AI Art Tattoo

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  NumPy

  NumPy (pronounced NUM-py) is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays. The predecessor of NumPy, Numeric, was originally created by Jim Hugunin with contributions from several other developers. In 2005, Travis Oliphant created NumPy by incorporating features of the competing Numarray into Numeric, with extensive modifications. NumPy is open-source software and has many contributors. NumPy is fiscally sponsored by NumFOCUS. == History == === matrix-sig === The Python programming language was not originally designed for numerical computing, but attracted the attention of the scientific and engineering community early on. In 1995 the special interest group (SIG) matrix-sig was founded with the aim of defining an array computing package; among its members was Python designer and maintainer Guido van Rossum, who extended Python's syntax (in particular the indexing syntax) to make array computing easier. === Numeric === An implementation of a matrix package was completed by Jim Fulton, then expanded to support multi-dimensional arrays by Jim Hugunin and called Numeric (also variously known as the "Numerical Python extensions" or "NumPy"), with influences from the APL family of languages, Basis, MATLAB, FORTRAN, S and S+, and others. Hugunin, a graduate student at the Massachusetts Institute of Technology (MIT), joined the Corporation for National Research Initiatives (CNRI) in 1997 to work on JPython, leaving Paul Dubois of Lawrence Livermore National Laboratory (LLNL) to take over as maintainer. Other early contributors include David Ascher, Konrad Hinsen and Travis Oliphant. === Numarray === A new package called Numarray was written as a more flexible replacement for Numeric. Like Numeric, it too is now deprecated. Numarray had faster operations for large arrays, but was slower than Numeric on small ones, so for a time both packages were used in parallel for different use cases. The last version of Numeric (v24.2) was released on 11 November 2005, while the last version of numarray (v1.5.2) was released on 24 August 2006. There was a desire to get Numeric into the Python standard library, but Guido van Rossum decided that the code was not maintainable in its state then. === NumPy === In early 2005, NumPy developer Travis Oliphant wanted to unify the community around a single array package and ported Numarray's features to Numeric, releasing the result as NumPy 1.0 in 2006. This new project was part of SciPy. To avoid installing the large SciPy package just to get an array object, this new package was separated and called NumPy. Support for Python 3 was added in 2011 with NumPy version 1.5.0. In 2011, PyPy started development on an implementation of the NumPy API for PyPy. As of 2023, it is not yet fully compatible with NumPy. == Features == NumPy targets the CPython reference implementation of Python, which is a non-optimizing bytecode interpreter. Mathematical algorithms written for this version of Python often run much slower than compiled equivalents due to the absence of compiler optimization. NumPy addresses the slowness problem partly by providing multidimensional arrays and functions and operators that operate efficiently on arrays; using these requires rewriting some code, mostly inner loops, using NumPy. Using NumPy in Python gives functionality comparable to MATLAB since they are both interpreted, and they both allow the user to write fast programs as long as most operations work on arrays or matrices instead of scalars. In comparison, MATLAB boasts a large number of additional toolboxes, notably Simulink, whereas NumPy is intrinsically integrated with Python, a more modern and complete programming language. Moreover, complementary Python packages are available; SciPy is a library that adds more MATLAB-like functionality and Matplotlib is a plotting package that provides MATLAB-like plotting functionality. Although MATLAB can perform sparse matrix operations, NumPy alone cannot perform such operations and requires the use of the scipy.sparse library. Internally, both MATLAB and NumPy rely on BLAS and LAPACK for efficient linear algebra computations. Python bindings of the widely used computer vision library OpenCV utilize NumPy arrays to store and operate on data. Since images with multiple channels are simply represented as three-dimensional arrays, indexing, slicing or masking with other arrays are very efficient ways to access specific pixels of an image. The NumPy array as universal data structure in OpenCV for images, extracted feature points, filter kernels and many more vastly simplifies the programming workflow and debugging. Importantly, many NumPy operations release the global interpreter lock, which allows for multithreaded processing. NumPy also provides a C API, which allows Python code to interoperate with external libraries written in low-level languages. === The ndarray data structure === The core functionality of NumPy is its "ndarray", for n-dimensional array, data structure. These arrays are strided views on memory. In contrast to Python's built-in list data structure, these arrays are homogeneously typed: all elements of a single array must be of the same type. Such arrays can also be views into memory buffers allocated by C/C++, Python, and Fortran extensions to the CPython interpreter without the need to copy data around, giving a degree of compatibility with existing numerical libraries. This functionality is exploited by the SciPy package, which wraps a number of such libraries (notably BLAS and LAPACK). NumPy has built-in support for memory-mapped ndarrays. === Limitations === Inserting or appending entries to an array is not as trivially possible as it is with Python's lists. The np.pad(...) routine to extend arrays actually creates new arrays of the desired shape and padding values, copies the given array into the new one and returns it. NumPy's np.concatenate([a1,a2]) operation does not actually link the two arrays but returns a new one, filled with the entries from both given arrays in sequence. Reshaping the dimensionality of an array with np.reshape(...) is only possible as long as the number of elements in the array does not change. These circumstances originate from the fact that NumPy's arrays must be views on contiguous memory buffers. Algorithms that are not expressible as a vectorized operation will typically run slowly because they must be implemented in "pure Python", while vectorization may increase memory complexity of some operations from constant to linear, because temporary arrays must be created that are as large as the inputs. Runtime compilation of numerical code has been implemented by several groups to avoid these problems; open source solutions that interoperate with NumPy include numexpr and Numba. Cython and Pythran are static-compiling alternatives to these. Many modern large-scale scientific computing applications have requirements that exceed the capabilities of the NumPy arrays. For example, NumPy arrays are usually loaded into a computer's memory, which might have insufficient capacity for the analysis of large datasets. Further, NumPy operations are executed on a single CPU. However, many linear algebra operations can be accelerated by executing them on clusters of CPUs or of specialized hardware, such as GPUs and TPUs, which many deep learning applications rely on. As a result, several alternative array implementations have arisen in the scientific python ecosystem over the recent years, such as Dask for distributed arrays and TensorFlow or JAX for computations on GPUs. Because of its popularity, these often implement a subset of NumPy's API or mimic it, so that users can change their array implementation with minimal changes to their code required. A library named CuPy, accelerated by Nvidia's CUDA framework, has also shown potential for faster computing, being a 'drop-in replacement' of NumPy. == Examples == NumPy is conventionally imported as np. === Basic operations === === Universal functions === === Linear algebra === === Multidimensional arrays === === Incorporation with OpenCV === === Nearest-neighbor search === Functional Python and vectorized NumPy version. === F2PY === Quickly wrap native code for faster scripts.

