The Webby Awards (colloquially referred to as the Webbys) are awards for excellence on the Internet presented annually by the International Academy of Digital Arts and Sciences, a judging body composed of over three thousand industry experts and technology innovators. Categories include websites, advertising and media, online film and video, mobile sites and apps, and social. Two winners are selected in each category, one by members of The International Academy of Digital Arts and Sciences, and one by the public who cast their votes during Webby People's Voice voting. Each winner presents a five-word acceptance speech, a trademark of the annual awards show. In its early years, the award was hailed as the "Internet's highest honor" and was associated with the phrase "The Oscars of the Internet." == History == In its early years, the organization was one of several vying to be the premiere internet awards show. Both shows would compare themselves to the Oscars, as did media outlets such as The New York Times to Canada's Globe & Mail. The winners of the First Annual Webby Awards in 1995 were presented by John Brancato and Michael Ferris, writers for Columbia Pictures. It was held at the Hollywood Roosevelt Hotel. The televised Webby Awards were sponsored by the Academy of Web Design and Cool Site of the Day. The first Webby Awards were produced by Kay Dangaard at the Hollywood Roosevelt Hotel as a nod to the first site of the Academy of Motion Picture Arts and Sciences (Oscars). That first year, they were called "Webbie" Awards. The first "Site of the Year" winner was the pioneer webisodic serial The Spot. The modern Webby Awards were co-founded by Tiffany Shlain, a filmmaker, when she was hired by The Web Magazine to re-establish them, and were first held in San Francisco in 1997. They quickly became known for its requirement that winners give their acceptance speeches in five words. After this, the awards became more successful than the magazine and IDG closed the publication. Shlain and co-founder Maya Draisin Farrah continued to run The Webby Awards until 2004. The International Academy of Digital Arts and Sciences, which selects the winners of The Webby Awards, was established in 1998 by co-founders Tiffany Shlain, Spencer Ante and Maya Draisin. Members of the Academy include Kevin Spacey, Grimes, Questlove, Internet inventor Vint Cerf, Instagram's Head of Fashion Partnerships Eva Chen, comedian Jimmy Kimmel, Twitter founder Biz Stone, Vice Media co-founder and CEO Shane Smith, Tumblr's David Karp, Director of Harvard's Berkman Klein Center for Internet & Society Susan P. Crawford, Refinery29's Executive Creative Director Piera Gelardi, and CEO and co-founder of Gimlet Media Alex Blumberg. The Webby Awards is owned and operated by the Webby Media Group, a division of Recognition Media, which also owns and produces the Lovie Awards in Europe and Netted by the Webbys, a daily email publication launched in 2009. David-Michel Davies, CEO of Webby Media Group, current Executive Director of the Webby Awards and co-founder of Internet Week New York, was named Executive Director of the Webby Awards in 2005. In 2009, the 13th Annual Webby Awards received nearly 10,000 entries from all 50 US states and over 60 countries. That same year, more than 500,000 votes were cast in The Webby People's Voice Awards. In 2012, the 16th Annual Webby awards received 1.5 million votes from more than 200 countries for the People's Voice awards. In 2015, the 19th Annual Webby Awards received nearly 13,000 entries from all 50 U.S. states and over 60 countries worldwide. == Nomination process == The 2000 awards began the transition to nominee submissions. Previously, nominees had been selected by an internal committee. As early as 2017, organizations wanting to nominate themselves were charged $395 for a single entry. An "ad campaign entry" would cost $595. By 2024, those fees had risen to $495 and $675, respectively. Executive Academy Members with category-specific expertise evaluate the shortlisted entries based on the appropriate Website, Advertising & Media, Online Film & Video, Mobile Sites & Apps, and Social category criteria, and cast ballots to determine Webby Honorees, Nominees and Webby Winners. Deloitte provides