Fuzzy logic is a form of many-valued logic in which the truth value of variables may be any real number between 0 and 1. It is employed to handle the concept of partial truth, where the truth value may range between completely true and completely false. By contrast, in Boolean logic, the truth values of variables may only be the integer values 0 or 1. The term fuzzy logic was introduced with the 1965 proposal of fuzzy set theory by mathematician Lotfi Zadeh. Basic fuzzy logic had, however, been studied since the 1920s, as infinite-valued logic—notably by Łukasiewicz and Tarski. The works of Zadeh and Joseph Goguen in the 1960s and 1970s went further by considering issues such as linguistic variables and lattices. Fuzzy logic is based on the observation that people make decisions based on imprecise and non-numerical information. Fuzzy models or fuzzy sets are mathematical means of representing vagueness and imprecise information (hence the term fuzzy). These models have the capability of recognising, representing, manipulating, interpreting, and using data and information that are vague and lack certainty. Fuzzy logic has been applied to many fields, from control theory to artificial intelligence. == Overview == Classical logic only permits conclusions that are either true or false. However, there are also propositions with variable answers, which one might find when asking a group of people to identify a color. In such instances, the truth appears as the result of reasoning from inexact or partial knowledge in which the sampled answers are mapped on a spectrum. Both degrees of truth and probabilities range between 0 and 1 and hence may seem identical at first, but fuzzy logic uses degrees of truth as a mathematical model of vagueness, while probability is a mathematical model of ignorance. === Applying truth values === A basic application might characterize various sub-ranges of a continuous variable. For instance, a temperature measurement for anti-lock brakes might have several separate membership functions defining particular temperature ranges needed to control the brakes properly. Each function maps the same temperature value to a truth value in the 0 to 1 range. These truth values can then be used to determine how the brakes should be controlled. Fuzzy set theory provides a means for representing uncertainty. === Linguistic variables === In fuzzy logic applications, non-numeric values are often used to facilitate the expression of rules and facts. A linguistic variable such as age may accept values such as young and its antonym old. Because natural languages do not always contain enough value terms to express a fuzzy value scale, it is common practice to modify linguistic values with adjectives or adverbs. For example, we can use the hedges rather and somewhat to construct the additional values rather old or somewhat young. == Fuzzy systems == === Mamdani === The most well-known system is the Mamdani rule-based one. It uses the following rules: Fuzzify all input values into fuzzy membership functions. Execute all applicable rules in the rulebase to compute the fuzzy output functions. De-fuzzify the fuzzy output functions to get "crisp" output values. ==== Fuzzification ==== Fuzzification is the process of assigning the numerical input of a system to fuzzy sets with some degree of membership. This degree of membership may be anywhere within the interval [0,1]. If it is 0 then the value does not belong to the given fuzzy set, and if it is 1 then the value completely belongs within the fuzzy set. Any value between 0 and 1 represents the degree of uncertainty that the value belongs in the set. These fuzzy sets are typically described by words, and so by assigning the system input to fuzzy sets, we can reason with it in a linguistically natural manner. For example, in the image below, the meanings of the expressions cold, warm, and hot are represented by functions mapping a temperature scale. A point on that scale has three "truth values"—one for each of the three functions. The vertical line in the image represents a particular temperature that the three arrows (truth values) gauge. Since the red arrow points to zero, this temperature may be interpreted as "not hot"; i.e. this temperature has zero membership in the fuzzy set "hot". The orange arrow (pointing at 0.2) may describe it as "slightly warm" and the blue arrow (pointing at 0.8) "fairly cold". Therefore, this temperature has 0.2 membership in the fuzzy set "warm" and 0.8 membership in the fuzzy set "cold". The degree of membership assigned for each fuzzy set is the result of fuzzification. Fuzzy sets are often defined as triangle or trapezoid-shaped curves, as each value will have a slope where the value is increasing, a peak where the value is equal to 1 (which can have a length of 0 or greater) and a slope where the value is decreasing. They can also be defined using a sigmoid function. One common case is the standard logistic function defined as S ( x ) = 1 1 + e − x {\displaystyle S(x)={\frac {1}{1+e^{-x}}}} which has the following symmetry property S ( x ) + S ( − x ) = 1. {\displaystyle S(x)+S(-x)=1.