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  Document capture software

  Document capture software refers to applications that provide the ability and feature set to automate the process of scanning paper documents or importing electronic documents, often for the purposes of feeding advanced document classification and data collection processes. Most scanning hardware, both scanners and copiers, provides the basic ability to scan to any number of image file formats, including: PDF, TIFF, JPG, BMP, etc. This basic functionality is augmented by document capture software, which can add efficiency and standardization to the process. == Typical features == Typical features of Document Capture Software include: Barcode recognition Patch Code recognition Separation Optical Character Recognition (OCR) Optical Mark Recognition (OMR) Quality Assurance Indexing Migration === Goal for implementation of a document capture solution === The goal for implementing a document capture solution is to reduce the amount of time spent scanning, separating, enhancing, organizing, classifying, normalizing, and collecting information from document collections, and to produce metadata along with an image/PDF file, and/or OCR text. This information is then migrated to a file share, FTP site, database, Document Management or Enterprise Content Management system. These systems often provide a search function, allowing search of the assets based on the produced metadata, and then viewed using document imaging software. == General document capture system solutions == === Integration with document management system === ECM (Enterprise Content management) and their DMS component (Document Management System) are being adopted by many organizations as a corporate document management system for all types of electronic files, e.g. MS word, PDF ... However, much of the information held by organisations is on paper and this needs to be integrated within the same document repository. By converting paper documents into digital format through scanning, organizations convert paper into image formats such as TIF, JPG, and PDF, and also extract valuable index information or business data from the document using OCR technology. Digital documents and associated metadata can easily be stored in the ECM in a variety of formats. The most popular of these formats is PDF which not only provides an accurate representation of the document but also allows all the OCR text in the document to be stored behind the PDF image. This format is known as PDF with hidden text or text-searchable PDF. This allows users to search for documents by using keywords in the metadata fields or by searching the content of PDF files across the repository. ==== Advantages of scanning documents into a ECM/DMS ==== Information held on paper is usually just as valuable to organisations as the electronic documents that are generated internally. Often this information represents a large proportion of the day to day correspondence with suppliers and customers. Having the ability to manage and share this information internally through a document management system such as SharePoint or a CMIS-compatible repository improves collaboration between departments or employees and also eliminates the risk of losing this information through disasters such as floods or fire. Organisations adopting an ECM/DMS often implement electronic workflow which allows the information held on paper to be included as part of an electronic business process and incorporated into a customer record file along with other associated office documents and emails. For business critical documents, such as purchase orders and supplier invoices, digitising documents helps speed up business transactions as well as reduce manual effort involved in keying data into business systems, such as CRM, ERP and Accounting. Scanned invoices can also be routed to managers for payment approval via email or an electronic workflow. == Electronic document capture == In the earlier implementations of Document Capture Software, the technology focused solely on the digitization and capture of information from paper documents. Document images were acquired from document scanners via TWAIN/ISIS drivers. Only image-based file formats like TIF, JPG, and BMP were typically compatible with these solutions. But in recent years, as the volume of electronically-created documents and the number of proprietary file formats continues to increase at exponential rates, the need for handling documents existing in electronic formats has grown. The relevant document capture products have adapted to function with non-image file formats with the end-goal of creating a unified processing workflow capable of handling all incoming documents The ability to import files from a variety of sources is one example of such adaptation. Importing documents from ECM/DMS software solutions, email servers, FTP, and EDI is now as much of a requirement of document capture software as is paper capture. The normalization of output files to text-based PDF format is now another critical factor in long-term archival of proprietary electronic file formats. Normalization expands access and usage of files to users throughout the enterprise, rather than only those that created the original electronic file.

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  Sedona Canada Principles

  The Sedona Canada Principles are a set of authoritative guidelines published by The Sedona Conference to aid members of the Canadian legal community involved in the identification, collection, preservation, review and production of electronically stored information (ESI). The principles were drafted by a small group of lawyers, judges and technologists called the Sedona Working Group 7 or Sedona Canada. Sedona Canada is an offshoot of The Sedona Conference which is an American "non-profit ... research and educational institute dedicated to the advanced study of law and policy in the areas of antitrust law, complex litigation, and intellectual property rights". == Background == Civil procedure in Canada is jurisdictional with each province following its own rules of civil procedure. However, each province must address the fact that due to the advancement of technology the discovery process enshrined in the rules of civil procedure can be potentially derailed due to the sheer volume of electronically stored information (ESI). When dealing with litigation matters that involve electronically stored information (ESI), the discovery process is commonly called e-discovery. The problems associated with e-discovery in Canada led to the creation of the Sedona Canada Principles. Rule 29.1.03(4) of the wikibooks:Ontario Rules of Civil Procedure specifically refers to the Sedona Canada Principles in referencing Principles re Electronic Discovery although it has been reported that this rule has been largely ignored in practice. == Summary == The Sedona Canada Principles largely refer to the processes found in the Electronic Discovery Reference Model. The principles urge proportionality due to the potentially enormous volumes of documents that may be discoverable when dealing with ESI. They also encourage good faith in the document preservation stage and regular meetings between parties to discuss the scope of the litigation. Parties are urged to be aware of the potential costs involved in producing relevant ESI but are advised that only reasonably accessible ESI need be produced. The principles stipulate that parties should not be required to search for or collect deleted material unless there is an agreement or court order related to those terms. The use of electronic tools and processes such as data sampling and web harvesting are acceptable practices. Parties are encouraged to agree early in the litigation process on production format required for the exchange of relevant documents as part of the discovery process (native files, pdf, tiff, metadata requirements etc.). Agreements or direction should be sought, if necessary, with respect to privilege or other confidential information related to production of electronic documents and data. Parties should be aware that legal precedents can be formed as a result of e-discovery practices and sanctions can be considered for a party's failure to meet their discovery obligations unless it can be demonstrated that the failure was not intentional. All parties must bear the “reasonable” costs associated with e-discovery but other arrangements can be agreed upon by the parties or by court order. == Caselaw == In Warman v. National Post Company proportionality was at issue in a case where the plaintiff was suing the defendant for libel. A motion was brought by the defendant to have the plaintiff provide a mirror image of his hard drive in an effort to prove an internet article was indeed authored by the plaintiff. Issues of proportionality and the work of the Sedona Conference and Sedona Canada Principles were factored in to the decision to grant the defendant only limited access to the hard drive. In Innovative Health Group Inc. v. Calgary Health Region the plaintiff's legal obligation to produce imaged hard drives is in question. Justice Conrad refers to the advice of Sedona Canada on proportionality and problems associated with time and expense related to the difficulties associated with electronically stored information. In York University v. Michael Markicevic Justice Brown specifically refers to the need for the parties to agree upon a formal e-discovery plan to be drafted in consultation with Sedona Canada Principles. In Friends of Lansdowne v. Ottawa Master MacLeod refers to the need for Sedona Canada principles and states “This is particularly true in the current information age when e-mail is ubiquitous and multiple copies or variants of messages may be held on various kinds of data storage devices including individual hard drives, e-mail and Blackberry servers. Even documents that ultimately exist in paper form normally begin their life on computers and negotiations frequently involve exchanges of electronic drafts. To find every scrap of paper and every electronic trace of relevant information has become a nightmarish task that threatens to render any kind of litigation extravagantly expensive.” == Criticism == Critics of the Sedona Canada Principles believe they should address system integrity and that the true history of any file preserved cannot be identified without proof of the integrity of the electronic record systems management it comes from. Other criticism is more directed to the Sedona Canada working group and complaints that it is insular and irrelevant.

