AI Face Fusion

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  • Color gradient

    Color gradient

    In color science, a color gradient (also known as a color ramp or a color progression) specifies a range of position-dependent colors, usually used to fill a region. In assigning colors to a set of values, a gradient is a continuous colormap, a type of color scheme. In computer graphics, the term swatch has come to mean a palette of active colors. == Definitions == Color gradient is a set of colors arranged in a linear order (ordered) A continuous colormap is a curve through a colorspace === Strict definition === A colormap is a function which associate a real value r with point c in color space C {\displaystyle C} f : [ r m i n , r m a x ] ⊂ R → C {\displaystyle f:[r_{min},r_{max}]\subset \mathbf {R} \to C} which is defined by: a colorspace C an increasing sequence of sampling points r 0 < . . . < r m ∈ [ r m i n , r m a x ] {\displaystyle r_{0}<... Read more →

  • Pumping lemma for regular languages

    Pumping lemma for regular languages

    In the theory of formal languages, the pumping lemma for regular languages is a lemma that describes an essential property of all regular languages. Informally, it says that all sufficiently long strings in a regular language may be pumped—that is, have a middle section of the string repeated an arbitrary number of times—to produce a new string that is also part of the language. The pumping lemma is useful for proving that a specific language is not a regular language, by showing that the language does not have the property. Specifically, the pumping lemma says that for any regular language L {\displaystyle L} , there exists a constant p {\displaystyle p} such that any string w {\displaystyle w} in L {\displaystyle L} with length at least p {\displaystyle p} can be split into three substrings x {\displaystyle x} , y {\displaystyle y} and z {\displaystyle z} ( w = x y z {\displaystyle w=xyz} , with y {\displaystyle y} being non-empty), such that the strings x z , x y z , x y y z , x y y y z , . . . {\displaystyle xz,xyz,xyyz,xyyyz,...} are also in L {\displaystyle L} . The process of repeating y {\displaystyle y} zero or more times is known as "pumping". Moreover, the pumping lemma guarantees that the length of x y {\displaystyle xy} will be at most p {\displaystyle p} , thus giving a "small" substring x y {\displaystyle xy} that has the desired property. Languages with a finite number of strings vacuously satisfy the pumping lemma by having p {\displaystyle p} equal to the maximum string length in L {\displaystyle L} plus one. By doing so, no strings at all in L {\displaystyle L} have length at least p {\displaystyle p} . The pumping lemma was first proven by Michael Rabin and Dana Scott in 1959, and rediscovered shortly after by Yehoshua Bar-Hillel, Micha A. Perles, and Eli Shamir in 1961, as a simplification of their pumping lemma for context-free languages. == Formal statement == Let L {\displaystyle L} be a regular language. Then there exists an integer p ≥ 1 {\displaystyle p\geq 1} depending only on L {\displaystyle L} such that every string w {\displaystyle w} in L {\displaystyle L} of length at least p {\displaystyle p} ( p {\displaystyle p} is called the "pumping length") can be written as w = x y z {\displaystyle w=xyz} (i.e., w {\displaystyle w} can be divided into three substrings), satisfying the following conditions: | y | ≥ 1 {\displaystyle |y|\geq 1} | x y | ≤ p {\displaystyle |xy|\leq p} ( ∀ n ≥ 0 ) ( x y n z ∈ L ) {\displaystyle (\forall n\geq 0)(xy^{n}z\in L)} y {\displaystyle y} is the substring that can be pumped (removed or repeated any number of times, and the resulting string is always in L {\displaystyle L} ). (1) means the loop y {\displaystyle y} to be pumped must be of length at least one, that is, not an empty string; (2) means the loop must occur within the first p {\displaystyle p} characters. | x | {\displaystyle |x|} must be smaller than p {\displaystyle p} (conclusion of (1) and (2)), but apart from that, there is no restriction on x {\displaystyle x} and z {\displaystyle z} . In simple words, for any regular language L {\displaystyle L} , any sufficiently long string w {\displaystyle w} (in L {\displaystyle L} ) can be split into 3 parts, i.e. w = x y z {\displaystyle w=xyz} , such that all the strings x y n z {\displaystyle xy^{n}z} for n ≥ 0 {\displaystyle n\geq 0} are also in L {\displaystyle L} . Below is a formal expression of the pumping lemma. ∀ L ⊆ Σ ∗ , regular ( L ) ⟹ ∃ p ≥ 1 , ∀ w ∈ L , | w | ≥ p ⟹ ∃ x , y , z ∈ Σ ∗ , ( w = x y z ) ∧ ( | y | ≥ 1 ) ∧ ( | x y | ≤ p ) ∧ ( ∀ n ≥ 0 , x y n z ∈ L ) {\displaystyle {\begin{array}{l}\forall L\subseteq \Sigma ^{},{\mbox{regular}}(L)\implies \\\quad \exists p\geq 1,\forall w\in L,|w|\geq p\implies \\\qquad \exists x,y,z\in \Sigma ^{},(w=xyz)\land (|y|\geq 1)\land (|xy|\leq p)\land (\forall n\geq 0,xy^{n}z\in L)\end{array}}} == Use of the lemma to prove non-regularity == The pumping lemma is often used to