vote tabulation consulting for the Webby Awards. In addition to the award given in each category by the International Academy of Digital Arts and Sciences, another winner is selected in each category as determined by the general public during People's Voice voting. Winners of both the Academy-selected and People's Voice-selected awards are invited to the Webbys. == Awards granted == The Webby Awards are presented in over a hundred categories among all four types of entries. A website can be entered in multiple categories and receive multiple awards. In each category, two awards are handed out: a Webby Award selected by The International Academy of Digital Arts and Sciences, and a People's Voice Award selected by the general public. == Ceremony == Between 2005 and 2019, the Webby Awards were presented in New York City. Many of the ceremony hosts are comedians and comedic actors. Comedian Rob Corddry hosted the ceremony from 2005 to 2007. Seth Meyers of Saturday Night Live hosted in 2008 and 2009, B.J. Novak of the sitcom The Office in 2010, and Lisa Kudrow in 2011. Comedian, actor, and writer Patton Oswalt hosted from 2012 to 2014. Comedian Hannibal Buress hosted in 2015. The Webbys are famous for limiting recipients to five-word speeches, which are often humorous, although some exceed the limit. In 2005 when accepting his Lifetime Achievement Award, former Vice President Al Gore's speech was "Please don't recount this vote." He was introduced by Vint Cerf who used the same format to state, "We all invented the Internet." In 2013, the creator of the Graphics Interchange Format (GIF), Steve Wilhite, accepted his Webby and delivered his now famous five-word speech, "It's pronounced 'Jif' not 'Gif'." == Criticism == The Webbys have been criticized for their pay-to-enter and pay-to-attend policy (winners and nominees also have to pay to attend the award ceremony), and thus for not taking most websites into consideration before distributing their awards. Gawker, its Valleywag column, and others, have called the awards a scam, with Valleywag saying, "...somewhere along the way, the organizers figured out that this goofy charade could be milked for profit." In response, Webby Awards executive director David-Michel Davies told the Wall Street Journal that entry fees "provide the best and most sustainable model for ensuring that our judging process remains consistent and rigorous and is not dependent on things like sponsorships that can fluctuate from year to year." == Anthem Awards == In 2021, the Webby organization started a new line of awards, the Anthem Awards, to honor the purpose and mission-driven work of people, companies and organizations worldwide. The finalists and winners are selected by the International Academy of Digital Arts and Sciences.
Instance-based learning
In machine learning, instance-based learning (sometimes called memory-based learning) is a family of learning algorithms that, instead of performing explicit generalization, compare new problem instances with instances seen in training, which have been stored in memory. Because computation is postponed until a new instance is observed, these algorithms are sometimes referred to as "lazy." It is called instance-based because it constructs hypotheses directly from the training instances themselves. This means that the hypothesis complexity can grow with the data: in the worst case, a hypothesis is a list of n training items and the computational complexity of classifying a single new instance is O(n). One advantage that instance-based learning has over other methods of machine learning is its ability to adapt its model to previously unseen data. Instance-based learners may simply store a new instance or throw an old instance away. Examples of instance-based learning algorithms are the k-nearest neighbors algorithm, kernel machines and RBF networks. These store (a subset of) their training set; when predicting a value/class for a new instance, they compute distances or similarities between this instance and the training instances to make a decision. To battle the memory complexity of storing all training instances, as well as the risk of overfitting to noise in the training set, instance reduction algorithms have been proposed.