} From this it follows that ( S ( x ) + S ( − x ) ) ⋅ ( S ( y ) + S ( − y ) ) ⋅ ( S ( z ) + S ( − z ) ) = 1 {\displaystyle (S(x)+S(-x))\cdot (S(y)+S(-y))\cdot (S(z)+S(-z))=1} ==== Fuzzy logic operators ==== Fuzzy logic works with membership values in a way that mimics Boolean logic. To this end, replacements for basic operators ("gates") AND, OR, NOT must be available. There are several ways to accomplish this. A common replacement is called the Zadeh operators: For TRUE/1 and FALSE/0, the fuzzy expressions produce the same result as the Boolean expressions. There are also other operators, more linguistic in nature, called hedges that can be applied. These are generally adverbs such as very, or somewhat, which modify the meaning of a set using a mathematical formula. However, an arbitrary choice table does not always define a fuzzy logic function. In the paper (Zaitsev, et al), a criterion has been formulated to recognize whether a given choice table defines a fuzzy logic function and a simple algorithm of fuzzy logic function synthesis has been proposed based on introduced concepts of constituents of minimum and maximum. A fuzzy logic function represents a disjunction of constituents of minimum, where a constituent of minimum is a conjunction of variables of the current area greater than or equal to the function value in this area (to the right of the function value in the inequality, including the function value). Another set of AND/OR operators is based on multiplication, where Given any two of AND/OR/NOT, it is possible to derive the third. The generalization of AND is an instance of a t-norm. ==== IF-THEN rules ==== IF-THEN rules map input or computed truth values to desired output truth values. Example: Given a certain temperature, the fuzzy variable hot has a certain truth value, which is copied to the high variable. Should an output variable occur in several THEN parts, the values from the respective IF parts are combined using the OR operator. ==== Defuzzification ==== The goal is to get a continuous variable from fuzzy truth values. This would be easy if the output truth values were exactly those obtained from fuzzification of a given number. Since, however, all output truth values are computed independently, in most cases they do not represent such a set of numbers. One has then to decide for a number that matches best the "intention" encoded in the truth value. For example, for several truth values of fan_speed, an actual speed must be found that best fits the computed truth values of the variables 'slow', 'moderate' and so on. There is no single algorithm for this purpose. A common algorithm is For each truth value, cut the membership function at this value Combine the resulting curves using the OR operator Find the center-of-weight of the area under the curve The x position of this center is then the final output. === Takagi–Sugeno–Kang (TSK) === The Takagi–Sugeno or Takagi–Sugeno–Kang (TSK) system was introduced by Tomohiro Takagi and Michio Sugeno for fuzzy identification of systems and applications to modeling and control. Sugeno and Kang later developed methods for structure identification of such fuzzy models from input-output data. The TSK system is similar to Mamdani, but the defuzzification process is included in the execution of the fuzzy rules. These are also adapted, so that instead the consequent of the rule is represented through a polynomial function, usually constant in a zero-order model or linear in a first-order model. An example of a rule with a constant output would be: In this case, the output will be equal to the constant of the consequent (e.g. 2). In most scenarios we would have an entire rule base, with 2 or more rules. If this is the case, the output of the entire rule base will be the average of the consequent of each rule i (Y
Universal IR Evaluation