prove that a particular language is non-regular: a proof by contradiction may consist of exhibiting a string (of the required length) in the language that lacks the property outlined in the pumping lemma. Example: The language L = { a n b n : n ≥ 0 } {\displaystyle L=\{a^{n}b^{n}:n\geq 0\}} over the alphabet Σ = { a , b } {\displaystyle \Sigma =\{a,b\}} can be shown to be non-regular as follows: Assume that some constant p ≥ 1 {\displaystyle p\geq 1} exists as required by the lemma. Let w {\displaystyle w} in L {\displaystyle L} be given by w = a p b p {\displaystyle w=a^{p}b^{p}} , which is a string longer than p {\displaystyle p} . By the pumping lemma, there must exist a decomposition w = x y z {\displaystyle w=xyz} with | x y | ≤ p {\displaystyle |xy|\leq p} and | y | ≥ 1 {\displaystyle |y|\geq 1} such that x y i z {\displaystyle xy^{i}z} in L {\displaystyle L} for every i ≥ 0 {\displaystyle i\geq 0} . Since | x y | ≤ p {\displaystyle |xy|\leq p} , the string y {\displaystyle y} only consists of instances of a {\displaystyle a} . Because | y | ≥ 1 {\displaystyle |y|\geq 1} , it contains at least one instance of the letter a {\displaystyle a} . Pumping y {\displaystyle y} to give x y 2 z {\displaystyle xy^{2}z} gives a word with more instances of the letter a {\displaystyle a} than the letter b {\displaystyle b} , since some instances of a {\displaystyle a} but none of b {\displaystyle b} were added. Therefore, x y 2 z {\displaystyle xy^{2}z} is not in L {\displaystyle L} which contradicts the pumping lemma. Therefore, L {\displaystyle L} cannot be regular. The proof that the language of balanced (i.e., properly nested) parentheses is not regular follows the same idea. Given p {\displaystyle p} , there is a string of balanced parentheses that begins with more than p {\displaystyle p} left parentheses, so that y {\displaystyle y} will consist entirely of left parentheses. By repeating y {\displaystyle y} , a string can be produced that does not contain the same number of left and right parentheses, and so they cannot be balanced. == Proof of the pumping lemma == For every regular language there is a finite-state automaton (FSA) that accepts the language. The number of states in such an FSA are counted and that count is used as the pumping length p {\displaystyle p} . For a string of length at least p {\displaystyle p} , let q 0 {\displaystyle q_{0}} be the start state and let q 1 , . . . , q p {\displaystyle q_{1},...,q_{p}} be the sequence of the next p {\displaystyle p} states visited as the string is emitted. Because the FSA has only p {\displaystyle p} states, within this sequence of p + 1 {\displaystyle p+1} visited states there must be at least one state that is repeated. Write q s {\displaystyle q_{s}} for such a state. The transitions that take the machine from the first encounter of state q s {\displaystyle q_{s}} to the second encounter of state q s {\displaystyle q_{s}} match some string. This string is called y {\displaystyle y} in the lemma, and since the machine will match a string without the y {\displaystyle y} portion, or with the string y {\displaystyle y} repeated any number of times, the conditions of the lemma are satisfied. For example, the following image shows an FSA. The FSA accepts the string: abcd. Since this string has a length at least as large as the number of states, which is four (so the total number of states that the machine passes through to scan abcd would be 5), the pigeonhole principle indicates that there must be at least one repeated state among the start state and the next four visited states. In this example, only q 1 {\displaystyle q_{1}} is a repeated state. Since the substring bc takes the machine through transitions that start at state q 1 {\displaystyle q_{1}} and end at state q 1 {\displaystyle q_{1}} , that portion could be repeated and the FSA would still accept, giving the string abcbcd. Alternatively, the bc portion could be removed and the FSA would still accept giving the string ad. In terms of the pumping lemma, the string abcd is broken into an x {\displaystyle x} portion a, a y {\displaystyle y} portion bc and a z {\displaystyle z} portion d. As a side remark, the problem of checking whether a given string can be accepted by a given nondeterministic finite automaton without visiting any state repeatedly, is NP hard. == General version of pumping lemma for regular languages == If a language L {\displaystyle L} is regular, then there exists a number p ≥ 1 {\displaystyle p\geq 1} (the pumping length) such that every string u w v {\displaystyle uwv} in L {\displaystyle L} with | w | ≥ p {\displaystyle |w|\geq p} can be written in the form u w v = u x y z v {\displaystyle uwv=uxyzv} with strings x {\displaystyle x} , y {\displaystyle y} and z {\displaystyle z} such that | x y | ≤ p {\displaystyle |xy|\leq p} , | y | ≥ 1 {\displaystyle |y|\geq 1} and u x y i z v {\displaystyle uxy^{i}zv} is in L {\displaystyle L} for every integer i ≥ 0 {\displaystyle i\geq 0} . From this, the above standard v