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  The Citation Project

  The Citation Project is a series of studies that measure and analyze first-year college writing students' source use and their ability to understand and implement sources within their own writing. The Citation Project reveals students' source-use habits and the issues that can be seen based on their lack of proper citation skills, such as the prevalence of plagiarism, institution policies, and the results of current writing pedagogy. The Citation Project's central findings were first presented at the Conference on College Composition and Communication in 2012. Although The Citation Project originally referred to this single 2012 study, the feedback received led to the conception of the Project as a broader initiative and as a place to gather and publish studies and data relating to student writing habits for the usage of other researches. == Method == The Citation Project's data comes from the work of 20 researchers analyzing 174 first-year composition students' research papers. The student papers studied originated from 16 institutions across the United States of America, including community colleges, public and private universities, denominational colleges, and Ivy Leagues. Researchers used bibliographic coding to aggregate data regarding the type, length, reading level, and usage of students' sources. == Findings == === Student source assessment and use === This study found that students were capable of identifying, locating, and accessing librarian-approved academic sources, most commonly accessing them with the internet. Despite students demonstrating their ability to find appropriate sources, they tend to exclusively cite the first few pages of their sources. Students' use and analysis of their citations are often limited, frequently resorting to patchwriting, directly restating their source's points, and omitting their own interpretations of their reference's ideas. The Citation Project also highlights students' struggle to accurately determine, address, and value their sources' bias, authority, and credibility. According to the Project's researchers' analysis, these habits demonstrate that first-year college writing students minimally engage with their sources and the academic conversations between them. One researcher from the Citation Project, Rebecca Moore Howard, believes these findings do not point towards students being lazy, but is rather a result of a writing pedagogy that prioritizes efficient, product-focused writing. Another interpretation offered by Sandra Jamieson, another researcher from the Citation Project explains their findings as a result of a lack of adherence to Information Learning (IL) Standards. === Pedagogy === A significant focus of The Citation Project is the development of pedagogical practices intended to equip students with writing and research techniques that will set them up for future success. Writers associated with The Citation Project, such as Tricia Serviss, believe that the practices of teachers surrounding academic integrity and writing practices are what form the foundation of how students think about writing and how to engage with assignments throughout their academic career. They also stress the importance of teaching students to effectively engage with sources rather than simply how to correctly cite them. The Citation Project asserts that endowing students with the ability to read, understand, and synthesize a variety of sources in their writing is a skill that will benefit them throughout their academic careers, and that the surface level typographical focus that many writing programs utilize is inadequate. == Plagiarism == One of the areas that The Citation Project also looks at is how students commit plagiarism throughout their writing. Plagiarism tends to be a checkpoint that gives instructors a sense where students' citation skills stand. Findings from The Citation Project reveal that the most common type of plagiarism is patchwriting which is the act of using the same sentence with only changing a couple of words. These types of issues can be seen as a learning curve due to lack of proper training. Student's that commit plagiarism are often unaware. === Policies === Another issue found is that academic plagiarism policies may not benefit a student's growth but may instead obstruct it. Policies against plagiarism tend to be harsh on the student that committed of offense. Even though student plagiarism is often unintentional academic institutions see this behavior as intentional. Student may then face harsh consequences as a result from their lack of citation knowledge. Additionally, higher level institutions assume that new students already have the skill set to avoid plagiarism which may be an unrealistic expectation. == Legacy == === Inspired studies === ==== Parrott and Napier ==== In one study, "Critical Reading and Student Self-Selected Texts: Results of a Collaborative, Explicit Curricular Approach," Jill Parrot and Trenia Napier quoted the Citation Project's findings as evidence that current collegiate writing curriculums are an ineffective means of teaching students how to properly write academic research papers. The researchers accredited current writing pedagogy's lack of emphasis on teaching critical reading skills. Parrott and Napier tested their thesis by seeing if students would produce more academic writing if they partook in a writing course that taught critical reading. Their results mostly went against this hypothesis, finding students who received additional critical reading training only significantly improved in how they integrated their sources. ==== Kocatepe ==== In May Mehtap Kocatep's study, "Reconceptualising the notion of finding information: How undergraduate students construct information as they read-to-write in an academic writing class," Kocatep expresses that she believes current conversations around writing pedagogy, including the Citation Project, operate with the underlying misconception that information is an easily discoverable static entity and its retrieval is an objective, unbiased decision. Kocatepe instead offers the analysis of what students view as valuable information and if it is worth using is influenced by the socially constructed meanings available to writers at the moment. To further examine students' source engagement, Kocatepe did a study on how female university students from the United Arab Emirates find, retrieve, use, and value sources. Kocatepe's results mainly noted students' almost exclusive reliance on using Google to find sources, as well as how students' navigated mainly English-speaking academic conversations as non-native English speakers. ==== Dahlen, Nordstrom-Sanchez, and Graff ==== Dahlen, Nordstrom-Sanchez, and Graff built their study off The Citation Project research in order to explore the attitudes and practices of students in an undergraduate writing course. As the researchers acknowledge, data collected by the Citation Project was the subject of the bulk of their analysis. This study sought to examine undergraduate writing practices tied to source-usage and elucidate any relevant trends. Dahlen, Nordstrom-Sanchez and Graff found that undergraduate writing students were not engaging with outside sources properly. Key issues discussed include lack of engagement with broad source ideas (in favor of picking out quotes), lack of paraphrasing, and inability to link information between multiple sources. ==== Davis ==== Phillip M. Davis based much of the analysis in his study on data gathered by the Citation Project. This study aimed to examine the particular effects web-based research and study had on undergraduate's papers and the replicability of their bibliographies. Davis sought to see how the shift from physical in-person library based research to online, often at-home research changed the function and usability of the bibliography as a form of documenting source usage in a given work. The primary method of analysis involved examining students' bibliographies to see where they were finding information online and how these sources were accessed. A main issue Davis found was "persistency" of URLs used for online citations. He found that only 18% of URL-based citations continued to function (the others either no longer pointing to the correct document or ceasing to exist altogether) within 3 years of their usage by students, and more than half of claimed online citations could not be found in any form. He suggests that this result brings up questions about how web-based citations should be dealt with in a university context.

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  Hekaton (database)

  Hekaton (also known as SQL Server In-Memory OLTP) is an in-memory database for OLTP workloads built into Microsoft SQL Server. Hekaton was designed in collaboration with Microsoft Research and was released in SQL Server 2014. Traditional RDBMS systems were designed when memory resources were expensive, and were optimized for disk storage. Hekaton is instead optimized for a working set stored entirely in main memory, but is still accessible via T-SQL like normal tables. It is fundamentally different from the "DBCC PINTABLE" feature in earlier SQL Server versions. Hekaton was announced at the Professional Association for SQL Server (PASS) conference 2012.

