Metadirectory

A metadirectory system provides for the flow of data between one or more directory services and databases in order to maintain synchronization of that data. It is an important part of identity management systems. The data being synchronized typically are collections of entries that contain user profiles and possibly authentication or policy information. Most metadirectory deployments synchronize data into at least one LDAP-based directory server, to ensure that LDAP-based applications such as single sign-on and portal servers have access to recent data, even if the data is mastered in a non-LDAP data source. Metadirectory products support filtering and transformation of data in transit. Most identity management suites from commercial vendors include a metadirectory product, or a user provisioning product.

Alias Eclipse

Eclipse was a professional 2D image editing program available on Silicon Graphics and Windows workstations. Designed to manipulate high-resolution images like digitized movie frames and photographs for print, it offered color correction tools, image processing effects, rudimentary paint features, and spline-based drawing and masking. == History == Eclipse was originally developed in the late 1980s by Full Color Computing, an early provider of photo retouch and color prepress software for Silicon Graphics workstations. Alias Research (later Alias Systems Corporation), a developer of professional 3D graphics applications for the SGI platform, purchased the rights to Eclipse in fall 1990. Alias developed Eclipse through the early to mid-1990s, releasing version 2.5 in 1995 with improvements to the speed of color correction, effects, and rendering. Xyvision's Contex Prepress division purchased exclusive rights to Eclipse from Alias in 1996, and released version 3.0 the following year. Eclipse was subsequently sold to German developer Form & Vision GmbH, which continued development and ported it to the Windows platform. In 1999, Form & Vision released a demo of Eclipse 3.1.3 on the SGI platform which was limited to 1600 x 1600 pixel images, then ceased development of Eclipse on the SGI platform. Eclipse was thereafter developed exclusively for the Windows platform, culminating with version 3.1.4 in 2001. In the same year the firm went bankrupt. == Features == Eclipse was designed to work with very large images that could not be manipulated in real time on contemporary computer systems due to memory limitations, and thus allowed the user to make modifications to a lower-resolution copy of the original image in "proxy mode." Brush strokes, color corrections, and other edits were saved in proxy mode, then applied to the full-size image in post processing. This method also allowed for batch processing of a high-resolution image sequence using the edits applied to the original proxy image. Other features included color correction and separation, warping, special effects, text, and shape masking. Wavelet image compression created by LuraTech was added to Eclipse 3.1.4