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  • Sequential minimal optimization

    Sequential minimal optimization

    Sequential minimal optimization (SMO) is an algorithm for solving the quadratic programming (QP) problem that arises during the training of support-vector machines (SVM). It was invented by John Platt in 1998 at Microsoft Research. SMO is widely used for training support vector machines and is implemented by the popular LIBSVM tool. The publication of the SMO algorithm in 1998 has generated a lot of excitement in the SVM community, as previously available methods for SVM training were much more complex and required expensive third-party QP solvers. == Optimization problem == Consider a binary classification problem with a dataset (x1, y1), ..., (xn, yn), where xi is an input vector and yi ∈ {-1, +1} is a binary label corresponding to it. A soft-margin support vector machine is trained by solving a quadratic programming problem, which is expressed in the dual form as follows: max α ∑ i = 1 n α i − 1 2 ∑ i = 1 n ∑ j = 1 n y i y j K ( x i , x j ) α i α j , {\displaystyle \max _{\alpha }\sum _{i=1}^{n}\alpha _{i}-{\frac {1}{2}}\sum _{i=1}^{n}\sum _{j=1}^{n}y_{i}y_{j}K(x_{i},x_{j})\alpha _{i}\alpha _{j},} subject to: 0 ≤ α i ≤ C , for i = 1 , 2 , … , n , {\displaystyle 0\leq \alpha _{i}\leq C,\quad {\mbox{ for }}i=1,2,\ldots ,n,} ∑ i = 1 n y i α i = 0 {\displaystyle \sum _{i=1}^{n}y_{i}\alpha _{i}=0} where C is an SVM hyperparameter and K(xi, xj) is the kernel function, both supplied by the user; and the variables α i {\displaystyle \alpha _{i}} are Lagrange multipliers. == Algorithm == SMO is an iterative algorithm for solving the optimization problem described above. SMO breaks this problem into a series of smallest possible sub-problems, which are then solved analytically. Because of the linear equality constraint involving the Lagrange multipliers α i {\displaystyle \alpha _{i}} , the smallest possible problem involves two such multipliers. Then, for any two multipliers α 1 {\displaystyle \alpha _{1}} and α 2 {\displaystyle \alpha _{2}} , the constraints are reduced to: 0 ≤ α 1 , α 2 ≤ C , {\displaystyle 0\leq \alpha _{1},\alpha _{2}\leq C,} y 1 α 1 + y 2 α 2 = k , {\displaystyle y_{1}\alpha _{1}+y_{2}\alpha _{2}=k,} and this reduced problem can be solved analytically: one needs to find a minimum of a one-dimensional quadratic function. k {\displaystyle k} is the negative of the sum over the rest of terms in the equality constraint, which is fixed in each iteration. The algorithm proceeds as follows: Find a Lagrange multiplier α 1 {\displaystyle \alpha _{1}} that violates the Karush–Kuhn–Tucker (KKT) conditions for the optimization problem. Pick a second multiplier α 2 {\displaystyle \alpha _{2}} and optimize the pair ( α 1 , α 2 ) {\displaystyle (\alpha _{1},\alpha _{2})} . Repeat steps 1 and 2 until convergence. When all the Lagrange multipliers satisfy the KKT conditions (within a user-defined tolerance), the problem has been solved. Although this algorithm is guaranteed to converge, heuristics are used to choose the pair of multipliers so as to accelerate the rate of convergence. This is critical for large data sets since there are n ( n − 1 ) / 2 {\displaystyle n(n-1)/2} possible choices for α i {\displaystyle \alpha _{i}} and α j {\displaystyle \alpha _{j}} . == Related work == The first approach to splitting large SVM learning problems into a series of smaller optimization tasks was proposed by Bernhard Boser, Isabelle Guyon, and Vladimir Vapnik. It is known as the "chunking algorithm". The algorithm starts with a random subset of the data, solves this problem, and iteratively adds examples which violate the optimality conditions. One disadvantage of this algorithm is that it is necessary to solve QP-problems scaling with the number of SVs. On real world sparse data sets, SMO can be more than 1000 times faster than the chunking algorithm. In 1997, E. Osuna, R. Freund, and F. Girosi proved a theorem which suggests a whole new set of QP algorithms for SVMs. By the virtue of this theorem a large QP problem can be broken down into a series of smaller QP sub-problems. A sequence of QP sub-problems that always add at least one violator of the Karush–Kuhn–Tucker (KKT) conditions is guaranteed to converge. The chunking algorithm obeys the conditions of the theorem, and hence will converge. The SMO algorithm can be considered a special case of the Osuna algorithm, where the size of the optimization is two and both Lagrange multipliers are replaced at every step with new multipliers that are chosen via good heuristics. The SMO algorithm is closely related to a family of optimization algorithms called Bregman methods or row-action methods. These methods solve convex programming problems with linear constraints. They are iterative methods where each step projects the current primal point onto each constraint.

