EigenMoments is a set of orthogonal, noise robust, invariant to rotation, scaling and translation and distribution sensitive moments. Their application can be found in signal processing and computer vision as descriptors of the signal or image. The descriptors can later be used for classification purposes. It is obtained by performing orthogonalization, via eigen analysis on geometric moments. == Framework summary == EigenMoments are computed by performing eigen analysis on the moment space of an image by maximizing signal-to-noise ratio in the feature space in form of Rayleigh quotient. This approach has several benefits in Image processing applications: Dependency of moments in the moment space on the distribution of the images being transformed, ensures decorrelation of the final feature space after eigen analysis on the moment space. The ability of EigenMoments to take into account distribution of the image makes it more versatile and adaptable for different genres. Generated moment kernels are orthogonal and therefore analysis on the moment space becomes easier. Transformation with orthogonal moment kernels into moment space is analogous to projection of the image onto a number of orthogonal axes. Nosiy components can be removed. This makes EigenMoments robust for classification applications. Optimal information compaction can be obtained and therefore a few number of moments are needed to characterize the images. == Problem formulation == Assume that a signal vector s ∈ R n {\displaystyle s\in {\mathcal {R}}^{n}} is taken from a certain distribution having correlation C ∈ R n × n {\displaystyle C\in {\mathcal {R}}^{n\times n}} , i.e. C = E [ s s T ] {\displaystyle C=E[ss^{T}]} where E[.] denotes expected value. Dimension of signal space, n, is often too large to be useful for practical application such as pattern classification, we need to transform the signal space into a space with lower dimensionality. This is performed by a two-step linear transformation: q = W T X T s , {\displaystyle q=W^{T}X^{T}s,} where q = [ q 1 , . . . , q n ] T ∈ R k {\displaystyle q=[q_{1},...,q_{n}]^{T}\in {\mathcal {R}}^{k}} is the transformed signal, X = [ x 1 , . . . , x n ] T ∈ R n × m {\displaystyle X=[x_{1},...,x_{n}]^{T}\in {\mathcal {R}}^{n\times m}} a fixed transformation matrix which transforms the signal into the moment space, and W = [ w 1 , . . . , w n ] T ∈ R m × k {\displaystyle W=[w_{1},...,w_{n}]^{T}\in {\mathcal {R}}^{m\times k}} the transformation matrix which we are going to determine by maximizing the SNR of the feature space resided by q {\displaystyle q} . For the case of Geometric Moments, X would be the monomials. If m = k = n {\displaystyle m=k=n} , a full rank transformation would result, however usually we have m ≤ n {\displaystyle m\leq n} and k ≤ m {\displaystyle k\leq m} . This is specially the case when n {\displaystyle n} is of high dimensions. Finding W {\displaystyle W} that maximizes the SNR of the feature space: S N R t r a n s f o r m = w T X T C X w w T X T N X w , {\displaystyle SNR_{transform}={\frac {w^{T}X^{T}CXw}{w^{T}X^{T}NXw}},} where N is the correlation matrix of the noise signal. The problem can thus be formulated as w 1 , . . . , w k = a r g m a x w w T X T C X w w T X T N X w {\displaystyle {w_{1},...,w_{k}}=argmax_{w}{\frac {w^{T}X^{T}CXw}{w^{T}X^{T}NXw}}} subject to constraints: w i T X T N X w j = δ i j , {\displaystyle w_{i}^{T}X^{T}NXw_{j}=\delta _{ij},} where δ i j {\displaystyle \delta _{ij}} is the Kronecker delta. It can be observed that this maximization is Rayleigh quotient by letting A = X T C X {\displaystyle A=X^{T}CX} and B = X T N X {\displaystyle B=X^{T}NX} and therefore can be written as: w 1 , . . . , w k = a r g m a x x w T A w w T B w {\displaystyle {w_{1},...,w_{k}}={\underset {x}{\operatorname {arg\,max} }}{\frac {w^{T}Aw}{w^{T}Bw}}} , w i T B w j = δ i j {\displaystyle w_{i}^{T}Bw_{j}=\delta _{ij}} === Rayleigh quotient === Optimization of Rayleigh quotient has the form: max w R ( w ) = max w w T A w w T B w {\displaystyle \max _{w}R(w)=\max _{w}{\frac {w^{T}Aw}{w^{T}Bw}}} and A {\displaystyle A} and B {\displaystyle B} , both are symmetric and B {\displaystyle B} is positive definite and therefore invertible. Scaling w {\displaystyle w} does not change the value of the object function and hence and additional scalar constraint w T B w = 1 {\displaystyle w^{T}Bw=1} can be imposed on w {\displaystyle w} and no solution would be lost when the objective function is optimized. This constraint optimization problem can be solved using Lagrangian multiplier: max w w T A w {\displaystyle \max _{w}{w^{T}Aw}} subject to w T B w = 1 {\displaystyle {w^{T}Bw}=1} max w L ( w ) = max w ( w T A w − λ w T B w ) {\displaystyle \max _{w}{\mathcal {L}}(w)=\max _{w}(w{T}Aw-\lambda w^{T}Bw)} equating first derivative to zero and we will have: A w = λ B w {\displaystyle Aw=\lambda Bw} which is an instance of Generalized Eigenvalue Problem (GEP). The GEP has the form: A w = λ B w {\displaystyle Aw=\lambda Bw} for any pair ( w , λ ) {\displaystyle (w,\lambda )} that is a solution to above equation, w {\displaystyle w} is called a generalized eigenvector and λ {\displaystyle \lambda } is called a generalized eigenvalue. Finding w {\displaystyle w} and λ {\displaystyle \lambda } that satisfies this equations would produce the result which optimizes