Gremlin (query language)
Gremlin is a graph traversal language and virtual machine developed by Apache TinkerPop of the Apache Software Foundation. Gremlin works for both OLTP-based graph databases as well as OLAP-based graph processors. Gremlin's automata and functional language foundation enable Gremlin to naturally support imperative and declarative querying, host language agnosticism, user-defined domain specific languages, an extensible compiler/optimizer, single- and multi-machine execution models, and hybrid depth- and breadth-first evaluation with Turing completeness. As an explanatory analogy, Apache TinkerPop and Gremlin are to graph databases what the JDBC and SQL are to relational databases. Likewise, the Gremlin traversal machine is to graph computing as what the Java virtual machine is to general purpose computing. == History == 2009-10-30 the project is born, and immediately named "TinkerPop" 2009-12-25 v0.1 is the first release 2011-05-21 v1.0 is released 2012-05-24 v2.0 is released 2015-01-16 TinkerPop becomes an Apache Incubator project 2015-07-09 v3.0.0-incubating is released 2016-05-23 Apache TinkerPop becomes a top-level project 2016-07-18 v3.1.3 and v3.2.1 are first releases as Apache TinkerPop 2017-12-17 v3.3.1 is released 2018-05-08 v3.3.3 is released 2019-08-05 v3.4.3 is released 2020-02-20 v3.4.6 is released 2021-05-01 v3.5.0 is released 2022-04-04 v3.6.0 is released 2023-07-31 v3.7.0 is released 2025-11-12 v3.8.0 is released == Vendor integration == Gremlin is an Apache2-licensed graph traversal language that can be used by graph system vendors. There are typically two types of graph system vendors: OLTP graph databases and OLAP graph processors. The table below outlines those graph vendors that support Gremlin. == Traversal examples == The following examples of Gremlin queries and responses in a Gremlin-Groovy environment are relative to a graph representation of the MovieLens dataset. The dataset includes users who rate movies. Users each have one occupation, and each movie has one or more categories associated with it. The MovieLens graph schema is detailed below. === Simple traversals === For each vertex in the graph, emit its label, then group and count each distinct label. What year was the oldest movie made? What is Die Hard's average rating? === Projection traversals === For each category, emit a map of its name and the number of movies it represents. For each movie with at least 11 ratings, emit a map of its name and average rating. Sort the maps in decreasing order by their average rating. Emit the first 10 maps (i.e. top 10). === Declarative pattern matching traversals === Gremlin supports declarative graph pattern matching similar to SPARQL. For instance, the following query below uses Gremlin's match()-step. What 80's action movies do 30-something programmers like? Group count the movies by their name and sort the group count map in decreasing order by value. Clip the map to the top 10 and emit the map entries. === OLAP traversal === Which movies are most central in the implicit 5-stars graph? == Gremlin graph traversal machine == Gremlin is a virtual machine composed of an instruction set as well as an execution engine. An analogy is drawn between Gremlin and Java. === Gremlin steps (instruction set) === The following traversal is a Gremlin traversal in the Gremlin-Java8 dialect. The Gremlin language (i.e. the fluent-style of expressing a graph traversal) can be represented in any host language that supports function composition and function nesting. Due to this simple requirement, there exists various Gremlin dialects including Gremlin-Groovy, Gremlin-Scala, Gremlin-Clojure, etc. The above Gremlin-Java8 traversal is ultimately compiled down to a step sequence called a traversal. A string representation of the traversal above provided below. The steps are the primitives of the Gremlin graph traversal machine. They are the parameterized instructions that the machine ultimately executes. The Gremlin instruction set is approximately 30 steps. These steps are sufficient to provide general purpose computing and what is typically required to express the common motifs of any graph traversal query. Given that Gremlin is a language, an instruction set, and a virtual machine, it is possible to design another traversal language that compiles to the Gremlin traversal machine (analogous to how Scala compiles to the JVM). For instance, the popular SPARQL graph pattern match language can be compiled to execute on the Gremlin machine. The following SPARQL query would compile to In Gremlin-Java8, the SPARQL query above would be represented as below and compile to the identical Gremlin step sequence (i.e. traversal). === Gremlin Machine (virtual machine) === The Gremlin graph traversal machine can execute on a single machine or across a multi-machine compute cluster. Execution agnosticism allows Gremlin to run over both graph databases (OLTP) and graph processors (OLAP).