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  • Markovian discrimination

    Markovian discrimination

    Markovian discrimination is a class of spam filtering methods used in CRM114 and other spam filters to filter based on statistical patterns of transition probabilities between words or other lexical tokens in spam messages that would not be captured using simple bag-of-words naive Bayes spam filtering. == Markovian Discrimination vs. Bag-of-Words Discrimination == A bag-of-words model contains only a dictionary of legal words and their relative probabilities in spam and genuine messages. A Markovian model additionally includes the relative transition probabilities between words in spam and in genuine messages, where the relative transition probability is the likelihood that a given word will be written next, based on what the current word is. Put another way, a bag-of-words filter discriminates based on relative probabilities of single words alone regardless of phrase structure, while a Markovian word-based filter discriminates based on relative probabilities of either pairs of words, or, more commonly, short sequences of words. This allows the Markovian filter greater sensitivity to phrase structure. Neither naive Bayes nor Markovian filters are limited to the word level for tokenizing messages. They may also process letters, partial words, or phrases as tokens. In such cases, specific bag-of-words methods would correspond to general bag-of-tokens methods. Modelers can parameterize Markovian spam filters based on the relative probabilities of any such tokens' transitions appearing in spam or in legitimate messages. == Visible and Hidden Markov Models == There are two primary classes of Markov models, visible Markov models and hidden Markov models, which differ in whether the Markov chain generating token sequences is assumed to have its states fully determined by each generated token (the visible Markov models) or might also have additional state (the hidden Markov models). With a visible Markov model, each current token is modeled as if it contains the complete information about previous tokens of the message relevant to the probability of future tokens, whereas a hidden Markov model allows for more obscure conditional relationships. Since those more obscure conditional relationships are more typical of natural language messages including both genuine messages and spam, hidden Markov models are generally preferred over visible Markov models for spam filtering. Due to storage constraints, the most commonly employed model is a specific type of hidden Markov model known as a Markov random field, typically with a 'sliding window' or clique size ranging between four and six tokens.

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  • ISSCO Graphics

    ISSCO Graphics

    Integrated Software Systems Corporation (ISSCO), doing business as ISSCO Graphics, was an American software developer and publisher based in San Diego, California, and active from 1970 to 1986. They were best known for their enterprise graphics software packages, including Tellagraf, CueChart and Disspla. == History == ISSCO Graphics had considered acquiring Breakthrough Software, whose software focus involved PC DOS, as a means of getting into the PC arena, but backed off when Computer Associates made an offer to acquire ISSCO. By early 1987 it was reported that "Issco users breathe sigh of relief" that all was well. The ISSCO User's Group was founded in 1976. ISSCO, which was founded in 1970 by Peter Preuss, was acquired by Computer Associates in 1986. == Notable products == === Tellagraf === ISSCO's Tellagraf is an early software package designed to allow end-users to "turn out full color, professional quality charts" with initial results displayed on a screen, modified as needed, and then "a final 'hard-copy' can be made .. or made into 35mm color transparencies for projection onto a screen." Users of Tellagraf often had access to CueChart and Disspla software. Often computer sites having one had all three. Terminals with varying degrees of graphics, such as the DEC's VT100 and Tektronix's Tektronix 4xxx family of text and graphics terminals. were supported, and the software ran on popular computing platforms. Four years are important to Tellagraf's early history: 1978: ease of use 1980: graphic-artist quality 1982: introduction of CueChart, and recognition by IEEE. 1983: "quality graphics enters the mainstream of data processing with ..." Tellegraf was eventually acquired by Computer Associates and renamed CA-Tellegraf. SAS users found it helpful. Universities, research institutes and financial services firms were among early users. === Disspla === Disspla is a package of data plotting subroutines that can be used from high level languages. It was also acquired by Computer Associates. === Tellaplan === In 1983 ISSCO introduced Tellaplan, "a project planning, report and schedule charting system for Tell-A- Graf users in IBM MVS or CMS or Digital Equipment Corp. VAX computers" atop which they built "two visual project management software packages" three years later.