Rayleigh quotient. One way of maximizing Rayleigh quotient is through solving the Generalized Eigen Problem. Dimension reduction can be performed by simply choosing the first components w i {\displaystyle w_{i}} , i = 1 , . . . , k {\displaystyle i=1,...,k} , with the highest values for R ( w ) {\displaystyle R(w)} out of the m {\displaystyle m} components, and discard the rest. Interpretation of this transformation is rotating and scaling the moment space, transforming it into a feature space with maximized SNR and therefore, the first k {\displaystyle k} components are the components with highest k {\displaystyle k} SNR values. The other method to look at this solution is to use the concept of simultaneous diagonalization instead of Generalized Eigen Problem. === Simultaneous diagonalization === Let A = X T C X {\displaystyle A=X^{T}CX} and B = X T N X {\displaystyle B=X^{T}NX} as mentioned earlier. We can write W {\displaystyle W} as two separate transformation matrices: W = W 1 W 2 . {\displaystyle W=W_{1}W_{2}.} W 1 {\displaystyle W_{1}} can be found by first diagonalize B: P T B P = D B {\displaystyle P^{T}BP=D_{B}} . Where D B {\displaystyle D_{B}} is a diagonal matrix sorted in increasing order. Since B {\displaystyle B} is positive definite, thus D B > 0 {\displaystyle D_{B}>0} . We can discard those eigenvalues that large and retain those close to 0, since this means the energy of the noise is close to 0 in this space, at this stage it is also possible to discard those eigenvectors that have large eigenvalues. Let P ^ {\displaystyle {\hat {P}}} be the first k {\displaystyle k} columns of P {\displaystyle P} , now P T ^ B P ^ = D B ^ {\displaystyle {\hat {P^{T}}}B{\hat {P}}={\hat {D_{B}}}} where D B ^ {\displaystyle {\hat {D_{B}}}} is the k × k {\displaystyle k\times k} principal submatrix of D B {\displaystyle D_{B}} . Let W 1 = P ^ D B ^ − 1 / 2 {\displaystyle W_{1}={\hat {P}}{\hat {D_{B}}}^{-1/2}} and hence: W 1 T B W 1 = ( P ^ D B ^ − 1 / 2 ) T B ( P ^ D B ^ − 1 / 2 ) = I {\displaystyle W_{1}^{T}BW_{1}=({\hat {P}}{\hat {D_{B}}}^{-1/2})^{T}B({\hat {P}}{\hat {D_{B}}}^{-1/2})=I} . W 1 {\displaystyle W_{1}} whiten B {\displaystyle B} and reduces the dimensionality from m {\displaystyle m} to k {\displaystyle k} . The transformed space resided by q ′ = W 1 T X T s {\displaystyle q'=W_{1}^{T}X^{T}s} is called the noise space. Then, we diagonalize W 1 T A W 1 {\displaystyle W_{1}^{T}AW_{1}} : W 2 T W 1 T A W 1 W 2 = D A {\displaystyle W_{2}^{T}W_{1}^{T}AW_{1}W_{2}=D_{A}} , where W 2 T W 2 = I {\displaystyle W_{2}^{T}W_{2}=I} . D A {\displaystyle D_{A}} is the matrix with eigenvalues of W 1 T A W 1 {\displaystyle W_{1}^{T}AW_{1}} on its diagonal. We may retain all the eigenvalues and their corresponding eigenvectors since most of the noise are already discarded in previous step. Finally the transformation is given by: W = W 1 W 2 {\displaystyle W=W_{1}W_{2}} where W {\displaystyle W} diagonalizes both the numerator and denominator of the SNR, W T A W = D A {\displaystyle W^{T}AW=D_{A}} , W T B W = I {\displaystyle W^{T}BW=I} and the transformation of signal s {\displaystyle s} is defined as q = W T X T s = W 2 T W 1 T X T s {\displaystyle q=W^{T}X^{T}s=W_{2}^{T}W_{1}^{T}X^{T}s} . === Information loss === To find the information loss when we discard some of the eigenvalues and eigenvectors we can perform following analysis: η = 1 − t r a c e ( W 1 T A W 1 ) t r a c e ( D B − 1 / 2 P T A P D B − 1 / 2 ) = 1 − t r a c e ( D B ^ − 1 / 2 P ^ T A P ^ D B ^ − 1 / 2 ) t r a c e ( D B − 1 / 2 P T A P D B − 1 / 2 ) {\displaystyle {\begin{array}{lll}\eta &=&
Grammar induction
Grammar induction (or grammatical inference) is the process in machine learning of learning a formal grammar (usually as a collection of re-write rules or productions or alternatively as a finite-state machine or automaton of some kind) from a set of observations, thus constructing a model which accounts for the characteristics of the observed objects. More generally, grammatical inference is that branch of machine learning where the instance space consists of discrete combinatorial objects such as strings, trees and graphs. == Grammar classes == Grammatical inference has often been very focused on the problem of learning finite-state machines of various types (see the article Induction of regular languages for details on these approaches), since there have been efficient algorithms for this problem since the 1980s. Since the beginning of the century, these approaches have been extended to the problem of inference of context-free grammars and richer formalisms, such as multiple context-free grammars and parallel multiple context-free grammars. Other classes of grammars for which grammatical inference has been studied are combinatory categorial grammars, stochastic context-free grammars, contextual grammars and pattern languages. == Learning models == The simplest form of learning is where the learning algorithm merely receives a set of examples drawn from the language in question: the aim is to learn the language from examples of it (and, rarely, from counter-examples, that is, example that do not belong to the language). However, other learning models have been studied. One frequently studied alternative is the case where the learner can ask membership queries as in the exact query learning model or minimally