Subject indexing

Subject indexing is the act of describing or classifying a document by index terms, keywords, or other symbols in order to indicate what different documents are about, to summarize their contents or to increase findability. In other words, the objective is to identify and describe the subject of documents. Indexes are constructed, separately, on three distinct levels: terms in a document, such as a book; objects in a collection, such as a library; and documents (such as books and articles) within a field of knowledge. Subject indexing is used in information retrieval especially to create bibliographic indexes to retrieve documents on a particular subject. Examples of academic indexing services are Zentralblatt MATH, Chemical Abstracts, and PubMed. The index terms were mostly assigned by experts but author keywords are also common. The process of indexing begins with any analysis of the subject of the document. The indexer must then identify terms that appropriately identify the subject, either by extracting words directly from the document or assigning words from a controlled vocabulary. The terms in the index are then presented in a systematic order. Indexers must decide how many terms to include and how specific the terms should be. Together this gives a depth of indexing. == Subject analysis == The first step in indexing is to decide on the subject matter of the document. In manual indexing, the indexer would consider the subject matter in terms of answer to a set of questions such as "Does the document deal with a specific product, condition or phenomenon?". As the analysis is influenced by the knowledge and experience of the indexer, it follows that two indexers may analyze the content differently and so come up with different index terms. This will impact on the success of retrieval. === Automatic vs. manual subject analysis === Automatic indexing follows set processes of analyzing frequencies of word patterns and comparing results to other documents in order to assign to subject categories. This requires no understanding of the material being indexed. This leads to more uniform indexing but at the expense of the true meaning being interpreted. A computer program will not understand the meaning of statements and may therefore fail to assign some relevant terms or assign incorrectly. Human indexers focus their attention on certain parts of the document such as the title, abstract, summary and conclusions, as analyzing the full text in depth is costly and time-consuming. An automated system takes away the time limit and allows the entire document to be analyzed, but also has the option to be directed to particular parts of the document. == Term selection == The second stage of indexing involves the translation of the subject analysis into a set of index terms. This can involve extracting from the document or assigning from a controlled vocabulary. With the ability to conduct a full text search widely available, many people have come to rely on their own expertise in conducting information searches and full text search has become very popular. Subject indexing and its experts, professional indexers, catalogers, and librarians, remains crucial to information organization and retrieval. These experts understand controlled vocabularies and are able to find information that cannot be located by full text search. The cost of expert analysis to create subject indexing is not easily compared to the cost of hardware, software and labor to manufacture a comparable set of full-text, fully searchable materials. With new web applications that allow every user to annotate documents, social tagging has gained popularity especially in the Web. One application of indexing, the book index, remains relatively unchanged despite the information revolution. === Extraction/Derived indexing === Extraction indexing involves taking words directly from the document. It uses natural language and lends itself well to automated techniques where word frequencies are calculated and those with a frequency over a pre-determined threshold are used as index terms. A stop-list containing common words (such as "the", "and") would be referred to and such stop words would be excluded as index terms. Automated extraction indexing may lead to loss of meaning of terms by indexing single words as opposed to phrases. Although it is possible to extract commonly occurring phrases, it becomes more difficult if key concepts are inconsistently worded in phrases. Automated extraction indexing also has the problem that, even with use of a stop-list to remove common words, some frequent words may not be useful for allowing discrimination between documents. For example, the term glucose is likely to occur frequently in any document related to diabetes. Therefore, use of this term would likely return most or all the documents in the database. Post-coordinated indexing where terms are combined at the time of searching would reduce this effect but the onus would be on the searcher to link appropriate terms as opposed to the information professional. In addition terms that occur infrequently may be highly significant for example a new drug may be mentioned infrequently but the novelty of the subject makes any reference significant. One method for allowing rarer terms to be included and common words to be excluded by automated techniques would be a relative frequency approach where frequency of a word in a document is compared to frequency in the database as a whole. Therefore, a term that occurs more often in a document than might be expected based on the rest of the database could then be used as an index term, and terms that occur equally frequently throughout will be excluded. Another problem with automated extraction is that it does not recognize when a concept is discussed but is not identified in the text by an indexable keyword. Since this process is based on simple string matching and involves no intellectual analysis, the resulting product is more appropriately known as a concordance than an index. === Assignment indexing === An alternative is assignment indexing where index terms are taken from a controlled vocabulary. This has the advantage of controlling for synonyms as the preferred term is indexed and synonyms or related terms direct the user to the preferred term. This means the user can find articles regardless of the specific term used by the author and saves the user from having to know and check all possible synonyms. It also removes any confusion caused by homographs by inclusion of a qualifying term. A third advantage is that it allows the linking of related terms whether they are linked by hierarchy or association, e.g. an index entry for an oral medication may list other oral medications as related terms on the same level of the hierarchy but would also link to broader terms such as treatment. Assignment indexing is used in manual indexing to improve inter-indexer consistency as different indexers will have a controlled set of terms to choose from. Controlled vocabularies do not completely remove inconsistencies as two indexers may still interpret the subject differently. == Index presentation == The final phase of indexing is to present the entries in a systematic order. This may involve linking entries. In a pre-coordinated index the indexer determines the order in which terms are linked in an entry by considering how a user may formulate their search. In a post-coordinated index, the entries are presented singly and the user can link the entries through searches, most commonly carried out by computer software. Post-coordination results in a loss of precision in comparison to pre-coordination. == Depth of indexing == Indexers must make decisions about what entries should be included and how many entries an index should incorporate. The depth of indexing describes the thoroughness of the indexing process with reference to exhaustivity and specificity. === Exhaustivity === An exhaustive index is one which lists all possible index terms. Greater exhaustivity gives a higher recall, or more likelihood of all the relevant articles being retrieved, however, this occurs at the expense of precision. This means that the user may retrieve a larger number of irrelevant documents or documents which only deal with the subject in little depth. In a manual system a greater level of exhaustivity brings with it a greater cost as more man-hours are required. The additional time taken in an automated system would be much less significant. At the other end of the scale, in a selective index only the most important aspects are covered. Recall is reduced in a selective index as if an indexer does not include enough terms, a highly relevant article may be overlooked. Therefore, indexers should strive for a balance and consider what the document may be used. They may also have to consider the implications of time and expense. === Specificity === The specificity describes how closel

