathcal {A}}} can be realized in a secret sharing scheme if and only if f A {\displaystyle f_{\mathcal {A}}} is monotone. The size of a secret sharing scheme is defined as the total number of bits in the shares S i {\displaystyle S_{i}} . For monotone functions, there is an upper bound on the communication cost in CDS for any monotone function f {\displaystyle f} in terms of the size of any secret sharing scheme with access structure given by f {\displaystyle f} , C D S ϵ = 0 , δ = 0 ( f ) ≤ S h a r i n g S i z e ( f ) {\displaystyle CDS_{\epsilon =0,\delta =0}(f)\leq SharingSize(f)} For some concrete classes of secret sharing schemes, this relationship can be extended to general (non-monotone) Boolean functions. This leads to an upper bound on CDS cost in terms of the size of any span program that computes f {\displaystyle f} , C D S ϵ = 0 , δ = 0 ( f ) ≤ S P k ( f ) {\displaystyle CDS_{\epsilon =0,\delta =0}(f)\leq SP_{k}(f)} The class of problems with efficient (polynomial size) span program is the complexity class M o d k L {\displaystyle Mod_{k}L} , so problems in this class have efficient CDS protocols. === Sub-exponential upper bounds for all functions === Using a matching vector family based construction, it has been proven that ∀ f , C D S ϵ = 0 , δ = 0 ( f ) ≤ 2 O ( n log ⁡ n ) {\displaystyle \forall f,\,\,\,\,\,\,CDS_{\epsilon =0,\delta =0}(f)\leq 2^{O({\sqrt {n\log n}})}} . The technique for this proof is similar to one used to prove upper bounds on private information retrieval. This upper bound on CDS also leads to sub-exponential upper bounds on the size of a large class of secret sharing schemes. === Lower bounds from communication complexity === In a CDS protocol, the referee is given the inputs ( x , y ) {\displaystyle (x,y)} . This means it is not clear if the messages sent by Alice a