Feature selection
In machine learning, feature selection is the process of selecting a subset of relevant features (variables, predictors) for use in model construction. Feature selection techniques are used for several reasons: simplification of models to make them easier to interpret, shorter training times, to avoid the curse of dimensionality, improve the compatibility of the data with a certain learning model class, to encode inherent symmetries present in the input space. The central premise when using feature selection is that data sometimes contains features that are redundant or irrelevant, and can thus be removed without incurring much loss of information. Redundancy and irrelevance are two distinct notions, since one relevant feature may be redundant in the presence of another relevant feature with which it is strongly correlated. Feature extraction creates new features from functions of the original features, whereas feature selection finds a subset of the features. Feature selection techniques are often used in domains where there are many features and comparatively few samples (data points). == Introduction == A feature selection algorithm can be seen as the combination of a search technique for proposing new feature subsets, along with an evaluation measure which scores the different feature subsets. The simplest algorithm is to test each possible subset of features finding the one which minimizes the error rate. This is an exhaustive search of the space, and is computationally intractable for all but the smallest of feature sets. The choice of evaluation metric heavily influences the algorithm, and it is these evaluation metrics which distinguish between the three main categories of feature selection algorithms: wrappers, filters and embedded methods. Wrapper methods use a predictive model to score feature subsets. Each new subset is used to train a model, which is tested on a hold-out set. Counting the number of mistakes made on that hold-out set (the error rate of the model) gives the score for that subset. As wrapper methods train a new model for each subset, they are very computationally intensive, but usually provide the best performing feature set for that particular type of model or typical problem. Filter methods use a proxy measure instead of the error rate to score a feature subset. This measure is chosen to be fast to compute, while still capturing the usefulness of the feature set. Common measures include the mutual information, the pointwise mutual information, Pearson product-moment correlation coefficient, Relief-based algorithms, and inter/intra class distance or the scores of significance tests for each class/feature combinations. Filters are usually less computationally intensive than wrappers, but they produce a feature set which is not tuned to a specific type of predictive model. This lack of tuning means a feature set from a filter is more general than the set from a wrapper, usually giving lower prediction performance than a wrapper. However the feature set doesn't contain the assumptions of a prediction model, and so is more useful for exposing the relationships between the features. Many filters provide a feature ranking rather than an explicit best feature subset, and the cut off point in the ranking is chosen via cross-validation. Filter methods have also been used as a preprocessing step for wrapper methods, allowing a wrapper to be used on larger problems. One other popular approach is the Recursive Feature Elimination algorithm, commonly used with Support Vector Machines to repeatedly construct a model and remove features with low weights. Embedded methods are a catch-all group of techniques which perform feature selection as part of the model construction process. The exemplar of this approach is the LASSO method for constructing a linear model, which penalizes the regression coefficients with an L1 penalty, shrinking many of them to zero. Any features which have non-zero regression coefficients are 'selected' by the LASSO algorithm. Improvements to the LASSO include Bolasso which bootstraps samples; Elastic net regularization, which combines the L1 penalty of LASSO with the L2 penalty of ridge regression; and FeaLect which scores all the features based on combinatorial analysis of regression coefficients. AEFS further extends LASSO to nonlinear scenario with autoencoders. These approaches tend to be between filters and wrappers in terms of computational complexity. In traditional regression analysis, the most popular form of feature selection is stepwise regression, which is a wrapper technique. It is a greedy algorithm that adds the best feature (or deletes the worst feature) at each round. The main control issue is deciding when to stop the algorithm. In machine learning, this is typically done by cross-validation. In statistics, some criteria are optimized. This leads to the inherent