Artificial intelligence in Brazilian industry

In 2022, 16.9% (1,620) of the 9,586 Brazilian industrial companies with 100 or more employees used artificial intelligence in their operations Among the companies that used AI, the areas of administration (73.8%), product project development (65.9%), processes, services and marketing (65.1%) were those that used it the most, followed by the areas of production (56.4%) and logistics (48.4%). == Current scenario == === Adoption in Brazilian industrial sectors === In senior management, the majority (56%) of executives have a long-term vision for its use. The study also shows that IT, Innovation, and Marketing are the areas where AI use is most widespread, and that 43% of companies are developing or adapting the algorithms they use. The majority of large institutions that reported some type of AI use purchased these solutions from other companies (76%). Some factors for the adoption of artificial intelligence in companies include the establishment of an autonomous strategy by the company (87.0%), and the influence of suppliers and/or customers (63.0%) and the main difficulties in using technologies were high costs (80.8%), lack of qualified personnel in the company (54.6%) and excessive economic risks (49.5%). Three variables are considered the most relevant to explain the option to use AI: the implementation of a digital security policy, the size of companies with 250 or more employees and the characteristics of the company related to information and communication. When analyzing AI use by company size in Brazil, large companies have the highest proportion of AI use, mainly due to their investment capacity and technology experimentation. However, when comparing Brazil and Europe, indicators show an acceleration in AI use among large European companies, while in Brazil the situation remains stable. In 2023, 30% of large companies in the European bloc used some type of AI, a figure that rose to 41% in 2024, while in Brazil these proportions were 41% in 2023 and 38% in 2024. === Workforce === The challenge of upskilling begins with employees who are capable of understanding recent technological changes. Similarly, companies must create the environment and conditions for workforce development conducive to innovation, and universities must be prepared to provide knowledge aligned with the transition process, which in turn must be supported by public policies. The concern with training a specialized workforce in AI can be seen in the low number of graduates and PhDs in computer science and computer engineering in Brazil, compared to the number shown in other countries. As recorded in the document Recommendations for the Advancement of Artificial Intelligence in Brazil, 2019 data from the Coordination for the Improvement of Higher Education Personnel (CAPES) indicate that "the number of PhDs graduated annually in computing remained below 400 in 2016, and is not expected to have increased during the Covid-19 pandemic" (ABC, 2023). In the United States, by contrast, the number of PhDs graduated in these two areas has remained around 1,800 for the past 11 years, and during this period, the number of PhDs specializing in AI jumped from 10% to 19%. Based on data from the CNPq Lattes Platform (October 2019), it is possible to observe that the number of professionals in the AI field in Brazil is 4,429 specialists. This is still a small number compared to the 415,166 IT jobs in the country's business sector alone. === R&D, scientific production and integration with industry === China and the United States lead in the number of publications. These two countries are followed by the G7 members: India, Austria, South Korea, and Spain. Brazil appears in the next group, alongside the Netherlands, Russia, Indonesia, and Ireland. Regarding the promotion of research and technologies related to AI, public entities such as the Coordination for the Improvement of Higher Education Personnel (Capes) and the National Council for Scientific and Technological Development (CNPq) stood out as the main funders. Currently, different countries and territories have been promoting the development of Artificial Intelligence (AI). In the Brazilian case, one of the main initiatives is the creation of Engineering Research Centers/Applied Research Centers (CPE/CPA) in AI by the São Paulo Research Foundation (FAPESP), in collaboration with the Ministry of Science, Technology and Innovation (MCTI), the Ministry of Communications (MC) and the Brazilian Internet Steering Committee (CGI.br). In terms of the number of patents filed and the volume of investments, the leading nations in AI are the United States, China, France, Germany, the United Kingdom, Russia, India, Switzerland, Japan, South Korea, the Netherlands, Sweden, Finland, Ireland, Singapore, Canada, Israel, and Italy. Brazil appears among the top twenty countries in some rankings, mainly due to its good number of publications (approximately 10% of the number of articles published by the United States). The US is home to approximately 60% of the world's top AI researchers, followed by China (11%), Europe (10%), and Canada (6%). To change this scenario, in August 2024, the Brazilian government announced an investment of R$23 billion until 2028 in artificial intelligence, seeking to “transform the country into a global reference in innovation”. == Future challenges == The Organization for Economic Cooperation and Development (2020) report highlighted three factors that hinder the digital transformation journey and application of AI in Brazil: insufficient infrastructure, high costs due to the tax system, and financial limitations, such as limited access to financing. The costs of adopting technology, its incompatibility with the business, and the lack of training also represent obstacles that Brazilian industry must overcome. There are also inherent obstacles for companies. A McKinsey review emphasizes that once a company chooses one or more sectors to focus on, it must select specific applications. Buyers aren't interested in artificial intelligence simply because it's a breakthrough technology; they want AI to generate a good return on investment, whether by solving specific problems, saving money, or increasing sales. If an AI vendor tried to offer a horizontal solution, the value proposition might not be as compelling. Part of the solution to Brazil's technological backwardness involves building an ecosystem fueled by private institutions, universities, and governments.