    Read more →
  • Argumentation framework

    Argumentation framework

    In artificial intelligence and related fields, an argumentation framework is a way to deal with contentious information and draw conclusions from it using formalized arguments. In an abstract argumentation framework, entry-level information is a set of abstract arguments that, for instance, represent data or a proposition. Conflicts between arguments are represented by a binary relation on the set of arguments. In concrete terms, an argumentation framework is represented with a directed graph such that the nodes are the arguments, and the arrows represent the attack relation. There exist some extensions of the Dung's framework, like the logic-based argumentation frameworks or the value-based argumentation frameworks. == Abstract argumentation frameworks == === Formal framework === Abstract argumentation frameworks, also called argumentation frameworks à la Dung, are defined formally as a pair: A set of abstract elements called arguments, denoted A {\displaystyle A} A binary relation on A {\displaystyle A} , called attack relation, denoted R {\displaystyle R} For instance, the argumentation system S = ⟨ A , R ⟩ {\displaystyle S=\langle A,R\rangle } with A = { a , b , c , d } {\displaystyle A=\{a,b,c,d\}} and R = { ( a , b ) , ( b , c ) , ( d , c ) } {\displaystyle R=\{(a,b),(b,c),(d,c)\}} contains four arguments ( a , b , c {\displaystyle a,b,c} and d {\displaystyle d} ) and three attacks ( a {\displaystyle a} attacks b {\displaystyle b} , b {\displaystyle b} attacks c {\displaystyle c} and d {\displaystyle d} attacks c {\displaystyle c} ). Dung defines some notions : an argument a ∈ A {\displaystyle a\in A} is acceptable with respect to E ⊆ A {\displaystyle E\subseteq A} if and only if E {\displaystyle E} defends a {\displaystyle a} , that is ∀ b ∈ A {\displaystyle \forall b\in A} such that ( b , a ) ∈ R , ∃ c ∈ E {\displaystyle (b,a)\in R,\exists c\in E} such that ( c , b ) ∈ R {\displaystyle (c,b)\in R} , a set of arguments E {\displaystyle E} is conflict-free if there is no attack between its arguments, formally : ∀ a , b ∈ E , ( a , b ) ∉ R {\displaystyle \forall a,b\in E,(a,b)\not \in R} , a set of arguments E {\displaystyle E} is admissible if and only if it is conflict-free and all its arguments are acceptable with respect to E {\displaystyle E} . === Different semantics of acceptance === ==== Extensions ==== To decide if an argument can be accepted or not, or if several arguments can be accepted together, Dung defines several semantics of acceptance that allows, given an argumentation system, sets of arguments (called extensions) to be computed. For instance, given S = ⟨ A , R ⟩ {\displaystyle S=\langle A,R\rangle } , E {\displaystyle E} is a complete extension of S {\displaystyle S} only if it is an admissible set and every acceptable argument with respect to E {\displaystyle E} belongs to E {\displaystyle E} , E {\displaystyle E} is a preferred extension of S {\displaystyle S} only if it is a maximal element (with respect to the set-theoretical inclusion) among the admissible sets with respect to S {\displaystyle S} , E {\displaystyle E} is a stable extension of S {\displaystyle S} only if it is a conflict-free set that attacks every argument that does not belong in E {\displaystyle E} (formally, ∀ a ∈ A ∖ E , ∃ b ∈ E {\displaystyle \forall a\in A\backslash E,\exists b\in E} such that ( b , a ) ∈ R {\displaystyle (b,a)\in R} , E {\displaystyle E} is the (unique) grounded extension of S {\displaystyle S} only if it is the smallest element (with respect to set inclusion) among the complete extensions of S {\displaystyle S} . There exists some inclusions between the sets of extensions built with these semantics : Every stable extension is preferred, Every preferred extension is complete, The grounded extension is complete, If the system is well-founded (there exists no infinite sequence a 0 , a 1 , … , a n , … {\displaystyle a_{0},a_{1},\dots ,a_{n},\dots } such that ∀ i > 0 , ( a i + 1 , a i ) ∈ R {\displaystyle \forall i>0,(a_{i+1},a_{i})\in R} ), all these semantics coincide—only one extension is grounded, stable, preferred, and complete. Some other semantics have been defined. One introduce the notation E x t σ ( S ) {\displaystyle Ext_{\sigma }(S)} to note the set of σ {\displaystyle \sigma } -extensions of the system S {\displaystyle S} . In the case of the system S {\displaystyle S} in the figure above, E x t σ ( S ) = { { a , d } } {\displaystyle Ext_{\sigma }(S)=\{\{a,d\}\}} for every Dung's semantic—the system is well-founded. That explains why the semantics coincide, and the accepted arguments are: a {\displaystyle a} and d {\displaystyle d} . ==== Labellings ==== Labellings are a more expressive way than extensions to express the acceptance of the arguments. Concretely, a labelling is a mapping that associates every argument with a label in (the argument is accepted), out (the argument is rejected), or undec (the argument is undefined—not accepted or refused). One can also note a labelling as a set of pairs ( a r g u m e n t , l a b e l ) {\displaystyle ({\mathit {argument}},{\mathit {label}})} . Such a mapping does not make sense without additional constraint. The notion of reinstatement labelling guarantees the sense of the mapping. L {\displaystyle L} is a reinstatement labelling on the system S = ⟨ A , R ⟩ {\displaystyle S=\langle A,R\rangle } if and only if : ∀ a ∈ A , L ( a ) = i n {\displaystyle \forall a\in A,L(a)={\mathit {in}}} if and only if ∀ b ∈ A {\displaystyle \forall b\in A} such that ( b , a ) ∈ R , L ( b ) = o u t {\displaystyle (b,a)\in R,L(b)={\mathit {out}}} ∀ a ∈ A , L ( a ) = o u t {\displaystyle \forall a\in A,L(a)={\mathit {out}}} if and only if ∃ b ∈ A {\displaystyle \exists b\in A} such that ( b , a ) ∈ R {\displaystyle (b,a)\in R} and L ( b ) = i n {\displaystyle L(b)={\mathit {in}}} ∀ a ∈ A , L ( a ) = u n d e c {\displaystyle \forall a\in A,L(a)={\mathit {undec}}} if and only if L ( a ) ≠ i n {\displaystyle L(a)\neq {\mathit {in}}} and L ( a ) ≠ o u t {\displaystyle L(a)\neq {\mathit {out}}} One can convert every extension into a reinstatement labelling: the arguments of the extension are in, those attacked by an argument of the extension are out, and the others are undec. Conversely, one can build an extension from a reinstatement labelling just by keeping the arguments in. Indeed, Caminada proved that the reinstatement labellings and the complete extensions can be mapped in a bijective way. Moreover, the other Datung's semantics can be associated to some particular sets of reinstatement labellings. Reinstatement labellings distinguish arguments not accepted because they are attacked by accepted arguments from undefined arguments—that is, those that are not defended cannot defend themselves. An argument is undec if it is attacked by at least another undec. If it is attacked only by arguments out, it must be in, and if it is attacked some argument in, then it is out. The unique reinstatement labelling that corresponds to the system S {\displaystyle S} above is L = { ( a , i n ) , ( b , o u t ) , ( c , o u t ) , ( d , i n ) } {\displaystyle L=\{(a,{\mathit {in}}),(b,{\mathit {out}}),(c,{\mathit {out}}),(d,{\mathit {in}})\}} . === Inference from an argumentation system === In the general case when several extensions are computed for a given semantic σ {\displaystyle \sigma } , the agent that reasons from the system can use several mechanisms to infer information: Credulous inference: the agent accepts an argument if it belongs to at least one of the σ {\displaystyle \sigma } -extensions—in which case, the agent risks accepting some arguments that are not acceptable together ( a {\displaystyle a} attacks b {\displaystyle b} , and a {\displaystyle a} and b {\displaystyle b} each belongs to an extension) Skeptical inference: the agent accepts an argument only if it belongs to every σ {\displaystyle \sigma } -extension. In this case, the agent risks deducing too little information (if the intersection of the extensions is empty or has a very small cardinal). For these two methods to infer information, one can identify the set of accepted arguments, respectively C r σ ( S ) {\displaystyle Cr_{\sigma }(S)} the set of the arguments credulously accepted under the semantic σ {\displaystyle \sigma } , and S c σ ( S ) {\displaystyle Sc_{\sigma }(S)} the set of arguments accepted skeptically under the semantic σ {\displaystyle \sigma } (the σ {\displaystyle \sigma } can be missed if there is no possible ambiguity about the semantic). Of course, when there is only one extension (for instance, when the system is well-founded), this problem is very simple: the agent accepts arguments of the unique extension and rejects others. The same reasoning can be done with labellings that correspond to the chosen semantic : an argument can be accepted if it is in for each labelling and refused if it is out for each labelling, the others being in an undecided state (the status of the arguments can remind the