adequate teacher model introduced by Angluin. == Methodologies == There is a wide variety of methods for grammatical inference. Two of the classic sources are Fu (1977) and Fu (1982). Duda, Hart & Stork (2001) also devote a brief section to the problem, and cite a number of references. The basic trial-and-error method they present is discussed below. For approaches to infer subclasses of regular languages in particular, see Induction of regular languages. A more recent textbook is de la Higuera (2010), which covers the theory of grammatical inference of regular languages and finite state automata. D'Ulizia, Ferri and Grifoni provide a survey that explores grammatical inference methods for natural languages. === Induction of probabilistic grammars === There are several methods for induction of probabilistic context-free grammars. === Grammatical inference by trial-and-error === The method proposed in Section 8.7 of Duda, Hart & Stork (2001) suggests successively guessing grammar rules (productions) and testing them against positive and negative observations. The rule set is expanded so as to be able to generate each positive example, but if a given rule set also generates a negative example, it must be discarded. This particular approach can be characterized as "hypothesis testing" and bears some similarity to Mitchel's version space algorithm. The Duda, Hart & Stork (2001) text provide a simple example which nicely illustrates the process, but the feasibility of such an unguided trial-and-error approach for more substantial problems is dubious. === Grammatical inference by genetic algorithms === Grammatical induction using evolutionary algorithms is the process of evolving a representation of the grammar of a target language through some evolutionary process. Formal grammars can easily be represented as tree structures of production rules that can be subjected to evolutionary operators. Algorithms of this sort stem from the genetic programming paradigm pioneered by John Koza. Other early work on simple formal languages used the binary string representation of genetic algorithms, but the inherently hierarchical structure of grammars couched in the EBNF language made trees a more flexible approach. Koza represented Lisp programs as trees. He was able to find analogues to the genetic operators within the standard set of tree operators. For example, swapping sub-trees is equivalent to the corresponding process of genetic crossover, where sub-strings of a genetic code are transplanted into an individual of the next generation. Fitness is measured by scoring the output from the functions of the Lisp code. Similar analogues between the tree structured lisp representation and the representation of grammars as trees, made the application of genetic programming techniques possible for grammar induction. In the case of grammar induction, the transplantation of sub-trees corresponds to the swapping of production rules that enable the parsing of phrases from some language. The fitness operator for the grammar is based upon some measure of how well it performed in parsing some group of sentences from the target language. In a tree representation of a grammar, a terminal symbol of a production rule corresponds to a leaf node of the tree. Its parent nodes corresponds to a non-terminal symbol (e.g. a noun phrase or a verb phrase) in the rule set. Ultimately, the root node might correspond to a sentence non-terminal. === Grammatical inference by greedy algorithms === Like all greedy algorithms, greedy grammar inference algorithms make, in iterative manner, decisions that seem to be the best at that stage. The decisions made usually deal with things like the creation of new rules, the removal of existing rules, the choice of a rule to be applied or the merging of some existing rules. Because there are several ways to define 'the stage' and 'the best', there are also several greedy grammar inference algorithms. These context-free grammar generating algorithms make the decision after every read symbol: Lempel-Ziv-Welch algorithm creates a context-free grammar in a deterministic way such that it is necessary to store only the start rule of the generated grammar. Sequitur and its modifications. These context-free grammar generating algorithms first read the whole given symbol-sequence and then start to make decisions: Byte pair encoding and its optimizations. === Distributional learning === A more recent approach is based on distributional learning. Algorithms using these approaches have been applied to learning context-free grammars and mildly context-sensitive languages and have been proven to be correct and efficient for large subclasses of these grammars. === Learning of pattern languages === Angluin defines a pattern to be "a string of constant symbols from Σ and variable symbols from a disjoint set". The language of such a pattern is the set of all its nonempty ground instances i.e. all strings resulting from consistent replacement of its variable symbols by nonempty strings of constant symbols. A pattern is called descriptive for a finite input set of strings if its language is minimal (with respect to set inclusion) among all pattern languages subsuming the input set. Angluin gives a polynomial algorithm to compute, for a given input string set, all descriptive patterns in one variable