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  • Distributional–relational database

    Distributional–relational database

    A distributional–relational database, or word-vector database, is a database management system (DBMS) that uses distributional word-vector representations to enrich the semantics of structured data. As distributional word-vectors can be built automatically from large-scale corpora, this enrichment supports the construction of databases which can embed large-scale commonsense background knowledge into their operations. Distributional-Relational models can be applied to the construction of schema-agnostic databases (databases in which users can query the data without being aware of its schema), semantic search, schema-integration and inductive and abductive reasoning as well as different applications in which a semantically flexible knowledge representation model is needed. The main advantage of distributional–relational models over purely logical or semantic web models is the fact that the core semantic associations can be automatically captured from corpora, in contrast to the definition of manually curated ontologies and rule knowledge bases. == Distributional–relational models == Distributional–relational models were first formalized as a mechanism to cope with the vocabulary/semantic gap between users and the schema behind the data. In this scenario, distributional semantic relatedness measures, combined with semantic pivoting heuristics can support the approximation between user queries (expressed in their own vocabulary), and data (expressed in the vocabulary of the designer). In this model, the database symbols (entities and relations) are embedded into a distributional semantic space and have a geometric interpretation under a latent or explicit semantic space. The geometric aspect supports the semantic approximation between entities from different databases, or between a query term and a database entity. The distributional relational model then becomes a double layered model where the semantics of the structured data provides the fine-grained semantics intended by the database designer, which is extended by the distributional semantic model which contains the semantic associations expressed at a broader use. These models support the generalization from a closed communication scenario (in which database designers and users live in the same context, e.g. the same organization) to an open communication scenario (e.g. different organizations, the Web), creating an abstraction layer between users and the specific representation of the conceptual model.

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  • Is an AI Bug Finder Worth It in 2026?

    Is an AI Bug Finder Worth It in 2026?

    In search of the best AI bug finder? An AI bug finder is software that uses machine learning to help you get more done — it turns a rough idea into a polished result in seconds. When choosing one, weigh output quality, pricing, export formats, and how well it fits the tools you already use. Whether you are a beginner or a pro, the right AI bug finder slots into your workflow and pays for itself fast. Below we compare features, pricing, and real output so you can choose with confidence.

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  • Best AI Coding Assistants in 2026

    Best AI Coding Assistants in 2026

    Curious about the best AI coding assistant? An AI coding assistant is software that uses machine learning to help you get more done — it combines speed, accuracy, and an interface that just works. Hands-on testing shows real-world results vary, so a short free trial is the smartest way to decide. Whether you are a beginner or a pro, the right AI coding assistant slots into your workflow and pays for itself fast. Read on for hands-on impressions, pricing tiers, and the standout features that matter.

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  • Frankenstein complex

    Frankenstein complex

    The Frankenstein complex is a term coined by Isaac Asimov in his robot series, referring to the fear of mechanical men. == History == Some of Asimov's science fiction short stories and novels predict that this suspicion will become strongest and most widespread in respect of "mechanical men" that most-closely resemble human beings (see android), but it is also present on a lower level against robots that are plainly electromechanical automatons. The "Frankenstein complex" is similar in many respects to Masahiro Mori's uncanny valley hypothesis. The name, "Frankenstein complex", is derived from the name of Victor Frankenstein in the 1818 novel Frankenstein; or, The Modern Prometheus by Mary Shelley. In Shelley's story, Frankenstein created an intelligent, somewhat superhuman being, but he finds that his creation is horrifying to behold and abandons it. This ultimately leads to Victor's death at the conclusion of a vendetta between himself and his creation. In much of his fiction, Asimov depicts the general attitude of the public towards robots as negative, with ordinary people fearing that robots will either replace them or dominate them, although dominance would not be allowed under the specifications of the Three Laws of Robotics, the first of which is: "A robot may not harm a human being or, through inaction, allow a human being to come to harm." However, Asimov's fictitious earthly public is not fully persuaded by this, and remains largely suspicious and fearful of robots. I, Robot's short story "Little Lost Robot" is about this "fear of robots". In Asimov's robot novels, the Frankenstein complex is a major problem for roboticists and robot manufacturers. They do all they can to reassure the public that robots are harmless, even though this sometimes involves hiding the truth because they think that the public would misunderstand it. The fear by the public and the response of the manufacturers is an example of the theme of paternalism, the dread of paternalism, and the conflicts that arise from it in Asimov's fiction. The same theme occurs in many later works of fiction featuring robots, although it is rarely referred to as such.