In computer science, Universal IR Evaluation (information retrieval evaluation) aims to develop measures of database retrieval performance that shall be comparable across all information retrieval tasks. == Measures of "relevance" == IR (information retrieval) evaluation begins whenever a user submits a query (search term) to a database. If the user is able to determine the relevance of each document in the database (relevant or not relevant), then for each query, the complete set of documents is naturally divided into four distinct (mutually exclusive) subsets: relevant documents that are retrieved, not relevant documents that are retrieved, relevant documents that are not retrieved, and not relevant documents that are not retrieved. These four subsets (of documents) are denoted by the letters a, b, c, d respectively and are called Swets variables, named after their inventor. In addition to the Swets definitions, four relevance metrics have also been defined: Recall refers to the fraction of relevant documents that are retrieved (a/(a+b)), and Precision refers to the fraction of retrieved documents that are relevant (a/(a+c)). These are the most commonly used and well-known relevance metrics found in the IR evaluation literature. Two less commonly used metrics include the Fallout, i.e., the fraction of not relevant documents that are retrieved (b/(b+d)), and the Miss, which refers to the fraction of relevant documents that are not retrieved (c/(c+d)) during any given search. == Universal IR evaluation techniques == Universal IR evaluation addresses the mathematical possibilities and relationships among the four relevance metrics Precision, Recall, Fallout and Miss, denoted by P, R, F and M, respectively. One aspect of the problem involves finding a mathematical derivation of a complete set of universal IR evaluation points. The complete set of 16 points, each one a quadruple of the form (P, R, F, M), describes all the possible universal IR outcomes. For example, many of us have had the experience of querying a database and not retrieving any documents at all. In this case, the Precision would take on the undetermined form 0/0, the Recall and Fallout would both be zero, and the Miss would be any value greater than zero and less than one (assuming a mix of relevant and not relevant documents were in the database, none of which were retrieved). This universal IR evaluation point would thus be denoted by (0/0, 0, 0, M), which represents only one of the 16 possible universal IR outcomes. The mathematics of universal IR evaluation is a fairly new subject since the relevance metrics P, R, F, M were not analyzed collectively until recently (within the past decade). A lot of the theoretical groundwork has already been formulated, but new insights in this area await discovery.
Dr. Sbaitso
Dr. Sbaitso ( SPAYT-soh) is an artificial intelligence speech synthesis program released late in 1991 by Creative Labs in Singapore for MS-DOS-based personal computers. The name is an acronym for "SoundBlaster Acting Intelligent Text-to-Speech Operator." == History == Dr. Sbaitso was distributed with various sound cards manufactured by Creative Technology in the early 1990s. The text-to-speech engine used is a version of Monologue, which was developed by First Byte Software. Monologue is a later release of First Byte's "SmoothTalker" software from 1984. The program "conversed" with the user as if it were a psychologist, though most of its responses were along the lines of "WHY DO YOU FEEL THAT WAY?" rather than any sort of complicated interaction. When confronted with a phrase it could not understand, it would often reply with something such as "THAT'S NOT MY PROBLEM." Dr. Sbaitso repeated text out loud that was typed after the word "SAY." Repeated swearing or abusive behavior on the part of the user caused Dr. Sbaitso to "break down" in a "PARITY ERROR" before resetting itself. The same would happen, if the user types "SAY PARITY." The program introduced itself with the following lines: HELLO [UserName], MY NAME IS DOCTOR SBAITSO. I AM HERE TO HELP YOU. SAY WHATEVER IS IN YOUR MIND FREELY, OUR CONVERSATION WILL BE KEPT IN STRICT CONFIDENCE. MEMORY CONTENTS WILL BE WIPED OFF AFTER YOU LEAVE, SO, TELL ME ABOUT YOUR PROBLEMS. The program was designed to showcase the digitized voices the cards were able to produce, though the quality was far from lifelike. Additionally, there was a version of this program for Microsoft Windows through the use of a program called Prody Parrot; this version of the software featured a more detailed graphical user interface. The text-to-speech was also used as the voice of 1st Prize from the Baldi's Basics series, albeit slowed down. == Commands == If the user submits "HELP", a list of commands will appear. If the user then submits "M", more commands will appear. There are three pages of commands in total, with guidance on how to use each of the features.