x. To this end, she builds an automaton representing all possibly relevant patterns; using sophisticated arguments about word lengths, which rely on x being the only variable, the state count can be drastically reduced. Erlebach et al. give a more efficient version of Angluin's pattern learning algorithm, as well as a parallelized version. Arimura et al. show that a language class obtained from limited unions of patterns can be learned in polynomial time. === Pattern theory === Pattern theory, formulated by Ulf Grenander, is a mathematical formalism to describe knowledge of the world as patterns. It differs from other approaches to artificial intelligence in that it does not begin by prescribing algorithms and machinery to recognize and classify patterns; rather, it prescribes a vocabulary to articulate and recast the pattern concepts in precise language. In addition to the new algebraic vocabulary, its statistical approach was novel in its aim to: Identify the hidden variables of a data set using real world data rather than artificial stimuli, which was commonplace at the time. Formulate prior distributions for hidden variables and models for the observed variables that form the vertices of a Gibbs-like graph. Study the randomness and variability of these graphs. Create the basic classes of stochastic models applied by listing the deformations of the patterns. Synthesize (sample) from the models, not just analyze signals with it. Broad in its mathematical coverage, pattern theory spans algebra and statistics, as well as local topological and global entropic properties. == Applications == The principle of grammar induction has been applied to other aspects of natural language processing, and has been applied (among many other problems) to semantic parsing, natural language understanding, example-based translation, language acquisition, grammar-based compre
WebCL
WebCL (Web Computing Language) is a JavaScript binding to OpenCL for heterogeneous parallel computing within any compatible web browser without the use of plug-ins, first announced in March 2011. It is developed on similar grounds as OpenCL and is considered as a browser version of the latter. Primarily, WebCL allows web applications to actualize speed with multi-core CPUs and GPUs. With the growing popularity of applications that need parallel processing like image editing, augmented reality applications and sophisticated gaming, it has become more important to improve the computational speed. With these background reasons, a non-profit Khronos Group designed and developed WebCL, which is a Javascript binding to OpenCL with a portable kernel programming, enabling parallel computing on web browsers, across a wide range of devices. In short, WebCL consists of two parts, one being Kernel programming, which runs on the processors (devices) and the other being JavaScript, which binds the web application to OpenCL. The completed and ratified specification for WebCL 1.0 was released on March 19, 2014. == Implementation == Currently, no browsers natively support WebCL. However, non-native add-ons are used to implement WebCL. For example, Nokia developed a WebCL extension. Mozilla does not plan to implement WebCL in favor of WebGL Compute Shaders, which were in turn scrapped in favor of WebGPU. Mozilla (Firefox) - hg.mozilla.org/projects/webcl/ === WebCL working draft === Samsung (WebKit) - github.com/SRA-SiliconValley/webkit-webcl (unavailable) Nokia (Firefox) - github.com/toaarnio/webcl-firefox (down since Nov 2014, Last Version for FF 34) Intel (Crosswalk) - www.crosswalk-project.org === Example C code === The basic unit of a parallel program is kernel. A kernel is any parallelizable task used to perform a specific job. More often functions can be realized as kernels. A program can be composed of one or more kernels. In order to realize a kernel, it is essential that a task is parallelizable. Data dependencies and order of execution play a vital role in producing efficient parallelized algorithms. A simple example can be thought of the case of loop unrolling performed by C compilers, where a statement like:can be unrolled into:Above statements can be parallelized and can be made to run simultaneously. A kernel follows a similar approach where only the snapshot of the ith iteration is captured inside kernel. Rewriting the above code using a kernel:Running a WebCL application involves the following steps: Allow access to devices and provide context Hand over the kernel to a device Cause the device to execute the kernel Retrieve results from the device Use the data inside JavaScript Further details about the same can be found at == Exceptions List == WebCL, being a JavaScript based implementation, doesn't return an error code when errors occur. Instead, it throws an exception such as OUT_OF_RESOURCES, OUT_OF_HOST_MEMORY, or the WebCL-specific WEBCL_IMPLEMENTATION_FAILURE. The exception object describes the machine-readable name and human-readable message describing the error. The syntax is as follows: From the code above, it can be observed that the message field can be a NULL value. Other exceptions include: INVALID_OPERATION – if the blocking form of this function is called from a WebCLCallback INVALID_VALUE – if eventWaitList is empty INVALID_CONTEXT – if events specified in eventWaitList do not belong to the same context