problem of nesting. More robust methods have been explored, such as branch and bound and piecewise linear network. == Subset selection == Subset selection evaluates a subset of features as a group for suitability. Subset selection algorithms can be broken up into wrappers, filters, and embedded methods. Wrappers use a search algorithm to search through the space of possible features and evaluate each subset by running a model on the subset. Wrappers can be computationally expensive and have a risk of over fitting to the model. Filters are similar to wrappers in the search approach, but instead of evaluating against a model, a simpler filter is evaluated. Embedded techniques are embedded in, and specific to, a model. Many popular search approaches use greedy hill climbing, which iteratively evaluates a candidate subset of features, then modifies the subset and evaluates if the new subset is an improvement over the old. Evaluation of the subsets requires a scoring metric that grades a subset of features. Exhaustive search is generally impractical, so at some implementor (or operator) defined stopping point, the subset of features with the highest score discovered up to that point is selected as the satisfactory feature subset. The stopping criterion varies by algorithm; possible criteria include: a subset score exceeds a threshold, a program's maximum allowed run time has been surpassed, etc. Alternative search-based techniques are based on targeted projection pursuit which finds low-dimensional projections of the data that score highly: the features that have the largest projections in the lower-dimensional space are then selected. Search approaches include: Exhaustive Best first Simulated annealing Genetic algorithm Greedy forward selection Greedy backward elimination Particle swarm optimization Targeted projection pursuit Scatter search Variable neighborhood search Two popular filter metrics for classification problems are correlation and mutual information, although neither are true metrics or 'distance measures' in the mathematical sense, since they fail to obey the triangle inequality and thus do not compute any actual 'distance' – they should rather be regarded as 'scores'. These scores are computed between a candidate feature (or set of features) and the desired output category. There are, however, true metrics that are a simple function of the mutual information; see here. Other available filter metrics include: Class separability Error probability Inter-class distance Probabilistic distance Entropy Consistency-based feature selection Correlation-based feature selection == Optimality criteria == The choice of optimality criteria is difficult as there are multiple objectives in a feature selection task. Many common criteria incorporate a measure of accuracy, penalised by the number of features selected. Examples include Akaike information criterion (AIC) and Mallows's Cp, which have a penalty of 2 for each added feature. AIC is based on information theory, and is effectively derived via the maximum entropy principle. Other criteria are Bayesian information criterion (BIC), which uses a penalty of log n {\displaystyle {\sqrt {\log {n}}}} for each added feature, minimum description length (MDL) which asymptotically uses log n {\displaystyle {\sqrt {\log {n}}}} , Bonferroni / RIC which use 2 log p {\displaystyle {\sqrt {2\log {p}}}} , maximum dependency feature selection, and a variety of new criteria that are motivated by false discovery rate (FDR), which use something close to 2 log p q {\displaystyle {\sqrt {2\log {\frac {p}{q}}}}} . A maximum entropy rate criterion may also be used to select the most relevant subset of features. == Structure learning == Filter feature selection is a specific case of a more general paradigm called structure learning. Feature selection finds the relevant feature set for a specific target variable whereas structure learning finds the relationships between all the variables, usually by expressing these relationships as a graph. The most common structure learning algorithms
Triplet loss
Triplet loss is a machine learning loss function widely used in one-shot learning, a setting where models are trained to generalize effectively from limited examples. It was conceived by Google researchers for their prominent FaceNet algorithm for face detection. Triplet loss is designed to support metric learning. Namely, to assist training models to learn an embedding (mapping to a feature space) where similar data points are closer together and dissimilar ones are farther apart, enabling robust discrimination across varied conditions. In the context of face detection, data points correspond to images. == Definition == The loss function is defined using triplets of training points of the form ( A , P , N ) {\displaystyle (A,P,N)} . In each triplet, A {\displaystyle A} (called an "anchor point") denotes a reference