    Read more →
  • Outfit of the day

    Outfit of the day

    Outfit of the day (commonly abbreviated OOTD) is a phrase used online by users sharing what outfits (or "fits") they wear on a particular day or occasion. The video or post often mentions where each article of clothing, shoes, jewelry, and other accessories is from. OOTD posts are typically found on social media websites, such as Tumblr, Instagram, and Pinterest, and OOTD videos on YouTube and TikTok. Motives for sharing OOTD content vary, from encouraging viewers to buy a certain product, showing off personal style, or giving outfit inspiration. == History == "Outfit of the Day" videos started as early as 2010 but gained popularity in 2019. By 2016, the hashtag "OOTD" on Instagram had over 80 million posts. OOTD videos have become popular with the average internet user, as they express one's fashion sense and style to their followers. == Use in marketing == Brands will use famous influencers to promote their products using the "outfit of the day" tactic in hopes that users will buy the product to emulate the influencer. This tactic has increased sales for many brands. Creators of OOTD content can also profit, often through brand deals or affiliate links. Vogue has a recurring segment on YouTube that shows "Every outfit (fill in celebrity name here) wears in a week." == Variants == A variant is "outfit(s) of the week" (OOTW), where a user will share multiple outfits to be worn over the course of several days or a week. OOTDs are often seen in "Get ready with me" (GRWM) videos, where a user films their morning routine. In these videos, the filmers talk about their plans for the day, what makeup products they are using to get ready, and the "Outfit of the day" they are wearing. == Criticism == Some fashion writers have suggested that the proliferation of OOTD content encourages people to buy new clothing rather than to wear already owned items. Some stylists have also proposed that OOTD content encourages users to follow trends rather than explore and find their own style.

    Read more →
  • ServerNet

    ServerNet

    ServerNet is a switched fabric communications link primarily used in proprietary computers made by Tandem Computers, Compaq, and HP. Its features include good scalability, clean fault containment, error detection and failover. The ServerNet architecture specification defines a connection between nodes, either processor or high performance I/O nodes such as storage devices. == History == Tandem Computers developed the original ServerNet architecture and protocols for use in its own proprietary computer systems starting in 1992, and released the first ServerNet systems in 1995. Early attempts to license the technology and interface chips to other companies failed, due in part to a disconnect between the culture of selling complete hardware / software / middleware computer systems and that needed for selling and supporting chips and licensing technology. A follow-on development effort ported the Virtual Interface Architecture to ServerNet with PCI interface boards connecting personal computers. Infiniband directly inherited many ServerNet features. As of 2017, systems still ship based on the ServerNet architecture.

    Read more →
  • Snake oil (cryptography)

    Snake oil (cryptography)