Neural style transfer
Neural style transfer (NST) software algorithms are able to manipulate digital images, or videos, in order to adopt the appearance or visual style of another image. NST algorithms are characterized by their use of deep neural networks for the sake of image transformation. Common uses for NST are the creation of artificial artwork from photographs, for example by transferring the appearance of famous paintings to user-supplied photographs. Several notable mobile apps use NST techniques for this purpose, including DeepArt and Prisma. This method has been used by artists and designers around the globe to develop new artwork based on existent style(s). == History == NST is an example of image stylization, a problem studied for over two decades within the field of non-photorealistic rendering. The first two example-based style transfer algorithms were image analogies and image quilting. Both of these methods were based on patch-based texture synthesis algorithms. Given a training pair of images–a photo and an artwork depicting that photo–a transformation could be learned and then applied to create new artwork from a new photo, by analogy. If no training photo was available, it would need to be produced by processing the input artwork; image quilting did not require this processing step, though it was demonstrated on only one style. NST was first published in the paper "A Neural Algorithm of Artistic Style" by Leon Gatys et al., originally released to ArXiv 2015, and subsequently accepted by the peer-reviewed CVPR conference in 2016. The original paper used a VGG-19 architecture that has been pre-trained to perform object recognition using the ImageNet dataset. In 2017, Google AI introduced a method that allows a single deep convolutional style transfer network to learn multiple styles at the same time. This algorithm permits style interpolation in real-time, even when done on video media. == Mathematics == This section closely follows the original paper. === Overview === The idea of Neural Style Transfer (NST) is to take two images—a content image p → {\displaystyle {\vec {p}}} and a style image a → {\displaystyle {\vec {a}}} —and generate a third image x → {\displaystyle {\vec {x}}} that minimizes a weighted combination of two loss functions: a content loss L content ( p → , x → ) {\displaystyle {\mathcal {L}}_{\text{content }}({\vec {p}},{\vec {x}})} and a style loss L style ( a → , x → ) {\displaystyle {\mathcal {L}}_{\text{style }}({\vec {a}},{\vec {x}})} . The total loss is a linear sum of the two: L NST ( p → , a → , x → ) = α L content ( p → , x → ) + β L style ( a → , x → ) {\displaystyle {\mathcal {L}}_{\text{NST}}({\vec {p}},{\vec {a}},{\vec {x}})=\alpha {\mathcal {L}}_{\text{content}}({\vec {p}},{\vec {x}})+\beta {\mathcal {L}}_{\text{style}}({\vec {a}},{\vec {x}})} By jointly minimizing the content and style losses, NST generates an image that blends the content of the content image with the style of the style image. Both the content loss and the style loss measures the similarity of two images. The content similarity is the weighted sum of squared-differences between the neural activations of a single convolutional neural network (CNN) on two images. The style similarity is the weighted sum of Gram matrices within each layer (see below for details). The original paper used a VGG-19 CNN, but the method works for any CNN. === Symbols === Let x → {\textstyle {\vec {x}}} be an image input to a CNN. Let F l ∈ R N l × M l {\textstyle F^{l}\in \mathbb {R} ^{N_{l}\times M_{l}}} be the matrix of filter responses in layer l {\textstyle l} to the image x → {\textstyle {\vec {x}}} , where: N l {\textstyle N_{l}} is the number of filters in layer l {\textstyle l} ; M l {\textstyle M_{l}} is the height times the width (i.e. number of pixels) of each filter in layer l {\textstyle l} ; F i j l ( x → ) {\textstyle F_{ij}^{l}({\vec {x}})} is the