point of a particular identity, P {\displaystyle P} (called a "positive point") denotes another point of the same identity in point A {\displaystyle A} , and N {\displaystyle N} (called a "negative point") denotes a point of an identity different from the identity in point A {\displaystyle A} and P {\displaystyle P} . Let x {\displaystyle x} be some point and let f ( x ) {\displaystyle f(x)} be the embedding of x {\displaystyle x} in the finite-dimensional Euclidean space. It shall be assumed that the L2-norm of f ( x ) {\displaystyle f(x)} is unity (the L2 norm of a vector X {\displaystyle X} in a finite dimensional Euclidean space is denoted by ‖ X ‖ {\displaystyle \Vert X\Vert } .) We assemble m {\displaystyle m} triplets of points from the training dataset. The goal of training here is to ensure that, after learning, the following condition (called the "triplet constraint") is satisfied by all triplets ( A ( i ) , P ( i ) , N ( i ) ) {\displaystyle (A^{(i)},P^{(i)},N^{(i)})} in the training data set: ‖ f ( A ( i ) ) − f ( P ( i ) ) ‖ 2 2 + α < ‖ f ( A ( i ) ) − f ( N ( i ) ) ‖ 2 2 {\displaystyle \Vert f(A^{(i)})-f(P^{(i)})\Vert _{2}^{2}+\alpha <\Vert f(A^{(i)})-f(N^{(i)})\Vert _{2}^{2}} The variable α {\displaystyle \alpha } is a hyperparameter called the margin, and its value must be set manually. In the FaceNet system, its value was set as 0.2. Thus, the full form of the function to be minimized is the following: L = ∑ i = 1 m max ( ‖ f ( A ( i ) ) − f ( P ( i ) ) ‖ 2 2 − ‖ f ( A ( i ) ) − f ( N ( i ) ) ‖ 2 2 + α , 0 ) {\displaystyle L=\sum _{i=1}^{m}\max {\Big (}\Vert f(A^{(i)})-f(P^{(i)})\Vert _{2}^{2}-\Vert f(A^{(i)})-f(N^{(i)})\Vert _{2}^{2}+\alpha ,0{\Big )}} == Intuition == A baseline for understanding the effectiveness of triplet loss is the contrastive loss, which operates on pairs of samples (rather than triplets). Training with the contrastive loss pulls embeddings of similar pairs closer together, and pushes dissimilar pairs apart. Its pairwise approach is greedy, as it considers each pair in isolation. Triplet loss innovates by considering relative distances. Its goal is that the embedding of an anchor (query) point be closer to positive points than to negative points (also accounting for the margin). It does not try to further optimize the distances once this requirement is met. This is approximated by simultaneously considering two pairs (anchor-positive and anchor-negative), rather than each pair in isolation. == Triplet "mining" == One crucial implementation detail when training with triplet loss is triplet "mining", which focuses on the smart selection of triplets for optimization. This process adds an additional layer of complexity compared to contrastive loss. A naive approach to preparing training data for the triplet loss involves randomly selecting triplets from the dataset. In general, the set of valid triplets of the form ( A ( i ) , P ( i ) , N ( i ) ) {\displaystyle (A^{(i)},P^{(i)},N^{(i)})} is very large. To speed-up training convergence, it is essential to focus on challenging triplets. In the FaceNet paper, several options were explored, eventually arriving at the following. For each anchor-positive pair, the algorithm considers only semi-hard negatives. These are negatives that violate the triplet requirement (i.e, are "hard"), but lie farther from the anchor than the positive (not too hard). Restated, for each A ( i ) {\displaystyle A^{(i)}} and P ( i ) {\displaystyle P^{(i)}} , they seek N ( i ) {\displaystyle N^{(i)}} such that: The rationale for this design choice is heuristic. It may appear puzzling that the mining process neglects "very hard" negatives (i.e., closer to the anchor than the positive). Experiments conducted by the FaceNet designers found that this often leads to a convergence to degenerate local minima. Triplet mining is performed at each training step, from within the sample points contained in the training batch (this is known as online mining), after embeddings were computed for all points in the batch. While ideally the entire dataset could be used, this is impractical in general. To support a large search space for triplets, the FaceNet authors used very large batches (1800 samples). Batches are constructed by selecting a large number of same-category sample points (40), and randomly selected negatives for them. == Extensions == Triplet loss has been extended to simultaneously maintain a series of distance orders by optimizing a continuous relevance degree with a chain (i.e., ladder) of distance inequalities. This leads to the Ladder Loss, which has been demonstrated to offer performance enhancements of visual-semantic embedding in learning to rank tasks. In Natural Language Processing, triplet loss is one of the loss functions considered for BERT fine-tuning in the SBERT architecture. Other extensions involve specifying multiple negatives (multiple negatives ranking loss).