INVALID_DEVICE_TYPE – if deviceType is given, but is not one of the valid enumerated values DEVICE_NOT_FOUND – if there is no WebCLDevice available that matches the given deviceType More information on exceptions can be found in the specs document. There is another exception that is raised upon trying to call an object that is ‘released’. On using the release method, the object doesn't get deleted permanently but it frees the resources associated with that object. In order to avoid this exception, releaseAll method can be used, which not only frees the resources but also deletes all the associated objects created. == Security == WebCL, being an open-ended software developed for web applications, has lots of scope for vulnerabilities in the design and development fields too. This forced the developers working on WebCL to give security the utmost importance. Few concerns that were addressed are: Out-of-bounds Memory Access: This occurs by accessing the memory locations, outside the allocated space. An attacker can rewrite or erase all the important data stored in those memory locations. Whenever there arises such a case, an error must be generated at the compile time, and zero must be returned at run-time, not letting the program override the memory. A project WebCL Validator, was initiated by the Khronos Group (developers) on handling this vulnerability. Memory Initialization: This is done to prevent the applications to access the memory locations of previous applications. WebCL ensures that this doesn't happen by initializing all the buffers, variables used to zero before it runs the current application. OpenCL 1.2 has an extension ‘cl_khr_initialize_memory’, which enables this. Denial of Service: The most common attack on web applications cannot be eliminated by WebCL or the browser. OpenCL can be provided with watchdog timers and pre-emptive multitasking, which can be used by WebCL in order to detect and terminate the contexts that are taking too long or consume lot of resources. There is an extension of OpenCL 1.2 ‘cl_khr_terminate_context’ like for the previous one, which enables to terminate the process that might cause a denial of service attack. == Related browser bugs == Bug 664147 - [WebCL] add openCL in gecko, Mozilla Bug 115457: [Meta] WebCL support for WebKit, WebKit Bugzilla
Content strategy
Content strategy guides the planning, development, and management of content. It is a recognized field in user experience design, and it also draws from adjacent disciplines such as information architecture, content management, business analysis, digital marketing, and technical communication. == Definitions == Content strategy has been described as planning for "the creation, publication, and governance of useful, usable content." It has also been called "a repeatable system that defines the entire editorial content development process for a website development project." In a 2007 article titled "Content Strategy: The Philosophy of Data," Rachel Lovinger describes the goal of content strategy as using "words and data to create unambiguous content that supports meaningful, interactive experiences." Here, she also provided the analogy that "content strategy is to copywriting as information architecture is to design." She encourages content strategists and collaborators to engage in early discussions about content meaning, models, and tools, to make sure strategy is integrated from the start rather than as an afterthought. The Content Strategy Alliance combines Kevin Nichols' definition with Kristina Halvorson's and defines content strategy as "getting the right content to the right user at the right time through strategic planning of content creation, delivery, and governance." == Practitioners == Content strategists are often familiar with a wide range of approaches, techniques, and tools. The perspectives that content strategists bring also depend heavily on their professional training and education. For instance, some specialize in "front-end strategy," which includes developing personas, journey mapping the user experience, aligning business strategy and user needs, developing a brand strategy, exploring different channels, and creating style guidelines and search engine optimization (SEO) guidelines. Others specialize in "back-end strategy," which includes creating content models, planning taxonomies and metadata, structuring content management systems, and building systems to support content reuse. Both roles involve addressing workflow and governance issues. Many organizations and individuals tend to confuse content strategists with editors. However, content strategy is "about more than just the written word," according to Washington State University associate professor Brett Atwood. For example, Atwood indicates that a practitioner needs to also "consider how content might be re-distributed and/or re-purposed in other channels of delivery." It has also been proposed that the content strategist performs the role of a curator. Just as a museum curator sifts through a collection of content and identifies key pieces that can be juxtaposed against each other to create meaning and spur excitement, a content strategist "must approach a business’s content as a medium that needs to be strategically selected and placed to engage the audience, convey a message, and inspire action."