activation of the i th {\textstyle i^{\text{th}}} filter at position j {\textstyle j} in layer l {\textstyle l} . A given input image x → {\textstyle {\vec {x}}} is encoded in each layer of the CNN by the filter responses to that image, with higher layers encoding more global features, but losing details on local features. === Content loss === Let p → {\textstyle {\vec {p}}} be an original image. Let x → {\textstyle {\vec {x}}} be an image that is generated to match the content of p → {\textstyle {\vec {p}}} . Let P l {\textstyle P^{l}} be the matrix of filter responses in layer l {\textstyle l} to the image p → {\textstyle {\vec {p}}} . The content loss is defined as the squared-error loss between the feature representations of the generated image and the content image at a chosen layer l {\displaystyle l} of a CNN: L content ( p → , x → , l ) = 1 2 ∑ i , j ( A i j l ( x → ) − A i j l ( p → ) ) 2 {\displaystyle {\mathcal {L}}_{\text{content }}({\vec {p}},{\vec {x}},l)={\frac {1}{2}}\sum _{i,j}\left(A_{ij}^{l}({\vec {x}})-A_{ij}^{l}({\vec {p}})\right)^{2}} where A i j l ( x → ) {\displaystyle A_{ij}^{l}({\vec {x}})} and A i j l ( p → ) {\displaystyle A_{ij}^{l}({\vec {p}})} are the activations of the i th {\displaystyle i^{\text{th}}} filter at position j {\displaystyle j} in layer l {\displaystyle l} for the generated and content images, respectively. Minimizing this loss encourages the generated image to have similar content to the content image, as captured by the feature activations in the chosen layer. The total content loss is a linear sum of the content losses of each layer: L content ( p → , x → ) = ∑ l v l L content ( p → , x → , l ) {\displaystyle {\mathcal {L}}_{\text{content }}({\vec {p}},{\vec {x}})=\sum _{l}v_{l}{\mathcal {L}}_{\text{content }}({\vec {p}},{\vec {x}},l)} , where the v l {\displaystyle v_{l}} are positive real numbers chosen as hyperparameters. === Style loss === The style loss is based on the Gram matrices of the generated and style images, which capture the correlations between different filter responses at different layers of the CNN: L style ( a → , x → ) = ∑ l = 0 L w l E l , {\displaystyle {\mathcal {L}}_{\text{style }}({\vec {a}},{\vec {x}})=\sum _{l=0}^{L}w_{l}E_{l},} where E l = 1 4 N l 2 M l 2 ∑ i , j ( G i j l ( x → ) − G i j l ( a → ) ) 2 . {\displaystyle E_{l}={\frac {1}{4N_{l}^{2}M_{l}^{2}}}\sum _{i,j}\left(G_{ij}^{l}({\vec {x}})-G_{ij}^{l}({\vec {a}})\right)^{2}.} Here, G i j l ( x → ) {\displaystyle G_{ij}^{l}({\vec {x}})} and G i j l ( a → ) {\displaystyle G_{ij}^{l}({\vec {a}})} are the entries of the Gram matrices for the generated and style images at layer l {\displaystyle l} . Explicitly, G i j l ( x → ) = ∑ k F i k l ( x → ) F j k l ( x → ) {\displaystyle G_{ij}^{l}({\vec {x}})=\sum _{k}F_{ik}^{l}({\vec {x}})F_{jk}^{l}({\vec {x}})} Minimizing this loss encourages the generated image to have similar style characteristics to the style image, as captured by the correlations between feature responses in each layer. The idea is that activation pattern correlations between filters in a single layer captures the "style" on the order of the receptive fields at that layer. Similarly to the previous case, the w l {\displaystyle w_{l}} are positive real numbers chosen as hyperparameters. === Hyperparameters === In the original paper, they used a particular choice of hyperparameters. The style loss is computed by w l = 0.2 {\displaystyle w_{l}=0.2} for the outputs of layers conv1_1, conv2_1, conv3_1, conv4_1, conv5_1 in the VGG-19 network, and zero otherwise. The content loss is computed by w l = 1 {\displaystyle w_{l}=1} for conv4_2, and zero otherwise. The ratio α / β ∈ [ 5 , 50 ] × 10 − 4 {\displaystyle \alpha /\beta \in [5,50]\times 10^{-4}} . === Training === Image x → {\displaystyle {\vec {x}}} is initially approximated by adding a small amount of white noise to input image p → {\displaystyle {\vec {p}}} and feeding it