    In cryptography, snake oil is any cryptographic method or product considered to be bogus or fraudulent. The name derives from snake oil, one type of patent medicine widely available in the 19th century United States. Distinguishing secure cryptography from insecure cryptography can be difficult from the viewpoint of a user. Many cryptographers, such as Bruce Schneier and Phil Zimmermann, undertake to educate the public in how secure cryptography is done, as well as highlighting the misleading marketing of some cryptographic products. The Snake Oil FAQ describes itself as "a compilation of common habits of snake oil vendors. It cannot be the sole method of rating a security product, since there can be exceptions to most of these rules. [...] But if you're looking at something that exhibits several warning signs, you're probably dealing with snake oil." == Some examples of snake oil cryptography techniques == This is not an exhaustive list of snake oil signs. A more thorough list is given in the references. Secret system Some encryption systems will claim to rely on a secret algorithm, technique, or device; this is categorized as security through obscurity. Criticisms of this are twofold. First, a 19th-century rule known as Kerckhoffs's principle, later formulated as Shannon's maxim, teaches that "the enemy knows the system" and the secrecy of a cryptosystem algorithm does not provide any advantage. Second, secret methods are not open to public peer review and cryptanalysis, so potential mistakes and insecurities can go unnoticed. Technobabble Snake oil salespeople may use "technobabble" to sell their product since cryptography is a complicated subject. "Unbreakable" Claims of a system or cryptographic method being "unbreakable" are always false (or true under some limited set of conditions), and are generally considered a sure sign of snake oil. "Military grade" There is no accepted standard or criterion for "military grade" ciphers. One-time pads One-time pads are a popular cryptographic method to invoke in advertising, because it is well known that one-time pads, when implemented correctly, are genuinely unbreakable. The problem comes in implementing one-time pads, which is rarely done correctly. Cryptographic systems that claim to be based on one-time pads are considered suspect, particularly if they do not describe how the one-time pad is implemented, or they describe a flawed implementation. Unsubstantiated "bit" claims Cryptographic products are often accompanied with claims of using a high number of bits for encryption, apparently referring to the key length used. However key lengths are not directly comparable between symmetric and asymmetric systems. Furthermore, the details of implementation can render the system vulnerable. For example, in 2008 it was revealed that a number of hard drives sold with built-in "128-bit AES encryption" were actually using a simple and easily defeated "XOR" scheme. AES was only used to store the key, which was easy to recover without breaking AES.

    Read more →
  • Scripped

    Scripped

    Scripped was an online screenplay services company offering three services: script writing, script registration, and script coverage. Scripped did not facilitate collaboration among screenwriters. It combined with Zhura in 2010. According to Techcrunch, Scripped had more than 60,000 writers as of March 2010. Scripped was administered by Sunil Rajaraman, Ryan Buckley and Zak Freer. Actor, writer, and director Edward Burns and screenwriter Steven E. de Souza joined Scripped's Board of Advisers in May 2008. In 2008, the company formed a partnership with Write Brothers, makers of Movie Magic Screenwriter software. On March 29, 2010, Scripped announced that it closed $250,000 in private investment and merged with competitor Zhura. Scripped's CEO, Sunil Rajaraman, remains the merged company's Chief Executive Officer. On April 1, 2015, citing a serious technical failure, Scripped shuttered its service. As part of the announcement, it was disclosed that their backup servers had failed as well, losing all of its users' stored scripts. The website URL currently redirects to WriterDuet's website, another online scriptwriting service; Scripped had advertised WriterDuet in Scripped's shutdown open letter. == Features == The Scripped Writer provided a built-in screenplay template which formatted the document to a standard for scripts as recommended by the AMPAS. The screenplay document was composed of seven elements: scene, action, character, dialog, parenthetical, transition and general. Each element had a specific style to which the Scripped Writer conformed as text was entered. Like other client-side screenplay software, Scripped offered Tab-Enter toggling between screenplay elements, making the writing process much faster. Text files could be imported into the Scripped Writer and automatically conformed to the screenplay template. Completed scripts could be exported as PDF files. In May 2011 the administrators of Scripped launched Scripted.com - a sister site focused on freelance writing jobs. Subsequent to the service's launch, the company was renamed to Scripted, Inc.