Diia
Diia (Ukrainian: Дія [ˈd⁽ʲ⁾ijɐ] , lit. 'Action'; also an acronym for Держава і Я, Derzhava i Ya, IPA: [derˈʒɑwɐ i ˈjɑ], lit. 'State and Me') is a mobile app, a web portal and a brand of e-governance in Ukraine. Launched in 2020, the Diia app allows Ukrainian citizens to use digital documents on their smartphones instead of physical ones for identification and sharing purposes. The Diia portal allows access to over 130 government services. Eventually, the government plans to make all kinds of state-person interactions available through Diia. Diia was built in partnership with the United States and is poised to be shared with other countries. On the sidelines of the 2023 World Economic Forum in Davos, USAID Administrator Samantha Power said the US hopes to replicate the success of Diia in other countries. == History == Diia was first presented on September 27, 2019, by the Ministry of Digital Transformation of Ukraine as a brand of the State in a Smartphone project. Vice Prime Minister and Minister of Digital Transformation Mykhailo Fedorov announced the creation of a mobile app and a web portal that would unite in a single place all the services provided by the state to citizens and businesses. On February 6, 2020, the mobile app Diia was officially launched. During the presentation, Ukrainian President Volodymyr Zelensky said that 9 million Ukrainians now have access to their driver's license and car registration documents on their phones, while Prime Minister Oleksiy Honcharuk called the implementation of the State in a Smartphone project a priority for the government. In April 2020, the Ukrainian government approved a resolution for experimental usage of digital ID-cards and passports which would be issued to all Ukrainians via the Diia. On October 5, 2020, during the Diia Summit, the government presented a first major update of the app and web portal branded "Diia 2.0". More types of documents were added to the app as well as the ability to share documents with others via a single tap on a push-message. The web portal in turn expanded the number of available services to 27, including the ability to register a private limited company in half an hour. President Zelensky who opened the summit, announced that in 2021 Ukraine will enter the "paper less" mode by prohibiting civil servants from demanding paper documents. By the end of 2020, the app had more than six million users, while the portal had 50 available services. In March 2021, the Ukrainian parliament adopted a bill equating digital identity documents with their physical analogues. Starting on August 23, Ukrainian citizens can use digital ID-cards and passports for all purposes while in Ukraine. According to Minister of Digital Transformation Mykhailo Fedorov, Ukraine will become the first country in the world where digital identity documents are considered legally equivalent to ordinary ones. In September 2024, Diia launched an online marriage registration service, which can be beneficial especially for military personnel who spend much time on the frontline separated from their partners. In October 2024, Diia's online marriage service appeared in Time's Inventions of the 2024 list. In the first month of its operations over 1.1 million Ukrainians tried to make proposals using the technology, and 435 couples got married. == Benefits and challenges == The first and most obvious benefit is the convenience of such a platform. Citizens can have many documents on their smartphones at once, without concern about losing or damaging them. Whenever needed, they can just open an app on their smartphones and show/check the document they need. The idea is that Diia will help cut the bureaucracy associated with public services, which in turn will help fight corruption and increase government savings. Fewer people are needed to be employed in the public sector and fewer human to human interactions are supposed to happen. With the start of the program, already 10% of government employees were reduced, which contributes to hundreds of millions of dollars in savings, but besides this, the initiative also improves the speed, efficiency, and transparency of government services. In addition, the digitalization of the government sector helps to develop the whole IT industry in the country, people become more digitally aware and educated, this affects other sectors as well, increasing the spread of digital infrastructure and expediting the speed of overall digitalization. The UN E-government Development Index, which assesses the capabilities of governments to integrate its functions electronically, such as the use of internet and mobile devices, ranked Ukraine 69th in 193 countries surveyed in 2020. Despite its low ranking in the e-government development index, Ukraine made a big jump on the e-participation index, which they ranked 43rd out of 193 countries from 0.66 in 2018 to 0.81 in 2020 (un.org, 2020), suggesting that the government and its citizens are adapting the IT-based government functions. The main goal of e-government according to Perez-Morote et.al. (2020) is to have accountability and transparency among the countries involved. But to do so, there are several challenges that a country should assess first prior to implementing e-government. In the research written by Heeks (2001), the author identified 2 main challenges that countries face in the development of e-government, first is the strategic challenge which involves the