Macroelectronics
Macroelectronics are flexible electronics that cover a large area. The most visible example of macroelectronics is flat-panel displays. Other emerging applications include rollable display, printable thin film solar cell and electronic skin. Flat-panel displays fabricated on glass substrates are fragile so fabricating directly on flexible substrates, such as polymers is being explored. Displays made on thin polymer substrates can be more rugged than glass. In September 2005, Philips Polymer Vision revealed the world's first prototype of a rollable electronic reader, which can unfold to a 5-inch display and roll back into a pocket-size (100×60×20 mm) device. Thin-film devices on flexible polymer substrates can lend themselves to low-cost fabrication processes (i.e., roll-to-roll printing), resulting in lightweight, rugged and flexible macroelectronic products.
ReRites
ReRites (also known as RERITES, ReadingRites, Big Data Poetry) is a literary work of "Human + A.I. poetry" by David Jhave Johnston that used neural network models trained to generate poetry which the author then edited. ReRites won the Robert Coover Award for a Work of Electronic Literature in 2022. == About the project == The ReRites project began as a daily rite of writing with a neural network, expanded into a series of performances from which video documentation has been published online, and concluded with a set of 12 books and an accompanying book of essays published by Anteism Books in 2019. In Electronic Literature, Scott Rettberg describes the early phases of the project in 2016, when it bore the preliminary name Big Data Poetry. Jhave (the artist name that David Jhave Johnston goes by) describes the process of writing ReRites as a rite: "Every morning for 2 hours (normally 6:30–8:30am) I get up and edit the poetic output of a neural net. Deleting, weaving, conjugating, lineating, cohering. Re-writing. Re-wiring authorship: hybrid augmented enhanced evolutionary". There is video documentation of the writing process. The human editing of the neural network's output is fundamental to this project, and Jhave gives examples of both unedited text extracts and his edited versions in publications about the project. Kyle Booten describes ReRites as "simultaneously dusty and outrageously verdant, monotonously sublime and speckled with beautiful and rare specimens". === Performances === ReRites was first shared with an audience through a series of performances where audience members and poets would participate in reading the automatically generated texts, which appeared on screen so fast that human readers could barely keep up. This has been described as allowing participants to "re-discover[..] the peculiar pleasures of being embodied", or, in Jhave's own words, as a space where human participants were "playing their wits and voices against an evocative infinite deep-learning muse". The first performance was at Brown University's Interrupt Festival in 2019. It has been performed many times since, including at the Barbican Centre in London and Anteism Books. === Print publications === For a single year Jhave published one book of poetry from the ReRites project each month. These twelve volumes are accompanied by a book of essays, all published by Anteism Books. The accompanying essays provide critical responses to the project from poets and scholars including Allison Parrish, Johanna Drucker, Kyle Booten, Stephanie Strickland, John Cayley, Lai-Tze Fan, Nick Montfort, Mairéad Byrne, and Chris Funkhouser. Allison Parrish notes elsewhere that these paratexts to ReRites serve a legitimising function for a genre of poetry that is not yet institutionally acknowledged. === Technical details === Starting in 2016 under the name Big Data Poetry, Jhave generated poems using, in his own words, "neural network code (..) adapted from three corporate github-hosted machine-learning libraries: TensorFlow (Google), PyTorch (Facebook), and AWD-LSTM (SalesForce)". He explains that the "models were trained on a customised corpus of 600,000 lines of poetry ranging from the romantic epoch to the 20th century avant garde". Jhave maintains a GitHub repository with some of the code supporting ReRites. == Reception == ReRites is described by John Cayley as "one of the most thorough and beautiful" poetic responses to machine learning. The work's influence on the field of electronic literature was acknowledged in 2022, when the work won the Electronic Literature Organization's Robert Coover Award for a Work of Electronic Literature. The jury described ReRites as particularly poignant in the time of the pandemic, as it was "a documentation of the performance of the private ritual of writing and the obsessive-compulsive need for writers to communicate — even when no one else is reading". The question of authorship and voice in ReRites has been raised by several critics. Although generated poetry is an established genre in electronic literature, Cayley notes that unlike the combinatory poems created by authors like Nick Montfort, where the author explicitly defines which words and phrases will be recombined, ReRites has "not been directed by literary preconceptions inscribed in the program itself, but only by patterns and rhythms pre-existing in the corpora". In an essay for the Australian journal TEXT, David Thomas Henry Wright asks how to understand authorship and