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  Sieve of Pritchard

  In mathematics, the sieve of Pritchard is an algorithm for finding all prime numbers up to a specified bound. Like the ancient sieve of Eratosthenes, it has a simple conceptual basis in number theory. It is especially suited to quick hand computation for small bounds. Whereas the sieve of Eratosthenes marks off each non-prime for each of its prime factors, the sieve of Pritchard avoids considering almost all non-prime numbers by building progressively larger wheels, which represent the pattern of numbers not divisible by any of the primes processed thus far. It thereby achieves a better asymptotic complexity, and was the first sieve with a running time sublinear in the specified bound. Its asymptotic running-time has not been improved on, and it deletes fewer composites than any other known sieve. It was created in 1979 by Paul Pritchard. Since Pritchard has created a number of other sieve algorithms for finding prime numbers, the sieve of Pritchard is sometimes singled out by being called the wheel sieve (by Pritchard himself) or the dynamic wheel sieve. == Overview == A prime number is a natural number that has no natural number divisors other than the number 1 and itself. To find all the prime numbers less than or equal to a given integer N, a sieve algorithm examines a set of candidates in the range 2, 3, …, N, and eliminates those that are not prime, leaving the primes at the end. The sieve of Eratosthenes examines all of the range, first removing all multiples of the first prime 2, then of the next prime 3, and so on. The sieve of Pritchard instead examines a subset of the range consisting of numbers that occur on successive wheels, which represent the pattern of numbers left after each successive prime is processed by the sieve of Eratosthenes. For i > 0, the ith wheel Wi represents this pattern. It is the set of numbers between 1 and the product Pi = p1 · p2 ⋯ pi of the first i prime numbers that are not divisible by any of these prime numbers (and is said to have an associated length Pi). This is because adding Pi to a number does not change whether it is divisible by one of the first i prime numbers, since the remainder on division by any one of these primes is unchanged. So W1 = {1} with length P1 = 2 represents the pattern of odd numbers; W2 = {1,5} with length P2 = 6 represents the pattern of numbers not divisible by 2 or 3; etc. Wheels are so-called because Wi can be usefully visualized as a circle of circumference Pi with its members marked at their corresponding distances from an origin. Then rolling the wheel along the number line marks points corresponding to successive numbers not divisible by one of the first i prime numbers. The animation shows W2 being rolled up to 30. It is useful to define Wi → n for n > 0 to be the result of rolling Wi up to n. Then the animation generates W2 → 30 = {1,5,7,11,13,17,19,23,25,29}. Note that up to 52 − 1 = 24, this consists only of 1 and the primes between 5 and 25. The sieve of Pritchard is derived from the observation that this holds generally: for all i > 0, the values in Wi → (p2i+1 − 1) are 1 and the primes between pi+1 and p2i+1. It even holds for i = 0, where the wheel has length 1 and contains just 1 (representing all the natural numbers). So the sieve of Pritchard starts with the trivial wheel W0 and builds successive wheels until the square of the wheel's first member after 1 is at least N. Wheels grow very quickly, but only their values up to N are needed and generated. It remains to find a method for generating the next wheel. Note in the animation that W3 = {1,5,7,11,13,17,19,23,25,29} − {5 · 1 , 5 · 5} can be obtained by rolling W2 up to 30 and then removing 5 times each member of W2.This also holds generally: for all i ≥ 0, Wi+1 = (Wi → Pi+1) − {pi+1 · w | w ∈ Wi}. Rolling Wi past Pi just adds values to Wi, so the current wheel is first extended by getting each successive member starting with w = 1, adding Pi to it, and inserting the result in the set. Then the multiples of pi+1 are deleted. Care must be taken to avoid a number being deleted that itself needs to be multiplied by pi+1. The sieve of Pritchard as originally presented does so by first skipping past successive members until finding the maximum one needed, and then doing the deletions in reverse order by working back through the set. This is the method used in the first animation above. A simpler approach is just to gather the multiples of pi+1 in a list, and then delete them. Another approach is given by Gries and Misra. If the main loop terminates with a wheel whose length is less than N, it is extended up to N to generate the remaining primes. The algorithm, for finding all primes up to N, is therefore as follows: Start with a set W = {1} and length = 1 representing wheel 0, and prime p = 2. As long as p2 ≤ N, do the following: if length < N, then extend W by repeatedly getting successive members w of W starting with 1 and inserting length + w into W as long as it does not exceed p · length or N; increase length to the minimum of p · length and N. repeatedly delete p times each member of W by first finding the largest ≤ length and then working backwards. note the prime p, then set p to the next member of W after 1 (or 3 if p was 2). if length < N, then extend W to N by repeatedly getting successive members w of W starting with 1 and inserting length + w into W as long as it does not exceed N; On termination, the rest of the primes up to N are the members of W after 1. === Example === To find all the prime numbers less than or equal to 150, proceed as follows. Start with wheel 0 with length 1, representing all natural numbers 1, 2, 3...: 1 The first number after 1 for wheel 0 (when rolled) is 2; note it as a prime. Now form wheel 1 with length 2 × 1 = 2 by first extending wheel 0 up to 2 and then deleting 2 times each number in wheel 0, to get: 1 2 The first number after 1 for wheel 1 (when rolled) is 3; note it as a prime. Now form wheel 2 with length 3 × 2 = 6 by first extending wheel 1 up to 6 and then deleting 3 times each number in wheel 1, to get 1 2 3 5 The first number after 1 for wheel 2 is 5; note it as a prime. Now form wheel 3 with length 5 × 6 = 30 by first extending wheel 2 up to 30 and then deleting 5 times each number in wheel 2 (in reverse order), to get 1 2 3 5 7 11 13 17 19 23 25 29 The first number after 1 for wheel 3 is 7; note it as a prime. Now wheel 4 has length 7 × 30 = 210, so we only extend wheel 3 up to our limit 150. (No further extending will be done now that the limit has been reached.) We then delete 7 times each number in wheel 3 until we exceed our limit 150, to get the elements in wheel 4 up to 150: 1 2 3 5 7 11 13 17 19 23 25 29 31 37 41 43 47 49 53 59 61 67 71 73 77 79 83 89 91 97 101 103 107 109 113 119 121 127 131 133 137 139 143 149 The first number after 1 for this partial wheel 4 is 11; note it as a prime. Since we have finished with rolling, we delete 11 times each number in the partial wheel 4 until we exceed our limit 150, to get the elements in wheel 5 up to 150: 1 2 3 5 7 11 13 17 19 23 25 29 31 37 41 43 47 49 53 59 61 67 71 73 77 79 83 89 91 97 101 103 107 109 113 119 121 127 131 133 137 139 143 149 The first number after 1 for this partial wheel 5 is 13. Since 13 squared is at least our limit 150, we stop. The remaining numbers (other than 1) are the rest of the primes up to our limit 150. Just 8 composite numbers are removed, once each. The rest of the numbers considered (other than 1) are prime. In comparison, the natural version of Eratosthenes sieve (stopping at the same point) removes composite numbers 184 times. == Pseudocode == The sieve of Pritchard can be expressed in pseudocode, as follows: algorithm Sieve of Pritchard is input: an integer N >= 2. output: the set of prime numbers in {1,2,...,N}. let W and Pr be sets of integer values, and all other variables integer values. k, W, length, p, Pr := 1, {1}, 2, 3, {2}; {invariant: p = pk+1 and W = Wk ∩ {\displaystyle \cap } {1,2,...,N} and length = minimum of Pk,N and Pr = the primes up to pk} while p2 <= N do if (length < N) then Extend W,length to minimum of plength,N; Delete multiples of p from W; Insert p into Pr; k, p := k+1, next(W, 1) if (length < N) then Extend W,length to N; return Pr ∪ {\displaystyle \cup } W - {1}; where next(W, w) is the next value in the ordered set W after w. procedure Extend W,length to n is {in: W = Wk and length = Pk and n > length} {out: W = Wk → {\displaystyle \rightarrow } n and length = n} integer w, x; w, x := 1, length+1; while x <= n do Insert x into W; w := next(W,w); x := length + w; length := n; procedure Delete multiples of p from W,length is integer w; w := p; while pw <= length do w := next(W,w); while w > 1 do w := prev(W,w); Remove pw from W; where prev(W, w) is the previous value in the ordered set W before w. The algorithm can be initialized with W0 instead of W1 at the minor complication of making next(W, 1) a special case when k = 0. This a

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  AIVA

  AIVA (Artificial Intelligence Virtual Artist) is an electronic composer recognized by the SACEM. == Description == Created in February 2016, AIVA specializes in classical and symphonic music composition. It became the world's first virtual composer to be recognized by a music society (SACEM). By reading a large collection of existing works of classical music (written by human composers such as Bach, Beethoven, Mozart) AIVA is capable of detecting regularities in music and on this base composing on its own. The algorithm AIVA is based on deep learning and reinforcement learning architectures. Since January 2019, the company offers a commercial product, Music Engine, capable of generating short (up to 3 minutes) compositions in various styles (rock, pop, jazz, fantasy, shanty, tango, 20th century cinematic, modern cinematic, and Chinese). AIVA was presented at TED by Pierre Barreau. == Discography == AIVA is a published composer; its first studio album "Genesis" was released in November 2016. Second album "Among the Stars" in 2018. 2016 CD album « Genesis » Hv-Com – LEPM 048427. Track listing "Genesis": 2018 CD album « Among the Stars » Hv-Com – LEPM 048708 Avignon Symphonic Orchestra [ORAP] also performed Aiva's compositions [2] in April 2017.