through the CNN. Then we successively backpropagate this loss through the network with the CNN weights fixed in order to update the pixels of x → {\displaystyle {\vec {x}}} . After several thousand epochs of training, an x → {\displaystyle {\vec {x}}} (hopefully) emerges that matches the style of a → {\displaystyle {\vec {a}}} and the content of p → {\displaystyle {\vec {p}}} . As of 2017, when implemented on a GPU, it takes a few minutes to converge. == Extensions == In some practical implementations, it is noted that the resulting image has too much high-frequency artifact, which can be suppressed by adding the total variation to the total loss. Compared to VGGNet, AlexNet does not work well for neural style transfer. NST has also been extended to videos. Subsequent work improved the speed of NST for images by using special-purpose normalizations. In a paper by Fei-Fei Li et al. adopted a different regularized loss metric and accelerated method for training to produce results in real-time (three orders of magnitude faster than Gatys). Their idea was to use not the pixel-based loss defined above but rather a 'perceptual loss' measuring t
Inverse consistency
In image registration, inverse consistency measures the consistency of mappings between images produced by a registration algorithm. The inverse consistency error, introduced by Christiansen and Johnson in 2001, quantifies the distance between the composition of the mappings from each image to the other, produced by the registration procedure, and the identity function, and is used as a regularisation constraint in the loss function of many registration algorithms to enforce consistent mappings. Inverse consistency is necessary for good image registration but it is not sufficient, since a mapping can be perfectly consistent but not register the images at all. == Definition == Image registration is the process of establishing a common coordinate system between two images, and given two images I 1 : Ω 1 → R I 2 : Ω 2 → R {\displaystyle {\begin{aligned}I_{1}:\Omega _{1}\to \mathbb {R} \\I_{2}:\Omega _{2}\to \mathbb {R} \end{aligned}}} registering a source image I 1 {\displaystyle I_{1}} to a target image I 2 {\displaystyle I_{2}} consists of determining a transformation f 1 : Ω 2 → Ω 1 {\displaystyle f_{1}:\Omega _{2}\to \Omega _{1}} that maps points from the target space to the source space. An ideal registration algorithm should not be sensitive to which image in the pair is used as source or target, and the registration operator should be antisymmetric such that the mappings f 1 : Ω 2 → Ω 1 f 2 : Ω 1 → Ω 2 {\displaystyle {\begin{aligned}f_{1}:\Omega _{2}\to \Omega _{1}\\f_{2}:\Omega _{1}\to \Omega _{2}\end{aligned}}} produced when registering I 1 {\displaystyle I_{1}} to I 2 {\displaystyle I_{2}} and I 2 {\displaystyle I_{2}} to I 1 {\displaystyle I_{1}} respectively should be the inverse of each other, i.e. f 2 = f 1 − 1 {\displaystyle f_{2}=f_{1}^{-1}} and f 1 = f 2 − 1 {\displaystyle f_{1}=f_{2}^{-1}} or, equivalently, f 2 ∘ f 1 = id Ω 2 {\displaystyle f_{2}\circ f_{1}=\operatorname {id} _{\Omega _{2}}} and f 1 ∘ f 2 = id Ω 1 {\displaystyle f_{1}\circ f_{2}=\operatorname {id} _{\Omega _{1}}} , where ∘ {\displaystyle \circ } denotes the function composition operator. Real algorithms are not perfect, and when swapping the role of source and target image in a registration problem the so obtained transformations are not the inverse of each other. Inverse consistency can be enforced by adding to the loss function of the registration a symmetric regularisation term that penalises inconsistent transformations ∫ Ω 2 ‖ f 2 ( f 1 ( x ) ) − x ‖ 2 d x + ∫ Ω 1 ‖ f 1 ( f 2 ( x ) ) − x ‖ 2 d x . {\displaystyle \int _{\Omega _{2}}\left\Vert f_{2}(f_{1}(x))-x\right\Vert ^{2}\mathrm {d} x+\int _{\Omega _{1}}\left\Vert f_{1}(f_{2}(x))-x\right\Vert ^{2}\mathrm {d} x.