preparedness (e-readiness) of the entire government system for electronic transformation, and second challenge is the tactical challenge where the government must design (e-governance design) a system where it can be understood by every user, it's important that the information that needs to be communicated to the consumers is received clearly. For the first challenge (e-readiness), Ukraine had an internet penetration rate of 76% in 2020 and is expected to grow to 82%, it is important that consumers have the internet access for it to enable the consumers to utilize the service. Another factor is the readiness of its institutional infrastructure, which means that the government has its own organization which is solely focused on implementing the e-government project. In the case of Ukraine, the e-governance team is led by Oleksandr Ryzhenko, and the country's e-governance initiative is even further strengthened by ensuring that the data and legal infrastructure are already prepared. Ukraine has done this by modernizing their legislation that is more appropriate in the digital service, and the data exchange solution used by Ukraine is called Trembita. The human infrastructure is also being updated, as competent individuals must be the one doing the task, hence, EGOV4UKRAINE was launched, this aims to get IT developers for developing a system for administrative services. These efforts by the Ukrainian government did not go unnoticed, and they received an award from the e-Governance Academy as "partner of the year 2017". For the second challenge, which deals with the system design, the success of Ukraine can be seen on the latest data of UNDP, where it shows a high increase in the E-participation index. In 2018, Ukraine ranked 75th it ranked 46th in 2020 (un.org, 2020). Despite visible success, the implementation of the e-government was accompanied by problems. Data leakage became the main one. In May 2020, the data of 26 million driver's licenses appeared in the public domain on the Internet. The Ukrainian government said the Diia app was not linked to a data breach, but it is impossible to say for certain. Any storage of official documents in electronic format is associated with the risk of their leakage. In addition, the Diia application still has data protection issues, as the required protection system has not been implemented. This is also compounded by the country's weak data protection legal regime. In addition, since 2023, Ukrainians are able to register their cars with this app. Issued license plates are not using regional codes, but they are using special codes starting with DI or PD. == Diia City == In May 2020, the government presented Diia City headed by Oleksandr Borniakov, a large-scale project which would establish a virtual model of a free economic zone for representatives of the creative economy. It would provide for special digital residency with a particular taxation regime, intellectual property protection and simplified regulations. Diia City concurrently imposes certain constraints on contracts involving individual entrepreneurs (FOPs). It also offers the benefit of tax rebates. Diia City garners endorsement from the Ukrainian government, believing it will support the country's position in the IT market. As of July 30, 2023, the program had more than 600 residents, including companies like iGama, Avenga, SBRobotiks, and Intellectsoft.
Relation network
A relation network (RN) is an artificial neural network component with a structure that can reason about relations among objects. An example category of such relations is spatial relations (above, below, left, right, in front of, behind). RNs can infer relations, they are data efficient, and they operate on a set of objects without regard to the objects' order. == History == In June 2017, DeepMind announced the first relation network. It claimed that the technology had achieved "superhuman" performance on multiple question-answering problem sets. == Design == RNs constrain the functional form of a neural network to capture the common properties of relational reasoning. These properties are explicitly added to the system, rather than established by learning just as the capacity to reason about spatial, translation-invariant properties is explicitly part of convolutional neural networks (CNN). The data to be considered can be presented as a simple list or as a directed graph whose nodes are objects and whose edges are the pairs of objects whose relationships are to be considered. The RN is a composite function: R N ( O ) = f ϕ ( ∑ i , j g θ ( o i , o j , q ) ) , {\displaystyle RN\left(O\right)=f_{\phi }\left(\sum _{i,j}g_{\theta }\left(o_{i},o_{j},q\right)\right),} where the input is a set of "objects" O = { o 1 , o 2 , . . . , o n } , o i ∈ R m {\displaystyle O=\left\lbrace o_{1},o_{2},...,o_{n}\right\rbrace ,o_{i}\in \mathbb {R} ^{m}} is the ith object, and fφ and gθ are functions with parameters φ and θ, respectively and q is the question. fφ and gθ are multilayer perceptrons, while the 2 parameters are learnable synaptic weights. RNs are differentiable. The output of gθ is a "relation"; therefore, the role of gθ is to infer any ways in which two objects are related. Image (128x128 pixel) processing is done with a 4-layer CNN. Outputs from the CNN are treated as the objects for relation analysis, without regard for what those "objects" explicitly represent. Questions were processed with a long short-term memory network.