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  • Best AI Bug Finders in 2026

    Best AI Bug Finders in 2026

    In search of the best AI bug finder? An AI bug finder is software that uses machine learning to help you get more done — it turns a rough idea into a polished result in seconds. When choosing one, weigh output quality, pricing, export formats, and how well it fits the tools you already use. Whether you are a beginner or a pro, the right AI bug finder slots into your workflow and pays for itself fast. We tested the leading options and ranked them by quality, value, and ease of use.

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  • AI Pair Programmers: Free vs Paid (2026)

    AI Pair Programmers: Free vs Paid (2026)

    Trying to pick the best AI pair programmer? An AI pair programmer is software that uses machine learning to help you get more done — it scales effortlessly from a single task to thousands. The best picks balance beginner-friendly simplicity with the depth power users need, and they ship updates often. Whether you are a beginner or a pro, the right AI pair programmer slots into your workflow and pays for itself fast. This guide breaks down the top picks, their pros and cons, and who each one is best for.

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  • Karen Spärck Jones

    Karen Spärck Jones

    Karen Ida Boalth Spärck Jones (26 August 1935 – 4 April 2007) was a self-taught programmer and a pioneering British computer and information scientist responsible for the concept of inverse document frequency (IDF), a technology that underlies most modern search engines. She was an advocate for women in computer science, her slogan being, "Computing is too important to be left to men." In 2019, The New York Times published her belated obituary in its series Overlooked, calling her "a pioneer of computer science for work combining statistics and linguistics, and an advocate for women in the field." From 2008, to recognise her achievements in the fields of information retrieval (IR) and natural language processing (NLP), the Karen Spärck Jones Award is awarded annually to a recipient for outstanding research in one or both of her fields. == Early life and education == Karen Ida Boalth Spärck Jones was born in Huddersfield, Yorkshire, England. Her parents were Alfred Owen Jones, a chemistry lecturer, and Ida Spärck, a Norwegian who worked for the Norwegian government while in exile in London during World War II. Spärck Jones was educated at a grammar school in Huddersfield and then from 1953 to 1956 at Girton College, Cambridge, studying history, with an additional final year in Moral Sciences (philosophy). While at Cambridge, Spärck Jones joined the organisation known as the Cambridge Language Research Unit (CLRU) and met the head of CLRU Margaret Masterman, who would inspire her to go into computer science. While working at the CLRU, Spärck Jones began pursuing her PhD. At the time of submission, her PhD thesis was cast aside as uninspired and lacking original thought, but was later published in its entirety as a book. She briefly became a school teacher before moving into computer science. Spärck Jones married fellow Cambridge computer scientist Roger Needham in 1958. Spärck Jones's mother, Ida Spärck, had fled Norway on one of the last boats out after the German invasion in April 1940, going on to serve the Norwegian government in exile in London throughout the war. This background of displacement and resilience shaped the household in which Spärck Jones grew up. She later kept her mother's Norwegian surname professionally after marrying, stating that "it maintains a permanent existence of your own." Spärck Jones described her entry into computing as almost accidental. She had been working as a schoolteacher when she began visiting the CLRU out of curiosity about her husband's work. It was Margaret Masterman — whom she later described as "a very strange and interesting woman" — who offered her a research position and drew her fully into the field. == Career == Spärck Jones worked at the Cambridge Language Research Unit from the late 1950s, then at Cambridge University Computer Laboratory from 1974 until her retirement in 2002. From 1999, she held the post of Professor of Computers and Information. She had been given a permanent position only in 1993, and earlier in her career had been employed on a series of short-term contracts. She continued to work in the Computer Laboratory until shortly before her death. Her publications include nine books and numerous papers. A full list of her publications is available from the Cambridge Computer Laboratory. Spärck Jones' main research interests, since the late 1950s, were natural language processing and information retrieval. In 1964, Spärck Jones published "Synonymy and Semantic Classification", which is now seen as a foundational paper in the field of natural language processing. One of her most important contributions was the concept of inverse document frequency (IDF) weighting in information retrieval, which she introduced in a 1972 paper. IDF is used in most search engines today, usually as part