Best arm identification

Best arm identification (BAI) is a sequential one-player game where the player has to find the best action (arm) among a list of actions (arms) by collecting information in the most efficient way. It is a multi-armed bandit game as a player only gets information about an arm by playing it. The most common objective in multi-armed bandit games is to minimize the regret (i.e., play the best action as much as possible), but in BAI, the goal is to find the best arm as efficiently as possible. This problem naturally arises in scenarios such as adaptive clinical trials where the number of patients is limited and the quantification of the confidence in a treatment is important. It also arises in hyperparameter optimization where the goal is to find the optimal choice of hyperparameters for an algorithm with the smallest possible number of experiments, as it can be costly in terms of time, energy, or money. == Stochastic multi-armed bandit == The stochastic multi-armed bandit (MAB) is a sequential game with one player and K {\displaystyle K} actions (arms). Each arm has an unknown probability distribution associated with it. At each turn, the player has to choose one action and receive an observation from the probability distribution associated with the arm. The more you play an arm, the more you get information on its probability distribution. === Best arm identification === In BAI the goal is to find the arm that has the probability distribution with the highest mean. BAI may be either fixed confidence or fixed horizon. In a fixed-confidence game, a confidence level δ {\displaystyle \delta } is fixed at the beginning of the game and the goal is to find the best arm with this confidence level in as few turns as possible. In a fixed horizon game, the number of turns T {\displaystyle T} is fixed, and the goal is to find the best arm with the highest possible confidence in T {\displaystyle T} turns. === Math formalisation === We have one player and K {\displaystyle K} actions (arms). Behind each arm k ∈ { 1 , … , K } {\displaystyle k\in \{1,\ldots ,K\}} lies an unknown distribution ν k {\displaystyle \nu _{k}} with mean μ k {\displaystyle \mu _{k}} . Each distribution ν k {\displaystyle \nu _{k}} belongs to a known family D {\displaystyle {\mathcal {D}}} (such as the set of Gaussian distributions or Bernoulli distributions). At each time step t {\displaystyle t} , the player selects an arm a t {\displaystyle a_{t}} and observes an independent sample X t ∼ ν a t {\displaystyle X_{t}\sim \nu _{a_{t}}} from the corresponding distribution. We will note μ ∗ := max μ a {\displaystyle \mu ^{}:=\max \mu _{a}} the highest mean. An arm a {\displaystyle a} that satisfies μ a = μ ∗ {\displaystyle \mu _{a}=\mu ^{}} is called an optimal arm; otherwise it is called suboptimal arm. In best arm identification (BAI) the objective is to identify an optimal arm. Two main settings for BAI appear in the literature: Fixed confidence: In this setting, one typically assumes that there exists a unique optimal arm. A confidence level δ ∈ ( 0 , 1 ) {\displaystyle \delta \in (0,1)} is specified at the beginning. The algorithm must stop at some finite stopping time τ δ < + ∞ {\displaystyle \tau _{\delta }<+\infty } and return an arm a ^ τ δ {\displaystyle {\hat {a}}_{\tau _{\delta }}} such that the probability