} Inverse consistency can be used as a quality metric to evaluate image registration results. The inverse consistency error ( I C E {\displaystyle ICE} ) measures the distance between the composition of the two transforms and the identity function, and it can be formulated in terms of both average ( I C E a {\displaystyle ICE_{a}} ) or maximum ( I C E m {\displaystyle ICE_{m}} ) over a region of interest Ω {\displaystyle \Omega } of the image: I C E a = 1 ∫ Ω d x ∫ Ω ‖ f 2 ( f 1 ( x ) ) − x ‖ d x I C E m = max x ∈ Ω ‖ f 2 ( f 1 ( x ) ) − x ‖ . {\displaystyle {\begin{aligned}ICE_{a}&={\frac {1}{\int _{\Omega }\mathrm {d} x}}\int _{\Omega }\left\Vert f_{2}(f_{1}(x))-x\right\Vert \mathrm {d} x\\ICE_{m}&=\max _{x\in \Omega }\left\Vert f_{2}(f_{1}(x))-x\right\Vert .\end{aligned}}} While inverse consistency is a necessary property of good registration algorithms, inverse consistency error alone is not a sufficient metric to evaluate the quality of image registration results, since a perfectly consistent mapping, with no other constraint, may be not even close to correctly register a pair of images.
Griffon (framework)
Griffon is an open source rich client platform framework which uses the Java, Apache Groovy, and/or Kotlin programming languages. Griffon is intended to be a high-productivity framework by rewarding use of the Model-View-Controller paradigm, providing a stand-alone development environment and hiding much of the configuration detail from the developer. The first release is the fruit of the effort by the Groovy Swing team and an attempt to take the best of rapid application development, as indicated by its Grails-like structure, the agility of Groovy, and the availability of components for Swing. The framework was redesign from scratch for version 2, allowing different JVM programming languages to be used either in isolation or in conjunction. Supported UI toolkits are Java Swing JavaFX Apache Pivot Lanterna == Overview == Griffon aims to reduce the typical confusion that occurs with traditional Java UI development. Due to the MVC structure of Griffon, developers never have to go searching for files or be confused on how to start a new project. Everything begins with: lazybones create
Huawei Mobile Services
Huawei Mobile Services (HMS) is a collection of proprietary services and high level application programming interfaces (APIs) developed by Huawei Technologies Co., Ltd. Its hub known as HMS Core serves as a toolkit for app development on Huawei devices. HMS is typically installed on Huawei devices on top of running HarmonyOS 4.x and earlier operating system on its earlier devices running the Android operating system with EMUI including devices already distributed with Google Mobile Services. Alongside, HMS Core Wear Engine for Android phones with lightweight based LiteOS wearable middleware app framework integration connectivity like notifications, status etc. HMS consists of seven key services and the HMS Core. The key services are Huawei ID, Huawei Cloud, AppGallery, Themes, Huawei Video, Browser, and Assistant. The web browser is based on Chromium. Huawei Quick Apps is the alternative to Google Instant Apps. By January 2020, over 50,000 apps had been integrated with HMS Core. Its rival, Google Mobile Services has 3 million apps on Google's Play Store. The AppGallery claimed 180 billion downloads in 2019. In March 2020, HMS was used by 650 million monthly active users across 170 countries. A Chinese phone manufacturer, LeTV, hosted a smartphone business communication meeting in Beijing on September 27, 2021, to demonstrate its phone, the LeTV S1. This was the first smartphone from a third-party manufacturer to include Huawei Mobile Services (HMS). == HMS on Android and HarmonyOS == Huawei Mobile Services on Android goes all the way back to August 2016 as Huawei ID services for phones, basic functionalities for Huawei P9 series. However, in May 2019 proved to be a significant change to HMS when Google was