    Read more →
  • Data exchange

    Data exchange

    Data exchange is the process of moving data from one information system to another. It often involves transforming data that is native to the source system into a form that is consumable by the target system or to a standardized form that is consumable by any compatible system. In particular, data exchange allows data to be shared between computer programs. Data exchange is similar to data integration except that data may be restructured with possible loss of content. There may be no way to transform a particular collection based on exchange constraints. Conversely, there may be multiple ways to transform the data, in which case one option must be identified in order to achieve compatibility between source and target. There are two main types of data exchange: broadcast and peer-to-peer (a.k.a. unicast). For broadcast, data is transmitted simultaneously to all consumers. Just as a conference call, all participants get the same information from the speaker at the same time. For peer-to-peer, data is sent to a single receiver, defined by a specific address. For example, a letter goes to just one mail box. == Single-domain == In some domains, a multiple source and target schema (proprietary data formats) may exist. An exchange or interchange format is often developed for a single domain, and then necessary routines (mappings) are written to (indirectly) transform/translate each and every source schema to each and every target schema by using the interchange format as an intermediate step. That requires less work than writing and debugging the many routines that would be required to directly translate each source schema directly to each target schema. Examples of these transformative interchange formats include: Standard Interchange Format for geospatial data; Data Interchange Format for spreadsheet data; Open Document Format for spreadsheets, charts, presentations and word processing documents; GPS eXchange Format or Keyhole Markup Language for describing GPS data; GDSII for integrated circuit layout. == Representation == A data exchange (a.k.a. interchange) language defines a domain-independent way to represent data. These languages have evolved from being markup and display-oriented to support the encoding of metadata that describes the structural attributes of the information. Practice has shown that certain types of formal languages are better suited for this task than others, since their specification is driven by a formal process instead of particular software implementation. For example, XML is a markup language that was designed to enable the creation of dialects (the definition of domain-specific sublanguages). However, it does not contain domain-specific dictionaries or fact types. Beneficial to a reliable data exchange is the availability of standard dictionaries-taxonomies and tools libraries such as parsers, schema validators, and transformation tools. === XML === The popularity of XML for data exchange on the World Wide Web has several reasons. First of all, it is closely related to the preexisting standards Standard Generalized Markup Language (SGML) and Hypertext Markup Language (HTML), and as such a parser written to support these two languages can be easily extended to support XML as well. For example, XHTML has been defined as a format that is formal XML, but understood correctly by most (if not all) HTML parsers. === YAML === YAML was designed to be human-readable and authored via a text editor with notion similar to reStructuredText and wiki syntax. YAML 1.2 also includes a shorthand notion that is compatible with JSON, and as such any JSON document is also valid YAML; this however does not hold the other way. === REBOL === REBOL was designed to be human-readable and authored via a text editor. It uses a simple free-form syntax with minimal punctuation and a rich set of data types (such as URL, email, date and time, tuple, string, tag) that respect common standards. It is designed to not need any additional meta-language, being designed in a metacircular fashion which is why the parse dialect used for definitions and transformations of REBOL dialects is also itself a dialect of REBOL. REBOL was used as a source of inspiration for JSON. === Gellish === Gellish English is a formalized subset of natural English (language), which includes a simple grammar and a large, extensible dictionary (taxonomy) that defines the general and domain specific terminology, whereas the concepts are arranged in a hierarchy, which supports inheritance of knowledge and requirements. The dictionary also includes standardized fact types. The terms and relation types together can be used to create and interpret expressions of facts, knowledge, requirements and other information. Gellish can be used in combination with SQL, RDF/XML, OWL and various other meta-languages. The Gellish standard is a combination of ISO 10303-221 (AP221) and ISO 15926. === List === The following describes and compares popular data exchange languages. Columns Schemas – Whether supports representing domain specific data structure definition Flexible – Whether supports extension of the semantic expression capabilities without modifying the schema Semantic verification – Whether supports semantic verification of the correctness of expressions in the language Dictionary – Whether includes a dictionary and a taxonomy (hierarchy) of concepts with inheritance Information model – Whether supports an information model Synonyms and homonyms – Whether supports the use of synonyms and homonyms in expressions Dialecting – Whether is available in multiple natural languages or dialects Web standard – Whether is standardized by a recognized body Transformations – Whether includes a translation to other standards Lightweight – Whether a lightweight version is available Human readable – Whether expressions are understandable without training Compatibility – Which other tools can be used or are required