of error is bounded: P ( a ^ τ δ ≠ a ∗ ) ≤ δ {\displaystyle \mathbb {P} ({\hat {a}}_{\tau _{\delta }}\neq a^{})\leq \delta } . The objective is to minimize the expected sample complexity E [ τ δ ] {\displaystyle \mathbb {E} [\tau _{\delta }]} . Such a setting appears, for example, when a constraint on the confidence is required (for example, if we require a confidence level of 95%, so δ = 1 − 0.95 = 0.05 {\displaystyle \delta =1-0.95=0.05} ). Fixed horizon: In this setting, the number of samples T {\displaystyle T} is fixed in advance. The goal is to design an algorithm that minimizes the probability of misidentifying the optimal arm: P ( a ^ T ≠ a ∗ ) {\displaystyle \mathbb {P} ({\hat {a}}_{T}\neq a^{})} . This setting appears when the number of experiments is limited (for drug tests, the number of patients can be fixed in advance). === Example of simple modelling === In the case where we have K {\displaystyle K} treatments and we want to be sure with a confidence level of 95% which treatment is the best to heal a specific disease. Each treatment heals or does not heal the disease with a probability μ k {\displaystyle \mu _{k}} , which means that each distribution is a Bernoulli distribution, so D {\displaystyle {\mathcal {D}}} is the set of Bernoulli distributions. We can use a BAI algorithm to minimize E [ τ 0.05 ] {\displaystyle \mathbb {E} [\tau _{0.05}]} , the number of patients required to find the best treatment with probability 95%. == Applications == Best arm identification naturally arises in several practical domains: Adaptive clinical trials: The objective is to identify the most effective treatment based on sequentially collected patient data. Each treatment can be modeled as having an underlying distribution of outcomes. The goal is to identify the treatment with the highest expected outcome with high confidence (fixed confidence setting δ {\displaystyle \delta } ) while minimizing the number of drug test patients (minimise E [ τ δ ] {\displaystyle \mathbb {E} [\tau _{\delta }]} ), as it costs to pay patients for this and we would like to use as little as possible less effective drugs. Hyperparameter tuning: Selecting the best configuration for machine learning models efficiently by treating each hyperparameter setting as an arm. The goal is to find the best hyperparameter with as few experiments possible as experiments are costly in time and in energy == Fixed confidence level == In the fixed-confidence setting, the goal is to design an algorithm that identifies the best arm with a prescribed confidence level δ {\displaystyle \delta } while minimizing the expected number of samples. Any such algorithm requires two key components: Stopping rule: A decision criterion that determines when to stop sampling. Formally, this defines a stopping time τ δ {\displaystyle \tau _{\delta }} and returns an arm a ^ τ δ {\displaystyle {\hat {a}}_{\tau _{\delta }}} such that P ( a ^ τ δ ≠ a ⋆ ) ≤ δ {\displaystyle \mathbb {P} ({\hat {a}}_{\tau _{\delta }}\neq a^{\star })\leq \delta } and P ( τ δ < + ∞ ) = 1 {\displaystyle \mathbb {P} (\tau _{\delta }<+\infty )=1} . Sampling rule: A policy π {\displaystyle \pi } that, at each round t {\displaystyle t} , selects the next arm to sample a t {\displaystyle a_{t}} based on all previous observations ( a s , X s ) s < t {\displaystyle (a_{s},X_{s})_{s