of the term frequency–inverse document frequency (TF–IDF) weighting scheme. In the 1980s, Spärck Jones began her work on early speech recognition systems. In 1982 she became involved in the Alvey Programme which was an initiative to motivate more computer science research across the country. == Significance of inverse document frequency == At the time Spärck Jones was working, most computer scientists were focused on making people adapt to machines — learning precise codes and commands to retrieve information. Spärck Jones was working in the opposite direction: teaching computers to understand human language as it is actually used. Her 1972 paper introduced the concept of inverse document frequency (IDF) by observing that not all words carry equal informational value. A word like "the" appears in virtually every document and tells a retrieval system almost nothing about what any specific document is about. A rare word like "photosynthesis," by contrast, is highly specific and informative. IDF assigns each word a statistical weight based on how rarely it occurs across a document collection — the rarer the word, the higher its weight. When combined with term frequency (TF), which measures how often a word appears within a single document, the resulting TF–IDF score gives every word a relevance rating that can be used to rank documents in response to a search query. By 2007, Spärck Jones noted that "pretty much every web engine uses those principles." Her colleague John Tait remarked that "a lot of the stuff she was working on until five or ten years ago seemed like mad nonsense, and now we take it for granted." The 1972 paper remains among the most cited works in information retrieval research, with over 4,500 citations recorded in Google Scholar at the time of her death. The conceptual foundation of TF–IDF — that word meaning is statistical and contextual — has also informed later developments in machine learning and natural language processing, including transformer-based language models such as BERT. == Impact on artificial intelligence == Even though Spärck Jones' views on artificial intelligence (AI) were rather pessimistic in regard to the perceived limitations of AI in information retrieval, her work in natural language processing, information retrieval, and introducing the concept of inverse document frequency (IDF) contributed to the future technological development of AI. Her statistical and ranking methods shifted the direction of the development of AI towards being more expandable and led by data. Her work had a more indirect and conceptual impact on AI, compared to the current and direct impact it has had on search engines. == Gender and advocacy == Spärck Jones spent the majority of her career at Cambridge on short-term contracts without permanent employment, a situation she attributed directly to gender. In her 2001 IEEE oral history interview she stated that Cambridge was "in many ways not user-friendly, in the sense of women-friendly." She was frequently the only woman present in professional meetings throughout her career. She channelled this experience into active advocacy. She was a founding member of the women@cl network at Cambridge's Computer Laboratory, worked on outreach programmes aimed at encouraging girls into computing, and became widely known for her slogan: "Computing is too important to be left to men." She was the first woman ever to receive the BCS Lovelace Medal. === Honours and awards === These include: Gerard Salton Award (1988) Elected a Fellow of Association for the Advancement of Artificial Intelligence (AAAI) in 1993 President of the Association for Computational Linguistics (ACL) in 1994 Honorary degree of Doctor of Science from The City University in 1997. Elected a Fellow of the British Academy (FBA), where she also served as Vice-President in 2000–2002 Fellow of European Association for Artificial Intelligence (ECCAI) Association for Information Science and Technology (ASIS&T) Award of Merit (2002) Association for Computational Linguistics (ACL) Lifetime Achievement Award (2004) ACM - AAAI Allen Newell Award (2006) BCS Lovelace Medal (2007) Association for Computing Machinery (ACM) Women's Group Athena Award (2007) == Death and legacy == Spärck Jones died on 4 April 2007, due to cancer at the age of 71. In 2008, the BCS Information Retrieval Specialist Group (BCS IRSG) in conjunction with the British Computer Society established an annual Karen Spärck Jones Award in her honour, to encourage and promote research that advances understanding of Natural Language Processing or Information Retrieval. The Karen Spärck Jones lecture sponsored by BCS recognises the contribution that women have made to computing. In August 2017, the University of Huddersfield renamed one of its campus buildings in her honour. Formerly known as Canalside West, the Spärck Jones building houses the University's School of Computing and Engineering. When Spärck Jones died in 2007, The Times did not publish an obituary for her, despite having published one for her husband Roger Needham in 2003. In 2019, The New York Times included her in its Overlooked series under the title "Ove