    Read more →
  • Social search

    Social search

    Social search is a behavior of retrieving and searching on a social searching engine that mainly searches user-generated content such as news, videos and images related search queries on social media like Facebook, LinkedIn, Twitter, Instagram and Flickr. It is an enhanced version of web search that combines traditional algorithms. The idea behind social search is that instead of ranking search results purely based on semantic relevance between a query and the results, a social search system also takes into account social relationships between the results and the searcher. The social relationships could be in various forms. For example, in LinkedIn people search engine, the social relationships include social connections between searcher and each result, whether or not they are in the same industries, work for the same companies, belong the same social groups, and go the same schools, etc. Social search may not be demonstrably better than algorithm-driven search. In the algorithmic ranking model that search engines used in the past, relevance of a site is determined after analyzing the text and content on the page and link structure of the document. In contrast, search results with social search highlight content that was created or touched by other users who are in the Social Graph of the person conducting a search. It is a personalized search technology with online community filtering to produce highly personalized results. Social search takes many forms, ranging from simple shared bookmarks or tagging of content with descriptive labels to more sophisticated approaches that combine human intelligence with computer algorithms. Depending on the feature-set of a particular search engine, these results may then be saved and added to community search results, further improving the relevance of results for future searches of that keyword. The principle behind social search is that human network oriented results would be more meaningful and relevant for the user, instead of computer algorithms deciding the results for specific queries. == Research and implementations == Over the years, there have been different studies, researches and some implementations of Social Search. In 2008, there were a few startup companies that focused on ranking search results according to one's social graph on social networks. Companies in the social search space include Sproose, Mahalo, Jumper 2.0, Scour, Wink, Eurekster, and Delver. Former efforts include Wikia Search. In 2008, a story on TechCrunch showed Google potentially adding in a voting mechanism to search results similar to Digg's methodology. This suggests growing interest in how social groups can influence and potentially enhance the ability of algorithms to find meaningful data for end users. There are also other services like Sentiment that turn search personal by searching within the users' social circles. In 2009, a startup project called HeyStaks (www.heystaks.com) developed a web browser plugin "HayStaks". HeyStaks applies social search through collaboration in web search as a way that leads to better search results. The main motivation for HeyStaks to work on this idea is to provide the user with features that search engines didn't provide at that time. For instance, different searches have indicated that about 70% of the time when user search for something, a friend or a coworker have found it already. Also, studies have shown that approximately, 30% of people who use online search, search for something that they have found before. The startup believe that they help avoid these kind of issues by providing a shared and rich search experience through a list of recommendations that get generated based on search results. In October 2009, Google rolled out its "Social Search"; after a time in beta, the feature was expanded to multiple languages in May 2011. Before the expansion however in 2010 Bing and Google were already taking into account re-tweets and Likes when providing search results. However, after a search deal with Twitter ended without renewal, Google began to retool its Social Search. In January 2012, Google released "Search plus Your World", a further development of Social Search. The feature, which is integrated into Google's regular search as an opt-out feature, pulls references to results from Google+ profiles. The goal was to deliver better, more relevant and personalized search results with this integration. This integration however had some problems in which Google+ still is not wildly adopted or has much usage among many users. Later on, Google was criticized by Twitter for the perceived potential impact of "Search plus Your World" upon web publishers, describing the feature's release to the public as a "bad day for the web", while Google replied that Twitter refused to allow deep search crawling by Google of Twitter's content. By Google integrating Google+, the company was encouraging users to switch to Google's social networking site in order to improve search results. One famous example occurred when Google showed a link to Mark Zuckerberg's dormant Google+ account rather than the active Facebook profile. In November 2014 these accusations started to die down because Google's Knowledge Graph started to finally show links to Facebook, Twitter, and other social media sites. In December 2008, Twitter had re-introduced their people search feature. While the interface had since changed significantly, it allows you to search either full names or usernames in a straight-forward search engine. In January 2013, Facebook announced a new search engine called Graph Search still in the beta stages. The goal was to allow users to prioritize results that were popular with their social circle over the general internet. Facebook's Graph search utilized Facebook's user generated content to target users. Although there have been different researches and studies in social search, social media networks have not vested enough interest in working with search engines. LinkedIn for example has taken steps to improve its own individual search functions in order to stray users from external search engines. Even Microsoft started working with Twitter in order to integrate some tweets into Bing's search results in November 2013. Yet Twitter has its own search engine which points out how much value their data has and why they would like to keep it in house. In the end though social search will never be truly comprehensive of the subjects that matter to people unless users opt to be completely public with their information. == Social discovery == Social discovery is the use of social preferences and personal information to predict what content will be desirable to the user. Technology is used to discover new people and sometimes new experiences shopping, meeting friends or even traveling. The discovery of new people is often in real-time, enabled by mobile apps. However, social discovery is not limited to meeting people in real-time, it also leads to sales and revenue for companies via social media. An example of retail would be the addition of social sharing with music, through the iTunes music store. There is a social component to discovering new music Social discovery is at the basis of Facebook's profitability, generating ad revenue by targeting the ads to users using the social connections to enhance the commercial appeal. == Social search engines == A social search engine in an aspect can be thought of as a search engine that provides an answer for a question from another answer by identifying a person in the answer. That can happen by retrieving a user submitted query and determining that the query is related to the question; and provides an answer, including the link to the resource, as part of search results that are responsive to the query. Few social search engines depend only on online communities. Depending on the feature-set of a particular search engine, these results may then be saved and added to community search results, further improving the relevance of results for future searches of that keyword. Social search engines are considered a part of Web 2.0 because they use the collective filtering of online communities to elevate particularly interesting or relevant content using tagging. These descriptive tags add to the meta data embedded in Web pages, theoretically improving the results for particular keywords over time. A user will generally see suggested tags for a particular search term, indicating tags that have previously been added. An implementation of a social search engine is Aardvark. Aardvark is a social search engine that is based on the "village paradigm" which is about connecting the user who has a question with friends or friends of friends whom can answer his or her question. In Aadvark, a user ask a question in different ways that mostly involves online ways such as instant messaging, email, web input or other non-online ways such as text message or voice. The Aar