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  Information literacy

  The Association of College and Research Libraries defines information literacy as a "set of integrated abilities encompassing the reflective discovery of information, the understanding of how information is produced and valued and the use of information in creating new knowledge and participating ethically in communities of learning". In the United Kingdom, the Chartered Institute of Library and Information Professionals' definition also makes reference to knowing both "when" and "why" information is needed. The 1989 American Library Association (ALA) Presidential Committee on Information Literacy formally defined information literacy (IL) as attributes of an individual, stating that "to be information literate, a person must be able to recognize when information is needed and have the ability to locate, evaluate and use effectively the needed information". In 1990, academic Lori Arp published a paper asking, "Are information literacy instruction and bibliographic instruction the same?" Arp argued that neither term was particularly well defined by theoreticians or practitioners in the field. Further studies were needed to lessen the confusion and continue to articulate the parameters of the question. The Alexandria Proclamation of 2005 defined the term as a human rights issue: "Information literacy empowers people in all walks of life to seek, evaluate, use and create information effectively to achieve their personal, social, occupational and educational goals. It is a basic human right in a digital world and promotes social inclusion in all nations." The United States National Forum on Information Literacy defined information literacy as "the ability to know when there is a need for information, to be able to identify, locate, evaluate, and effectively use that information for the issue or problem at hand." Meanwhile, in the UK, the library professional body CILIP, define information literacy as "the ability to think critically and make balanced judgements about any information we find and use. It empowers us as citizens to develop informed views and to engage fully with society." A number of other efforts have been made to better define the concept and its relationship to other skills and forms of literacy. Other pedagogical outcomes related to information literacy include traditional literacy, computer literacy, research skills and critical thinking skills. Information literacy as a sub-discipline is an emerging topic of interest and counter measure among educators and librarians with the prevalence of misinformation, fake news, and disinformation. Scholars have argued that in order to maximize people's contributions to a democratic and pluralistic society, educators should be challenging governments and the business sector to support and fund educational initiatives in information literacy. == History == The phrase "information literacy" first appeared in print in a 1974 report written on behalf of the National Commission on Libraries and Information Science by Paul G. Zurkowski, who was at the time president of the Information Industry Association (now the Software and Information Industry Association). Zurkowski used the phrase to describe the "techniques and skills" learned by the information literate "for utilizing the wide range of information tools as well as primary sources in molding information solutions to their problems" and drew a relatively firm line between the "literates" and "information illiterates." The concept of information literacy appeared again in a 1976 paper by Lee Burchina presented at the Texas A&M University library's symposium. Burchina identified a set of skills needed to locate and use information for problem solving and decision making. In another 1976 article in Library Journal, M.R. Owens applied the concept to political information literacy and civic responsibility, stating, "All [people] are created equal but voters with information resources are in a position to make more intelligent decisions than citizens who are information illiterates. The application of information resources to the process of decision-making to fulfill civic responsibilities is a vital necessity." In a literature review published in an academic journal in 2020, Oral Roberts University professor Angela Sample cites several conceptual waves of information literacy definitions as defining information as a way of thinking, a set of skills, and a social practice. The introduction of these concepts led to the adoption of a mechanism called metaliteracy and the creation of threshold concepts and knowledge dispositions, which led to the creation of the ALA's Information Literacy Framework. The American Library Association's Presidential Committee on Information Literacy released a report on January 10, 1989. Titled as the Presidential Committee on Information Literacy: Final Report, the article outlines the importance of information literacy, opportunities to develop it, and the idea of an Information Age School. The recommendations of the Committee led to establishment of the National Forum on Information Literacy, a coalition of more than 90 national and international organizations. In 1998, the American Association of School Librarians and the Association for Educational Communications and Technology published Information Power: Building Partnerships for Learning, which further established specific goals for information literacy education, defining some nine standards in the categories of "information literacy," "independent learning," and "social responsibility." Also in 1998, the Presidential Committee on Information Literacy updated its final report. The report outlined six recommendations from the original report, and examined areas of challenge and progress. In 1999, the Society of College, National and University Libraries (SCONUL) in the UK published The Seven Pillars of Information Literacy to model the relationship between information skills and IT skills, and the idea of the progression of information literacy into the curriculum of higher education. In 2003, the National Forum on Information Literacy, along with UNESCO and the National Commission on Libraries and Information Science, sponsored an international conference in Prague. Representatives from twenty-three countries gathered to discuss the importance of information literacy in a global context. The resulting Prague Declaration described information literacy as a "key to social, cultural, and economic development of nations and communities, institutions and individuals in the 21st century" and declared its acquisition as "part of the basic human right of lifelong learning". In the United States specifically, information literacy was prioritized in 2009 during President Barack Obama's first term. In effort to stress the value information literacy has on everyday communication, he designated October as National Information Literacy Awareness Month in his released proclamation. In 2015, the Association of College and Research Libraries (ACRL) adopted the Framework for Information Literacy for Higher Education, which defines information literacy as "the set of integrated abilities encompassing the reflective discovery of information, the understanding of how information is produced and valued, and the use of information in creating new knowledge and participating ethically in communities of learning".Association of College and Research Libraries (2015-02-09). "Framework for Information Literacy for Higher Education". Association of College and Research Libraries. American Library Association. Retrieved 2026-02-17. == Presidential Committee on Information Literacy == The American Library Association's Presidential Committee on Information Literacy defined information literacy as the ability "to recognize when information is needed and have the ability to locate, evaluate, and use effectively the needed information" and highlighted information literacy as a skill essential for lifelong learning and the production of an informed and prosperous citizenry. The committee outlined six principal recommendations. Included were recommendations like "Reconsider the ways we have organized information institutionally, structured information access, and defined information's role in our lives at home in the community, and in the work place"; to promote "public awareness of the problems created by information illiteracy"; to develop a national research agenda related to information and its use; to ensure the existence of "a climate conducive to students' becoming information literate"; to include information literacy concerns in teacher education democracy. In the updated report, the committee ended with an invitation, asking the National Forum and regular citizens to recognize that "the result of these combined efforts will be a citizenry which is made up of effective lifelong learners who can always find the information needed for the issue or decision at hand. This new

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  ARIS Express

  ARIS Express is a free-of-charge modeling tool for business process analysis and management. It supports different modeling notations such as BPMN 2, Event-driven Process Chains (EPC), Organizational charts, process landscapes, whiteboards, etc. ARIS Express was initially developed by IDS Scheer, which was bought by Software AG in December 2010. The tool is provided as freeware on the ARIS Community webpage. ARIS Express is notable - having been mentioned in research published by Schumm, Garcia, Krumnow and Greenwood amongst others. == History == ARIS Express was first announced on April 28, 2009 in a press release by IDS Scheer. The first release was on July 28, 2009 in a public beta test on ARIS Community. Only people, who registered before for the beta test were allowed to download and test this beta version. This closed beta test was followed with another public beta test. The official release of ARIS Express 1.0 was on September 9, 2009. In this first stable version, features such as Microsoft Visio import were added, which were not present in the version for the public beta test. On February 26, 2010, ARIS Express 2.0 was released. Major changes compared to version 1.0 include BPMN 2 support, integrated spellchecking and ARISalign integration. On May 25, 2010, version 2.1 of ARIS Express was released. This update improves BPMN 2 support, provides a new online help system for instant feedback, better ARISalign integration and some new symbols in different diagrams. Along with the release, a poster showing the most important modeling concepts supported by ARIS Express was released. In addition, an executable setup is provided for Microsoft Windows-based systems. Beginning of July, an update was released as ARIS Express 2.2, providing bug fixes only. ARIS Express version 2.2 is the current stable release. An official press release published mid of August 2010 said there are more than 50,000 downloads of ARIS Express. On February 2, 2011, version 2.3 of ARIS Express was released. This new version changes the file format of ARIS Express so that models can be shown in an interactive model viewer in ARIS Community. The release announcement contained no details about additional features or changes. == Functionality == === Overview === ARIS Express is a standalone single-user application. It is divided in a home screen and a modeling environment. The home screen is used to create new models or open recently edited ones. The modeling environment is used to edit diagrams. === Supported notations === The following notations are supported by ARIS Express. Users can create diagrams containing an unlimited number of modeling objects. BPMN 2 Collaboration Diagrams Event-driven Process Chains (EPC) Organizational charts Process landscape (value-added chain diagram) Data model in ERM notation IT infrastructure (network diagram) System landscape (component diagram) Whiteboard General diagram === Noteworthy features === Besides common features such as creating new diagrams, saving them as files or adding objects to the modeling canvas, ARIS Express also provides some noteworthy features, which can't be found in most comparable modeling tools. fragments - Often used modeling constructs such as an exclusive decision in a process model can be stored as fragments so that they are available for direct reuse in another model. smart designs - The flow of a process model or hierarchies of other models can be captured in a spreadsheet-like interface. While entering the data in the spreadsheet, the model is generated and laid out in the background while typing. mini toolbar - While moving the mouse pointer over an object in a diagram, a small toolbar is shown allowing quick access to the most important modeling actions. Microsoft Visio import - Diagrams created with Microsoft Visio 2007 or above can be imported to and edited in ARIS Express. A Microsoft Visio export is not provided. ARISalign import - Models created on the online collaboration platform ARISalign can be opened and edited in ARIS Express. === Exports === ARIS Express can export diagrams to different formats such as: PDF JPEG PNG EMF ADF ADF is the file format of ARIS Express. The professional tools of ARIS Platform are able to import diagrams stored in the ADF format. Yet, there are major limitations during import - namely, each object in diagram will be treated as unique object, despite having same type and name, forcing redrawing large sections of diagrams after import. Besides export formats, it is also possible to use the clipboard to copy and paste an ARIS Express diagram into typical office suites such as Microsoft PowerPoint. == Technology == ARIS Express is a Java-based application, which shares some of the features of ARIS Platform products such as ARIS Business Architect and ARIS Business Designer. In contrast to ARIS Platform products, ARIS Express doesn't use a central database for model storage. Instead, each diagram is stored in an ADF file. ARIS Express uses Java Web Start. After download, the application can be started immediately without installation procedure. For Microsoft Windows based systems, an ordinary setup is provided, too. ARIS Express requires Java 1.6.10 or above. On first startup, the user must enter a valid ARIS Community account to register the application. Creating an ARIS Community account is free-of-charge. After installation, no Internet connection is needed to use ARIS Express. ARIS Express uses a mechanism provided by Java Web Start to automatically update the application as soon as a new version becomes available and the user is connected to the Internet during startup. There are reports that this automated update failed while upgrading from version 1.0 to version 2.0. As ARIS Express is based on Java Web Start, it can be installed on any platform supported by Java. The ARIS Community and other Internet sources have reports of successful deployment of ARIS Express on other operating systems than Microsoft Windows. However, ARIS Express is officially supported only on Microsoft Windows. == Miscellaneous == A quick reference sheet is available for ARIS Express. The poster shows all supported diagrams plus the most important modelling concepts for each supported modelling language. ARIS Express contains a hidden game, a so-called Easter Egg. The game can be started by clicking several times on the product logo in the about dialog. Highscores achieved in the game can be submitted to a special page in ARIS Community. A Firefox Personas is available for ARIS Express.