prohibited from working with Huawei on any new devices extending ecosystem for AppGallery store front launched in April 2018, year prior. This also included bundling Google's Apps, including Gmail, Maps and YouTube. Any new Huawei devices launched after 16 May 2019 were unable to receive updates from Google services and would be considered 'uncertified' meaning Huawei's only solution at the time was to turn HMS into a genuine competitor to Google and incentivize app developers to utilize the platform. Huawei officially launched Huawei Mobile Services in China on December 24, 2019, as a beta. Huawei expanded Huawei Mobile Services in Europe in February 2020 and other markets in Asia, Latin America, Middle East & Africa, Canada, Mexico followed outside banned US market. HMS is available on the Honor 9X Pro, View 30 Pro, Huawei Mate XS. HMS is also available, alongside GMS, on many other Huawei models launched before the ban. Huawei promised developers it would take, “less than 10 minutes", to port their app over to HMS - to illustrate the ease of portability between Google's Play Store and the HMS AppGallery. On January 15, 2020, HMS Core 4.0 (Huawei Mobile Services Core 4.0) was officially launched. Huawei announced that at this time, there were already 1.3 million developers and 55,000 applications on board. The next day, Huawei held a developer day event in London and invested £20 million to encourage developers in the United Kingdom and Ireland to use HMS. On July 15, 2021, Huawei expanded HMS with classic HarmonyOS dual-framework that provided Java support and eventually with JavaScript and ArkTS (eTS) language support with HMS Core 6.0 for app development with primarily Android apps, alongside limited HAP imperative developed based apps that shares AOSP file system libraries in all types of devices from smartphones, tablets, smart screens, smartwatches, and car machines. Including various third-party development frameworks, such as React Native, Cordova, etc. At HDC 2023, Huawei unveiled HarmonyOS 5, marking a total break from the hybrid Android derived platform. This shift replaced the legacy Android and classic HarmonyOS-based HMS SDK with a full native API developer kit SDK built solely on OpenHarmony. The architecture moved from middleware services to vertical integration path. In this new model, HMS Core libraries are no longer external add-ons but are bundled directly into the system and DevEco Studio as native HarmonyOS Kits. == HMS Core == HMS Core is a hub for Huawei Mobile Services and serves as a toolkit for app development on Huawei devices. The core comprises Development, Growth and Monetizing and was created as a replacement for Google Mobile Services (GMS) Core. HMS core services were available in more than 55,000 apps in June 2020; HMS Core 5.0 debuted in September 2020. HMS Core 6.0 was launched in June 2021 with extended support for Huawei Cloud services. In June 2021, the number of registered developers within the HMS ecosystem was 4 million, and the number of apps integrated with the HMS Core had reached 134,000. As of July 2022, registered developers within HMS ecosystem had grown to 5 million, and the number of apps integrated with the HMS Core reached 203,000. The number of apps had grown to 220,000 by 30 September 2022. == AppGallery == The AppGallery has a key rival, Google's Play Store on Android. The AppGallery is available in 170 countries, across 78 languages. == Reception == The reception of HMS is mixed, with the majority of discussion based around the key Google/Android apps which are not yet present on the AppGallery and whether or not this presents a significant problem to users. The open development of HMS Core has been regarded by some as benefiting the Android project as a whole, "If Huawei continues to invest in a holistically open approach ... the result could be that we could all end up a bit less beholden to Google".