    Read more →
  • CANaerospace

    CANaerospace

    CANaerospace is a higher layer protocol based on Controller Area Network (CAN) which has been developed by Stock Flight Systems in 1998 for aeronautical applications. == Background == CANaerospace supports airborne systems employing the Line-replaceable unit (LRU) concept to share data across CAN and ensures interoperability between CAN LRUs by defining CAN physical layer characteristics, network layers, communication mechanisms, data types and aeronautical axis systems. CANaerospace is an open source project, was initiated to standardize the interface between CAN LRUs on the system level. CANaerospace is continuously being developed further and has also been published by NASA as the Advanced General Aviation Transport Experiments Databus Standard in 2001. It found widespread use in aeronautical research worldwide. A major research aircraft that employs several CANaerospace networks for real-time computer interconnection is the Stratospheric Observatory for Infrared Astronomy (SOFIA), a Boeing 747SP with a 2.5m astronomic telescope. CANaerospace is also frequently used in flight simulation and connects entire aircraft cockpits (i.e. in Eurofighter Typhoon simulators) to the simulation host computers. In Italy CANaerospace is used as UAV data bus technology. Furthermore, CANaerospace serves as communication network in several general aviation avionics systems. The CANaerospace interface definition closes the gap between the ISO/OSI layer 1 and 2 CAN protocol (which is implemented in the CAN controller itself) and the specific requirements of distributed systems in aircraft. It may be used as a primary or ancillary avionics network and was designed to meet the following requirements: Democratic network: CANaerospace does not require any master/slave relationships between LRUs or a "bus controller", thereby avoiding a potential single source of failure. Every node in the network has the same rights for participation in the bus traffic. Self-identifying message format: Each CANaerospace message contains information about the type of the data and the transmitting node. This allows the data to be unambiguously recognized at each receiving node. Continuous Message Numbering: Each CANaerospace message contains a continuously incremented number which allows coherent processing of messages in the receiving stations. Message Status Code: Each CANaerospace message contains information about the integrity of the data is conveying. This allows receiving stations to evaluate the quality of the received data and to react accordingly. Emergency Event Signaling: CANaerospace defines a mechanism that allows each node to transmit information about exception or error situations. This information can be used by other stations to determine the network health. Node Service Interface: As an enhancement to CAN, CANaerospace provides a means for individual stations on the network to communicate with each other using connection-oriented and connectionless services. Predefined CAN Identifier Assignment: CANaerospace offers a predefined identifier assignment list for normal operation data. In addition to the predefined list, user-defined identifier assignment lists may be used. Ease of Implementation: The amount of code to implement CANaerospace is very little by design in order to minimize the effort for testing and certification of flight safety critical systems. Openness to Extensions: All CANaerospace definitions are extendable to provide flexibility for future enhancements and to allow adaptions to the requirements of specific applications. Free Availability: No cost whatsoever apply for the use of CANaerospace. The specification can be downloaded from the Internet == Physical interface == To ensure interoperability and reliable communication, CANaerospace specifies the electrical characteristics, bus transceiver requirements and data rates with the corresponding tolerances based on ISO 11898. The bit timing calculation (baud rate accuracy, sample point definition) and robustness to electromagnetic interference are given special emphasis. Also addressed are CAN connector, wiring considerations and design guidelines to maximize electromagnetic compatibility. == Communication layers == The Bosch CAN specification itself allows messages being transmitted both periodically and aperiodically but does not cover issues like data representation, node addressing or connection-oriented protocols. CAN is entirely based on Anyone-to-Many (ATM) communication which means that CAN messages are always received by all stations in the network. The advantage of the CAN concept is inherent data consistency between all stations, the drawback is that it does not allow node addressing which is the basis for Peer-to-Peer (PTP) communication. Using CAN networks in aeronautical applications, however, demands a standard targeted to the specific requirements of airborne systems which implies that communication between individual stations in the network must be possible to enable the required degree of system monitoring. Consequently, CANaerospace defines additional ISO/OSI layer 3, 4 and 6 functions to support node addressing and unified ATM/PTP communication mechanisms. PTP communication allows to set up client/server interactions between individual stations in the network either temporarily or permanently. More than one of these interactions may be in effect at any given time and each node may be client for one operation and server for another at the same time. This CANaerospace mechanism is called "Node Service Concept" and allows i.e. to distribute system functions over several stations in the network or to control dynamic system reconfiguration in case of failure. The Node Service concept supports both connection-oriented and connectionless interactions like with TCP/IP and UDP/IP for Ethernet. Enabling both ATM and PTP communication for CAN requires the introduction of independent network layers to isolate the different types of communication. This is realized for CANaerospace by forming CAN identifier groups as shown in Figure 1. The resulting structure creates Logical Communication Channels (LCCs) and assigns a specific communication type (ATM, PTP) to each of the LCCs. User-defined LCCs provide the necessary freedom for designers and allow the implementation of CANaerospace according to the needs of specific applications. Figure 1: Logical Communication Channels for CANaerospace As a side effect, the CAN identifier groups in Figure 1 affect the priority of the message transmission in case of bus arbitration. The communication channels are therefore arranged according to their relative importance: Emergency Event Data Channel (EED): This communication channel is used for messages which require immediate action (i.e. system degradation or reconfiguration) and have to be transmitted with very high priority. Emergency Event Data uses ATM communication exclusively. High/Low Priority Node Service Data Channel (NSH/NSL): These communication channels are used for client/server interactions using PTP communication. The corresponding services may be of the connection-oriented as well as the connectionless type. NSH/NSL may also be used to support test and maintenance functions. Normal Operation Data Channel (NOD): This communication channel is used for the transmission of the data which is generated during normal system operation and described in the CANaerospace identifier assignment list. These messages may be transmitted periodically or aperiodically as well as synchronously or asynchronously. All messages which cannot be assigned to other communication channels shall use this channel. High/Low Priority User-Defined Data Channel (UDH/UDL): This channel is dedicated to communication which cannot, due to their specific characteristics, be assigned other channels without violating the CANaerospace specification. As long as the defined identifier range is used, the message content and the communication type (ATM, PTP) for these channels may be specified by the system designer. To ensure interoperability it is highly recommended that the use of these channels is minimized. Debug Service Data Channel (DSD): This channel is dedicated to messages which are used temporarily for development and test purposes only and are not transmitted during normal operation. As long as the defined identifier range is used, the message content and the communication type (ATM, PTP) for these channels may be specified by the system designer. == Data representation == The majority of the real-time control systems used in aeronautics employ "big endian" processor architectures. This data representation was therefore specified for CANaerospace as well. With big endian data representation, the most significant bit of any datum is arranged leftmost and transmitted first on CANaerospace as shown in Figure 2. Figure 2: "Big Endian" Data Representation for CANaerospace CANaerospace uses a self-identifying message