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  Affective computing

  Affective computing is the study and development of systems and devices that can recognize, interpret, process, and simulate human affects. It is an interdisciplinary field spanning computer science, psychology, and cognitive science. While some core ideas in the field may be traced as far back as to early philosophical inquiries into emotion, the modern idea originated with Rosalind Picard's 1995 paper entitled "Affective Computing" and her 1997 book of the same name published by MIT Press. One motivation for researching affective computing is the ability to give machines emotional intelligence, including simulating empathy. The goal is that a machine should interpret the emotional state of humans and adapt its behavior to those emotions, responding appropriately. Recent experimental research has shown that subtle affective haptic feedback can shape human reward learning and mobile interaction behavior, suggesting that affective computing systems may not only interpret emotional states but also actively modulate user actions through emotion-laden outputs. == Areas == === Detecting and recognizing emotional information === Detecting emotional information usually begins with passive sensors that capture data about the user's physical state or behavior without interpreting the input. The data gathered is analogous to the cues humans use to perceive emotions in others. For example, a video camera might capture facial expressions, body posture, and gestures, while a microphone might capture speech. Other sensors detect emotional cues by directly measuring physiological data, such as skin temperature and galvanic resistance. Recognizing emotional information requires the extraction of meaningful patterns from the gathered data. This is done using machine learning techniques that process different modalities, such as speech recognition, natural language processing, or facial expression detection. The goal of most of these techniques is to produce labels that would match the labels a human would give in the same situation. For example, if a person makes a facial expression furrowing their brow, then the computer vision system might be trained to label their face as appearing "confused" or as "concentrating" or "slightly negative" (as opposed to positive, which it might say if they were smiling in a happy-appearing way). This response is based on the data used to train the system. These labels may or may not correspond to what the person is actually feeling. === Emotion in machines === Another area within affective computing is the design of computational devices proposed to exhibit either innate emotional capabilities or that are capable of convincingly simulating emotions. A more practical approach, based on current technological capabilities, is the simulation of emotions in conversational agents in order to enrich and facilitate interactivity between human and machine. Marvin Minsky, one of the pioneering computer scientists in artificial intelligence, relates emotions to the broader issues of machine intelligence stating in The Emotion Machine that emotion is "not especially different from the processes that we call 'thinking.'" The innovative approach "digital humans" or virtual humans includes an attempt to give these programs, which simulate humans, an emotional dimension as well, including reactions, facial expressions, and gestures in accordance with the reaction that a real person would have in certain emotionally stimulating situations. Emotion in machines often refers to emotion in computational, often AI-based, systems. As a result, the terms 'emotional AI' is being used. Some modern large language models simulate emotions in their chats with humans. ChatGPT's simulated emotion leans more positive than that of most human responses. == Technologies == In psychology, cognitive science, and in neuroscience, there have been two main approaches for describing how humans perceive and classify emotion: continuous or categorical. The continuous approach tends to use dimensions such as negative vs. positive, calm vs. aroused. The categorical approach tends to use discrete classes such as happy, sad, angry, fearful, surprise, and disgust. Different kinds of machine learning regression and classification models are used for machines to produce continuous or discrete labels. Sometimes, models are also built that allow combinations across the categories (e.g. a happy-surprised face or a fearful-surprised face). The following sections consider many of the kinds of input data used for the task of emotion recognition. === Emotional speech === Various changes in the autonomic nervous system can indirectly alter a person's speech, and affective technologies can leverage this information to recognize emotion. For example, speech produced in a state of fear, anger, or joy becomes fast, loud, and precisely enunciated, with a higher and wider range in pitch, whereas emotions such as tiredness, boredom, or sadness tend to generate slow, low-pitched, and slurred speech. Some emotions have been found to be more easily computationally identified, such as anger or approval. Emotional speech processing technologies recognize the user's emotional state using computational analysis of speech features. Vocal parameters and prosodic features such as pitch variables and speech rate can be analyzed through pattern recognition techniques. Speech analysis is an effective method of identifying affective state, having an average reported accuracy of 70-80% in research from 2003 and 2006. These systems tend to outperform average human accuracy (approximately 60%) but are less accurate than systems which employ other modalities for emotion detection, such as physiological states or facial expressions. However, since many speech characteristics are independent of semantics or culture, this technique is considered to be a promising route for further research. ==== Algorithms ==== The process of speech/text affect detection requires the creation of a reliable database, knowledge base, or vector space model, broad enough to fit every need for its application, as well as the selection of a successful classifier which will allow for quick and accurate emotion identification. As of 2010, the most frequently used classifiers were linear discriminant classifiers (LDC), k-nearest neighbor (k-NN), Gaussian mixture model (GMM), support vector machines (SVM), artificial neural networks (ANN), decision tree algorithms, and hidden Markov models (HMMs). Various studies showed that choosing the appropriate classifier can significantly enhance the overall performance of the system. The list below gives a brief description of each algorithm: LDC – Classification happens based on the value obtained from the linear combination of the feature values, which are usually provided in the form of vector features. k-NN – Classification happens by locating the object in the feature space, and comparing it with the k nearest neighbors (training examples). The majority vote decides on the classification. GMM – A probabilistic model used for representing the existence of subpopulations within the overall population. Each sub-population is described using the mixture distribution, which allows for classification of observations into the sub-populations. SVM – A type of (usually binary) linear classifier which decides in which of the two (or more) possible classes, each input may fall into. ANN – is a mathematical model, inspired by biological neural networks, that can better grasp possible non-linearities of the feature space. Decision tree algorithms – work based on following a decision tree in which leaves represent the classification outcome, and branches represent the conjunction of subsequent features that lead to the classification. HMMs – a statistical Markov model in which the states and state transitions are not directly available to observation. Instead, the series of outputs dependent on the states are visible. In the case of affect recognition, the outputs represent the sequence of speech feature vectors, which allow the deduction of states' sequences through which the model progressed. The states can consist of various intermediate steps in the expression of an emotion, and each of them has a probability distribution over the possible output vectors. The states' sequences allow us to predict the affective state which we are trying to classify, and this is one of the most commonly used techniques within the area of speech affect detection. It has been proven that having enough acoustic evidence available the emotional state of a person can be classified by a set of majority voting classifiers. The proposed set of classifiers is based on three main classifiers: kNN, C4.5 and SVM-RBF Kernel. This set achieves better performance than each basic classifier taken separately. It is compared with two other sets of classifiers: one-against-all (OAA) multiclass SVM with Hybrid kernels and th

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  AI: When a Robot Writes a Play