Robinson compass mask

In image processing, a Robinson compass mask is a type of compass mask used for edge detection. It has eight major compass orientations, each will extract the edges in respect to its direction. A combined use of compass masks of different directions could detect the edges from different angles. == Technical explanation == The Robinson compass mask is defined by taking a single mask and rotating it to form eight orientations: North: [ − 1 0 1 − 2 0 2 − 1 0 1 ] {\displaystyle {\text{North:}}{\begin{bmatrix}-1&0&1\\-2&0&2\\-1&0&1\end{bmatrix}}} North West: [ 0 1 2 − 1 0 1 − 2 − 1 0 ] {\displaystyle {\text{North West:}}{\begin{bmatrix}0&1&2\\-1&0&1\\-2&-1&0\end{bmatrix}}} West: [ 1 2 1 0 0 0 − 1 − 2 − 1 ] {\displaystyle {\text{West:}}{\begin{bmatrix}1&2&1\\0&0&0\\-1&-2&-1\end{bmatrix}}} South West: [ 2 1 0 1 0 − 1 0 − 1 − 2 ] {\displaystyle {\text{South West:}}{\begin{bmatrix}2&1&0\\1&0&-1\\0&-1&-2\end{bmatrix}}} South: [ 1 0 − 1 2 0 − 2 1 0 − 1 ] {\displaystyle {\text{South:}}{\begin{bmatrix}1&0&-1\\2&0&-2\\1&0&-1\end{bmatrix}}} South East: [ 0 − 1 − 2 1 0 − 1 2 1 0 ] {\displaystyle {\text{South East:}}{\begin{bmatrix}0&-1&-2\\1&0&-1\\2&1&0\end{bmatrix}}} East: [ − 1 − 2 − 1 0 0 0 1 2 1 ] {\displaystyle {\text{East:}}{\begin{bmatrix}-1&-2&-1\\0&0&0\\1&2&1\end{bmatrix}}} North East: [ − 2 − 1 0 − 1 0 1 0 1 2 ] {\displaystyle {\text{North East:}}{\begin{bmatrix}-2&-1&0\\-1&0&1\\0&1&2\end{bmatrix}}} The direction axis is the line of zeros in the matrix. Robinson compass mask is similar to kirsch compass masks, but is simpler to implement. Since the matrix coefficients only contains 0, 1, 2, and are symmetrical, only the results of four masks need to be calculated, the other four results are the negation of the first four results. An edge, or contour is an tiny area with neighboring distinct pixel values. The convolution of each mask with the image would create a high value output where there is a rapid change of pixel value, thus an edge point is found. All the detected edge points would line up as edges. == Example == An example of Robinson compass masks applied to the original image. Obviously, the edges in the direction of the mask is enhanced.

Automatic image annotation

Automatic image annotation (also known as automatic image tagging or linguistic indexing) is the process by which a computer system automatically assigns metadata in the form of captioning or keywords to a digital image. This application of computer vision techniques is used in image retrieval systems to organize and locate images of interest from a database. This method can be regarded as a type of multi-class image classification with a very large number of classes - as large as the vocabulary size. Typically, image analysis in the form of extracted feature vectors and the training annotation words are used by machine learning techniques to attempt to automatically apply annotations to new images. The first methods learned the correlations between image features and training annotations. Subsequently, techniques were developed using machine translation to attempt to translate the textual vocabulary into the 'visual vocabulary,' represented by clustered regions known as blobs. Subsequent work has included classification approaches, relevance models, and other related methods. The advantages of automatic image annotation versus content-based image retrieval (CBIR) are that queries can be more naturally specified by the user. At present, Content-Based Image Retrieval (CBIR) generally requires users to search by image concepts such as color and texture or by finding example queries. However, certain image features in example images may override the concept that the user is truly focusing on. Traditional methods of image retrieval, such as those used by libraries, have relied on manually annotated images, which is expensive and time-consuming, especially given the large and constantly growing image databases in existence.