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  • Digital omnivore

    Digital omnivore

    A digital omnivore is a person who uses multiple modalities (devices) to access the Internet and other media content in their daily life. As people increasingly own mobile devices, cross-platform multimedia consumption has continued to shape the digital landscape, both in terms of the type of media content they consume and how they consume it. As of 2021, at least half of all global digital traffic is generated by mobile devices. == Connected devices and digital consumption == A 2015 study of digital media consumption showed that smartphones were primarily used for communication, and tablets were primarily used for entertainment – additionally, both were frequently used in conjuncture with other devices, like televisions. An earlier 2011 analysis of the way consumers in the U.S. viewed news content on their devices throughout the day demonstrated how people use different mobile devices for different functions. On a typical weekend morning, digital omnivores accessed their news using their tablet, favored their computer during the working day, and returned to tablet use in the evening, peaking between the hours of 9pm and midnight. Mobile phones were used for web-browsing throughout the day when users were away from their personal computer. Increased Wi-Fi availability and mobile broadband adoption have changed the way people are going online. In August 2011, more than a third (37.2%) of U.S. digital traffic coming from mobile phones occurred via a Wi-Fi connection while tablets, which traditionally required a Wi-Fi connection to access the Internet, are increasingly driving traffic using mobile broadband access. As of 2021, LTE, 5G, and other forms of mobile broadband access are available on the majority of mobile devices. Tablets contributed nearly 2% of all web browsing traffic in the United States in 2011. During this period, iPads also began to account for a higher share of Internet traffic than iPhones (46.8% vs. 42.6% of all iOS device traffic. == Implications for marketing, advertisers and publishers == As of 2021, the average amount of time spent daily consuming digital media was eight hours, an increase from 2020 and a further increase from 2019, partially as a result of the COVID-19 pandemic. Social media platforms such as Instagram, Facebook, Twitter, and TikTok, as well as other online platforms like YouTube, incorporate advertisements into the in-app or online experience, with some offering the ability to shop for and sell items through the app or website.

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  • Jacek M. Zurada

    Jacek M. Zurada

    Jacek M. Zurada is a Polish-American computer scientist who is a Professor of the Electrical and Computer Engineering Department at the University of Louisville, Kentucky. His M.S. and Ph.D. degrees are from Politechnika Gdaṅska (Gdansk University of Technology, Poland). He has held visiting appointments at the Swiss Federal Institute of Technology, Zurich, Princeton, Northeastern, and Auburn, and at overseas universities in Australia, Chile, China, France, Germany, Hong Kong, Italy, Japan, Poland, Singapore, Spain, and South Africa. He is a life fellow of IEEE and a fellow of the International Neural Networks Society and Doctor Honoris Causa of Czestochowa Institute of Technology, Poland. == Research == Zurada's research covers neural networks, deep learning, data mining with emphasis on data and feature understanding, rule extraction from semantic and visual information, machine learning, decomposition methods for salient feature extraction, and lambda learning rule for neural networks. == Professional and editorial service == Zurada was the editor-in-chief of IEEE Transactions on Neural Networks (1998–2003), an associate editor of IEEE Transactions on Circuits and Systems, Pt. I and Pt. II, Action Editor in Neural Networks (Elsevier) and on the editorial board of the Proceedings of the IEEE. He is an associate editor of Neurocomputing, Schedae Informaticae, the International Journal of Applied Mathematics and Computer Science, and Editor of the Springer Natural Computing, Advances in Intelligent Systems and Computing and Studies in Computational Intelligence Book series or volumes. == Awards and honours == In 2003 he was given the title of Professor by the President of Poland. Since 2005 he has been an elected Foreign Member of the Polish Academy of Sciences. He also received five honorary professorships from foreign universities, including Sichuan University in Chengdu, China, and Obuda University in Budapest, Hungary.

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  • Model inversion attack

    Model inversion attack

    Model inversion attack is a type of adversarial machine learning attack where an attacker tries to reconstruct or infer sensitive information about a model's training data by analyzing the outputs of a trained machine learning model. Instead of directly querying the underlying dataset, attackers query the model (usually via APIs or prediction interfaces), and leverage patterns in the model responses to infer properties of the original inputs. These attacks leverage the fact that machine learning models encode statistical information about their training data in their parameters and outputs, which can unintentionally leak private or proprietary information. Depending on the access level to the target model, model inversion attacks can be performed in both black-box and white-box settings. In a generic attack, an adversary makes several queries to a model and leverages the responses (e.g. confidence scores, predictions) to train a surrogate or inversion model that learns to approximate the inverse mapping from outputs to inputs. This process may enable the reconstruction of sensitive attributes, e.g., facial features, medical data, or user behavior patterns, from models trained on such data. The technique has been demonstrated against various models like deep neural networks, classification systems etc. The technique has significant privacy risks in areas like healthcare, finance, biometric identification etc. Mitigation strategies include restricting model access, reducing output granularity, using differential privacy and monitoring anomalous query patterns.

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