  AI: When a Robot Writes a Play (in Czech: AI: Když robot píše hru) is a 2021 experimental theatre play, where 90% of its script was automatically generated by artificial intelligence (the GPT-2 language model). The play is in Czech language, but an English version of the script also exists. == Creation == The play is the first result of the THEaiTRE research project, aiming to commemorate the centenary of the R.U.R. play by Karel Čapek by investigating to what extent artificial intelligence could be used to create theatre play scripts. The script of the play was created using the THEaiTRobot tool, based on the GPT-2 language model. First, the play dramaturge, David Košťák, described the initial setting of each scene in a few sentences, and wrote the first line for each character. Next, THEaiTRobot suggested a continuation of the script, which the dramaturge could use, reject, or use part of it and let the tool generate a new continuation. Another option was to manually insert another line or a scenic remark. The script was generated in English and was automatically translated to Czech by the state-of-the-art CUBBITT machine translation tool. The resulting script was then further post-edited by the dramaturge. The resulting script was made freely available for non-commercial use both in English and in Czech, with marked manually inserted texts and manual edits. The analysis shows that 90% of the English script is automatically generated, with 10% manually written or manually post-edited. In the Czech script, a larger amount of edits were made, but the analysis claims that these additional edits are corrections of errors of the automated translation and stylistic corrections which do not change the meaning of the lines as represented by the English script, but rather bring the Czech script closer to the English one. == Characters == The play contains 9 characters. The Robot appears in all the scenes, while each of the other characters appears in only one scene. Robot – The lead character, a male humanoid robot. Master – An old man, the creator of the Robot. Boy – A schoolboy. Masseuse – A sex worker in a brothel. Stranger – An engineer. Man. Psychologist. Administrator – A female clerk at an employment agency. Actress – A film actress and a model in a robot-like costume. == Plot == The play is composed of 8 scenes. It tells the story of a humanoid robot, who encounters 8 other characters and engages into various typically human situations and activities, related to death, love, sex, violence, etc. The individual scenes are not tightly linked, but there are some linking points, such as the central character of the robot or some repeated and developing themes, such as the robot's search for love. The scenes often contain some absurd turns and it is often hard to find sense in them. It is therefore a very complicated piece interpretationally, requiring the director and the actors to invest a lot of effort and creativity in finding a meaningful interpretation which would not deviate from the script. In the interpretation by Švanda theatre, who premiered the play and who also participated on the creation of the script, the scenes typically contain non-verbally expressed content which can add a lot to the meaning of the scene compared to what is contained in the actual script (as the script only contains the lines said by the characters). === Scene 1: Death === The play opens by the Robot parting with his dying Master. The Master gives the Robot several last lessons and talks with him about death, soul, and love. === Scene 2: Sense of Humour === In the second scene, the Robot meets a sad and angry Boy, who complains that he wants to go to school, that his girlfriend is crazy, that he wants to buy a car, etc. The Robot tries to help the Boy by giving him advice, but the Boy's reactions are quite negative and irritated. The Boy then repeatedly asks the Robot to tell him a joke; the Robot keeps refusing, but ultimately tells the following joke: When you are dead. When your children are dead. When your grandchildren are dead, I will be still alive. === Scene 3: Nightclub === The Robot wants to feel pleasure, so he goes to a "night club" (a brothel), where he meets a "Masseuse" (a prostitute). The Robot is initially "a bit cold", but eventually manages to enjoy the experience and falls in love with the Masseuse. In the Švanda theatre performance, the Robot and the Masseuse seem to have a sort of virtual sex without touching each other, reminiscent of the sex scene in Demolition Man. === Scene 4: Fear of the Dark === It is the night. The Robot is standing under a lamp, unable to move away from the light as he finds that he is afraid of the dark. He meets a Stranger, an engineer who tells him that robots don't have feelings and that people cannot be trusted, and keeps hurting him. In the Švanda theatre performance, the Man repeatedly zaps the Robot with some kind of electric pulse. === Scene 5: Killer Robot === A Man approaches the Robot and repeatedly asks him to kill him. Instead, the Robot sticks a finger into the Man's anus, which leads to an argument between the Man and the Robot. === Scene 6: Burn Out === The Robot meets a Psychologist, who keeps asking him lots of questions regarding his life, burnout feeling, love, relationships, and emotions. They also talk about the Robot using a device called emotion machine which helps him to get rid of stress. === Scene 7: Search for Job === The Robot comes to an employment agency. He meets an Administrator and asks her to help him find a job. He expresses the wish to become an actor, and talks about his experience as a clown. He reveals his name to be Troy McClure, which is a character from The Simpsons who is an actor. In the Švanda theatre performance, the Administrator starts to seduce the Robot once his name is revealed, which he keeps ignoring; the Administrator then becomes irritated. === Scene 8: Love at First Sight === The Robot meets a human Actress in a robotic costume and falls in love with her immediately. The Actress is first reluctant, but the Robot manages to seduce her and she also falls in love with him. The Robot tells her about a binary world, in which he lives and where he will also take her. Ultimately, the Actress agrees, and the whole play concludes by the Robot and the Actress promising each to other to always be together. In the Švanda theatre performance, the Robot does not have a physical body in this scene, we can only hear his voice and see a pulsating light (based on the line in the script where the Robot says: "I have no body. So I don't need to wear clothes. You can't see me, you only hear me."), and the Actress eventually also agrees to lose her physical body so that she can be with the Robot forever. == Theatrical performances == The play premiered on 26 February 2021 in Švanda Theatre in Prague, Czech Republic, directed by Daniel Hrbek. Due to the COVID-19 pandemic, the play was not played in front of a live audience, but it was broadcast online, in Czech language with English subtitles. The play was followed by a panel discussion by the project members and experts on artificial intelligence. The premiere was viewed by 13,498 spectators worldwide. A short trailer of the premiere is available on YouTube. In 2021, after the opening of the theatres in the Czech Republic to spectators, the play can be viewed at Švanda Theatre. The performance takes approximately 60 minutes, and is followed by a discussion of the creators with the audience. The derniere is planned for 4 February 2023. == Reception == The play received a number of reviews, both in its country of origin as well as internationally. It is praised as first of its kind, although some reviewers note the similarity to previous works, such as the musical Beyond the Fence, the play Lifestyle of the Richard and Family, or the short movie Sunspring; however, these works used less advanced technology, and either were very short (Sunspring) or necessitated a larger amount of human interventions. The reviewers note that the script is far from perfect, with many inconsistencies and nonsensical parts, and conclude that the technology is definitely not yet ready to replace human authors; however, some find some parts of the script frighteningly human-like. The amount of human intervention is a somewhat controversial topic, with some reviewers finding the human influence too large (especially in interpreting the script and putting the play on scene), while others feel that a greater amount of human intervention would have been favorable as this could greatly improve the quality of the play. The reviews also frequently comment on the amount of sex, violence and strong language in the play; this can be attributed to the method used for creating the script, where the GPT-2 language model reflects topics and language common in the human-written articles on the internet that were used to train the model. Furthermore, some r

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  Manhattan address algorithm

  The Manhattan address algorithm is a series of formulas used to estimate the closest east–west cross street for building numbers on north–south avenues in the New York City borough of Manhattan. == Algorithm == To find the approximate number of the closest cross street, divide the building number by a divisor (generally 20) and add (or subtract) the "tricky number" from the table below: For the north–south avenues, there are typically 20 address numbers between consecutive east–west streets (10 on either side of the avenue). A standard land lot on each avenue was originally 20 feet (6.1 m) wide, and there is about 200 feet (61 m) between each pair of east–west streets, for ten land lots between each pair of streets. The exceptions are Riverside Drive, as well as Fifth Avenue and Central Park West between 59th and 110th streets, which use a divisor of 10. These avenues all have buildings only on one side of the street, with a park on the other side. The "tricky number" often corresponds to a street near the southern end of the avenue. There are some notable exceptions: York Avenue address numbers are continuations of Avenue A address numbers, since the avenue was originally called Avenue A. East End Avenue address numbers are continuations of Avenue B address numbers, since the avenue was originally called Avenue B. Sixth Avenue and Broadway start south of Houston Street, the southern boundary of the Manhattan street numbering system. Although Park Avenue's southern terminus is at 32nd Street, a homeowner at 34th Street wanted the address "1 Park Avenue" (this was later changed). === Examples === For example, if you are at 62 Avenue B, 62 ÷ 20 ≈ 3 {\displaystyle 62\div 20\approx 3} , then add the "tricky number" 3 {\displaystyle 3} to give 6 {\displaystyle 6} . The nearest cross street to 62 Avenue B is East 6th Street. If you are at 78 Riverside Drive, 78 ÷ 10 ≈ 8 {\displaystyle 78\div 10\approx 8} , then add the "tricky number" 72 {\displaystyle 72} to give 80 {\displaystyle 80} . The nearest cross street to 78 Riverside Drive is West 80th Street. If you are at 501 5th Avenue, 501 ÷ 20 ≈ 25 {\displaystyle 501\div 20\approx 25} , then add the "tricky number" 18 {\displaystyle 18} to give 43 {\displaystyle 43} . The nearest cross street to 501 5th Avenue is actually 42nd Street, not 43rd Street, as the Manhattan address algorithm only gives approximate answers.