authority in ReRites: "Who or what is the authority of the work? The original data fed into the machine, that is not currently retrievable or discernible from the final works? The code that was taken and adapted for his purposes? Or Jhave, the human editor?" Wright concludes that Jhave is the only actor with any intentionality and therefore the authority of the work. The centrality of the human editor is also emphasised by other scholars. In a chapter analysing ReRites Malthe Stavning Erslev argues that the machine learning misrepresents the dataset it is trained on. While ReRites uses 21st century neural networks, it has been compared to earlier literary traditions. Poet Victoria Stanton, who read at one of the ReRites performances, has compared ReRites to found poetry, while David Thomas Henry Wright compares it to the Oulipo movement and Mark Amerika to the cut-up technique. Scholars also position ReRites firmly within the long tradition of generative poetry both in electronic literature and print, stretching from the I Ching, Queneau's Cent Mille Milliards de Poemes and Nabokov's Pale Fire to computer-generated poems like Christopher Strachey's Love Letter Generator (1952) and more contemporary examples. Jhave describes the process of working with the output from the neural network as "carving". In his book My Life as an Artificial Creative Intelligence, Mark Amerika writes that the "method of carving the digital outputs provided by the language model as part of a collaborative remix jam session with GPT-2, where the language artist and the language model play off each other’s unexpected outputs as if caught in a live postproduction set, is one I share with electronic literature composer David Jhave Johnston, whose AI poetry experiments precede my own investigations."
Are We Dating The Same Guy?
Are We Dating The Same Guy?, also abbreviated AWDTSG is a series of over 200 individual Facebook groups where women share dating profiles of men they matched with on dating networks to seek the opinion of other women who may have dated the same man in the past. The first group was created by Paola Sanchez and aimed at women living in the New York City environs. The groups have over 3.5 million members as of January 2024. The group's function is to post screenshots of a man's dating profile to that city's designated Facebook group, after which the poster asks "any tea?". Other users in the group will then share information about the man and share warnings. The groups are moderated by volunteers, and have been described as a feminist group. The groups have rules saying that personal information such as addresses must not be included in the Facebook posts. Users attempting to join the group are also examined to prevent fake profiles. The group is mainly for straight women. According to Vice, the men being posted about have no way to defend against accusations made about them, and on the other hand, posters cannot prove their stories unless backed up by others. Often times, members post pictures alongside personal information such as names, which may infringe on subjects' legal right to privacy. Lawyers have said these issues can lead to defamation lawsuits, and members can make false allegations and create fabricated stories. If members tell a man that he's been talked about on the group, the "snitch" will be banned and be "exposed to the whole group". == History == The first Are We Dating The Same Guy group was created by Paola Sanchez. The first group was created in March 2022 in New York City. A male counterpart, named "Are We Dating the Same Girl NYC" was created for New York, with mostly the same guidelines and rules to the original. When the original Are We Dating The Same Guy group found it, they denounced the new group. == Operations == Administrators are told not to respond to men asking to have posts about them removed, and to not remove said posts. The people being posted about have reported being questioned by their employers about things they have not done. Members of the groups sometimes criticise the physical appearance of the men being posted about. According to the Evening Standard, the groups "frequent[ly] mock" the appearance or dating profiles of the men who are posted about, despite being against the rules. For this behaviour, women are sometimes kicked out, or the group is disciplined en masse by admins. The groups have rules against hate towards men, but the rules can be difficult to enforce in large groups, with some having over 100,000 members. Some men have also been able to join the groups without being noticed. == Reception == In October 2023, Sera Bozza of Body+Soul wrote that consistently using Are We Dating The Same Guy can "affect your real-world view". She wrote that "A few stories of cheating may persuade you to believe that all men are unfaithful". Some lawyers and commentators have expressed concern that the groups fail to acknowledge the legal right to privacy and users can create false allegations and fabricated stories, and cyberbully men without them being able to defend themselves. This may lead to civil lawsuits against the author for defamation, harassment, and other related privacy torts. Netsafe, an online safety organisation in New Zealand, advises users of a similar group to familiarise themselves with the Harmful Digital Communications Act to ensure that posts do not lead to "harmful consequences". The Independent reported that men who have been posted on the dating groups have felt violated, and that even if reviewed positively by potentially thousands of strangers, the men being discussed about may have their reputation slightly decreased due to the association with being on the groups. The Independent also reported that some men believe that the groups are created to spread lies or mock them. Mashable reported that the growth of AWDTSG in recent years has led to the rise of a small industry of online reputation and content removal services, as increasing numbers of men seek assistance. A co-founder of Maximatic Media, one such agency offering these removal services, stated that many of the men contacting the firm do so in a state of panic after learning that allegations about them have circulated among tens of thousands of participants without their knowledge. Mashable similarly reported that the growing visibility of AWDTSG and similar platforms has contributed to what commentators describe as a "public trial" dynamic, where subjective accounts about dating behavior are interpreted as factual assessments and can influence a person's reputation among large audiences within their locale. The Oklahoman reported that anonymous, unverified claims in these groups have led some men to experience social and dating repercussions, although legal analysts argue that the benefits of community-based safety networks still outweigh these concerns in modern, app based dating environments. UTV/ITV News reportedly spoke to a man who was posted who alleged he attempted suicide, was clinically dead for three minutes, and spent three weeks in a psychiatric hospital as a result of the posts made about him. Many other men have talked about malicious false claims made about them. Self-described men’s rights activists have taken a dislike to these groups and have gotten multiple North American groups shut down by running campaigns, threatening lawsuits, and mass Facebook reporting. They also have Reddit communities dedicated to getting rid of such groups. Women who have posted in the groups have felt that they have put their safety at risk, with some having been confronted by the men they posted about. The group has been noted for exposing men who use dating apps while already in a relationship, misrepresent their ages, or repeatedly stand up the women they meet through apps, among other bad dating behaviors. For example, some members of the group had matched on a dating site with a man who had, several years prior, killed a stranger while having a mental break. After this information came to light, members of the group were warned. The group has also been noted to be complimentary of some men. == Lawsuits == In 2023, a 41-year-old man sued the administrators of the London group for $35,000 under defamation, alleging that the group "called names, accused of sending lewd photos and of being a bad parent". In January 2024 a man sued Meta, the owner of Facebook, along with Patreon, GoFundMe, and the AWDTSG website, as well as almost 30 group members due to alleged defamation, emotional distress, and invasion of privacy. Claiming that the groups violate anti-doxxing laws and do not fact check, seeking $75,000 in damages. He claims that the group shared fake images of him sending women texts containing harassment, his name and photo. His attorneys claim that if the images were real, they would fall under free speech in the First Amendment. By February, groups had raised $80,000. The Washington Post said that this case caused AWDTSG to "explode into public view". The case was dismissed in 2025 by the United States District Court for the Northern District of Illinois. On May 15, 2026, the United States Court of Appeals for the Seventh Circuit declined to renew the case in D'Ambrosio v. Meta Platforms Inc., et al. The plaintiff and his attorneys, Marc Trent and Aaron Walner of Trent Law Firm, were sanctioned "for frivolously appealing the dismissal of the claims," "misrepresentations of law," in connection with falsified citations included in the plaintiff's brief, and " disputing at oral argument without any evidentiary basis that [the plaintiff] client sent the text message she attributed to him." == By country == === Australia === In Australia, there are groups for multiple cities including Sydney, Melbourne, Adelaide, Perth, Brisbane and Rockhampton with many having several thousand members. The Sydney group has 30,000 members. In March 2023, the Adelaide version of the group, which had 7,000 members, was shut down. In 2024, groups titled "Sis, Are We Dating The Same Guy" stopped accepting new posts after an admin was sued for defamation and had to pay over AU$20,000 in legal fees. The case was settled out of court. The administrator announcing these closures cited a 2021 defamation High Court case involving detainee Dylan Voller, which led to the High Court saying that owners of Facebook groups can be held liable for defamatory comments, even if they did not know the comments had been made. === Canada === In 2023, a group was started for Ottawa. The founder previously was in a relationship full of "cheating and lies", which prompted her to creating the Facebook community. In 2023, the group for Vancouver and British Columbia was shut down after concerns about men being unable to protect themselves against fa