    Read more →
  • Semantic analytics

    Semantic analytics

    Semantic analytics, also termed semantic relatedness, is the use of ontologies to analyze content in web resources. This field of research combines text analytics and Semantic Web technologies like RDF. Semantic analytics measures the relatedness of different ontological concepts. Some academic research groups that have active project in this area include Kno.e.sis Center at Wright State University among others. == History == An important milestone in the beginning of semantic analytics occurred in 1996, although the historical progression of these algorithms is largely subjective. In his seminal study publication, Philip Resnik established that computers have the capacity to emulate human judgement. Spanning the publications of multiple journals, improvements to the accuracy of general semantic analytic computations all claimed to revolutionize the field. However, the lack of a standard terminology throughout the late 1990s was the cause of much miscommunication. This prompted Budanitsky & Hirst to standardize the subject in 2006 with a summary that also set a framework for modern spelling and grammar analysis. In the early days of semantic analytics, obtaining a large enough reliable knowledge bases was difficult. In 2006, Strube & Ponzetto demonstrated that Wikipedia could be used in semantic analytic calculations. The usage of a large knowledge base like Wikipedia allows for an increase in both the accuracy and applicability of semantic analytics. == Methods == Given the subjective nature of the field, different methods used in semantic analytics depend on the domain of application. No singular methods is considered correct, however one of the most generally effective and applicable method is explicit semantic analysis (ESA). ESA was developed by Evgeniy Gabrilovich and Shaul Markovitch in the late 2000s. It uses machine learning techniques to create a semantic interpreter, which extracts text fragments from articles into a sorted list. The fragments are sorted by how related they are to the surrounding text. Latent semantic analysis (LSA) is another common method that does not use ontologies, only considering the text in the input space. == Applications == Entity linking Ontology building / knowledge base population Search and query tasks Natural language processing Spoken dialog systems (e.g., Amazon Alexa, Google Assistant, Microsoft's Cortana) Artificial intelligence Knowledge management The application of semantic analysis methods generally streamlines organizational processes of any knowledge management system. Academic libraries often use a domain-specific application to create a more efficient organizational system. By classifying scientific publications using semantics and Wikipedia, researchers are helping people find resources faster. Search engines like Semantic Scholar provide organized access to millions of articles.

    Read more →
  • Squeaky Dolphin

    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."

    Read more →
  • Strong secrecy

    Strong secrecy

    Strong secrecy is a term used in formal proof-based cryptography for making propositions about the security of cryptographic protocols. It is a stronger notion of security than syntactic (or weak) secrecy. Strong secrecy is related with the concept of semantic security or indistinguishability used in the computational proof-based approach. Bruno Blanchet provides the following definition for strong secrecy: Strong secrecy means that an adversary cannot see any difference when the value of the secret changes For example, if a process encrypts a message m an attacker can differentiate between different messages, since their ciphertexts will be different. Thus m is not a strong secret. If however, probabilistic encryption were used, m would be a strong secret. The randomness incorporated into the encryption algorithm will yield different ciphertexts for the same value of m.

    Read more →