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  Local Economic Assessment Package

  The Local Economic Assessment Package (also known as “EDR-LEAP” or “LEAP Model”) is a web-based, interactive database and software tool used by local and regional agencies in the US to improve strategies for economic development. It provides local economic performance measures, and benchmarks for comparison of economic development factors against competing regions. It works by incorporating elements of economic base analysis as well as gap analysis and business cluster analysis to identify needs for improvement and paths for economic growth. The LEAP Model was originally developed for the Appalachian Regional Commission. Its theory and applications are discussed in peer-reviewed journal articles.

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  Technical data management system

  A technical data management system (TDMS) is a document management system (DMS) pertaining to the management of technical and engineering drawings and documents. Often the data are contained in 'records' of various forms, such as on paper, microfilms or digital media. Hence technical data management is also concerned with record management involving technical data. Technical document management systems are used within large organisations with large scale projects involving engineering. For example, a TDMS can be used for integrated steel plants (ISP), automobile factories, aero-space facilities, infrastructure companies, city corporations, research organisations, etc. In such organisations, technical archives or technical documentation centres are created as central facilities for effective management of technical data and records. TDMS functions are similar to that of conventional archive functions in concepts, except that the archived materials in this case are essentially engineering drawings, survey maps, technical specifications, plant and equipment data sheets, feasibility reports, project reports, operation and maintenance manuals, standards, etc. Document registration, indexing, repository management, reprography, etc. are parts of TDMS. Various kinds of sophisticated technologies such as document scanners, microfilming and digitization camera units, wide format printers, digital plotters, software, etc. are available, making TDMS functions an easier process than previous times. == Constituents of a technical data management system == Technical data refers to both scientific and technical information recorded and presented in any form or manner (excluding financial and management information). A Technical Data Management System is created within an organisation for archiving and sharing information such as technical specifications, datasheets and drawings. Similar to other types of data management system, a Technical Data Management System consists of the 4 crucial constituents mentioned below. === Data planning === Data plans (long-term or short-term) are constructed as the first essential step of a proper and complete TDMS. It is created to ultimately help with the 3 other constituents, data acquisition, data management and data sharing. A proper data plan should not exceed 2 pages and should address the following basics: Types of data (samples, experiment results, reports, drawings, etc.) and metadata (data that summarizes and describes other data. In this case, it refers to details such as sample sizes, experiment conditions and procedures, dates of reports, explanations of drawings, etc.) Means of researches and collections of data (field works, experiments in production lines, etc.) Costs of researches Policies for access, sharing (re-use within the organisation and re-distribution to the public) Proposals for archiving data and maintaining access to it === Data acquisition === Raw data is collected from primary sites of the organisations through the use of modern technologies. Please reference the table below for examples. The data collected is then transferred to technical data centres for data management. === Data management === After data acquisition, data is sorted out, whilst useful data is archived, unwanted data is disposed. When managing and archiving data, the features below of the data are considered. Names, labels, values and descriptions for variables and records. (In the case of TDMS, one example is names of equipments on an equipment datasheet) Derived data from the original data, with code, algorithm or command file used to create them. (In the case of TDMS, one example is an expectation report derived from the analysis of an equipment datasheet) Metadata associates with the data being archived === Data sharing === Archived and managed data are accessible to rightful entities. A proper and complete TDMS should share data to a suitable extent, under suitable security, in order to achieve optimal usage of data within the organisation. It aims for easy access when reused by other researchers and hence it enhances other research processes. Data is often referred in other tests and technical specifications, where new analysis is generated, managed and archived again. As a result, data is flowing within the organisation under effective management through the use of TDMS. == Advantages and disadvantages of usage of technical data management systems == There are strengths and weakness when using technical data management systems (TDMS) to archive data. Some of the advantages and disadvantages are listed below. === Advantages === ==== 1. Faster and easier data management ==== Since TDMS is integrated into the organisation's systems, whenever workers develop data files (SolidWorks, AutoCAD, Microsoft Word, etc.), they can also archive and manage data, linking what they need to their current work, at the same time they can also update the archives with useful data. This speeds up working processes and makes them more efficient. ==== 2. Increased security ==== All data files are centralized, hence internal and external data leakages are less likely to happen, and the data flow is more closely monitored. As a result, data in the organisation is more secured. ==== 3. Increased collaboration within the organisation ==== Since the data files are centralized and the data flow within the organisation increases, researchers and workers within the organisation are able to work on joint projects. More complex tasks can be performed for higher yields. ==== 4. Compatible to various formats of data ==== TDMS is compatible to many formats of data, from basic data like Microsoft Words to complex data like voice data. This enhances the quality of the management of data archived. === Disadvantages === ==== 1. Higher financial costs ==== Implementing TDMS into the organisation's systems involves monetary costs. Maintenance costs certain amount of human resources and money as well. These resources involve opportunity costs as they can be utilized in other aspects. ==== 2. Lower stability ==== Since TDMS manages and centralizes all the data the organisation processes, it links the working processes within the whole organisation together. It also increases the vulnerability of the organisation data network. If TDMS is not stable enough or when it is exposed to hacker and virus attacks, the organisation's data flow might shut down completely, affecting the work in an organisation-wide scale and leading to a lower stability as results. == Comparison between traditional data management approaches and technical data management systems == Test engineers and researchers are facing great challenges in turning complex test results and simulation data into usable information for higher yields of firms. These challenges are listed below. Increase in complication of designs Reduced in time and budgets available Higher quality is demanded === Traditional data management approaches === Many organisations are still applying the conventional file management systems, due to the difficulty in building a proper and complete archives for data management. The first approach is the simple file-folder system. This costs the problem of ineffectiveness as workers and researchers have to manually go through numerous layers of systems and files for the target data. Moreover, the target data may contain files with different formats and these files may not be stored in the same machine. These files are also easily lost if renamed or moved to another location. The second approach is conventional databases such as Oracle. These databases are capable of enabling easy search and access of data. However, a great drawback is that huge effort for preparing and modeling the data is required. For large-scale projects, huge monetary costs are induced, and extra IT human resources must be employed for constant handling, expanding and maintaining the inflexible system, which is custom for specific tasks, instead of all tasks. In the long-term, it is not cost-effective. === Technical data management systems (TDMS) === TDMS is developed based on 3 principles, flexible and organized file storage, self-scaling hybrid data index, and an interactive post-processing environment. The system in practical, mainly consists of 3 components, data files with essential and relevant Metadata, data finders for organizing and managing data regardless of files formats, and, a software of searching, analyzing and reporting. With metadata attached to original data files, the data finder can identify different related data files during searches, even if they are in different file formats. TDMS hence allows researchers to search for data like browsing the Internet. Last but not least, it can adapt to changes and update itself according to the changes, unlike databases. == Comparison between strong information systems and weak information systems == Complex organizations may need large amounts

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  Metadata

  Metadata (or metainformation) is data (or information) that defines and describes the characteristics of other data. It often helps to describe, explain, locate, or otherwise make data easier to retrieve, use, or manage. For example, the title, author, and publication date of a book are metadata about the book. But, while a data asset is finite, its metadata is infinite. As such, efforts to define, classify types, or structure metadata are expressed as examples in the context of its use. The term "metadata" has a history dating to the 1960s where it occurred in computer science and in popular culture. Different types of metadata serve different functions. For example, descriptive metadata for a document might include the author, creation date, file size and keywords. Metadata has various purposes. It can help users find relevant information and discover resources. It can also help organize electronic resources, provide digital identification, and archive and preserve resources. Metadata allows users to access resources by "allowing resources to be found by relevant criteria, identifying resources, bringing similar resources together, distinguishing dissimilar resources, and giving location information". Metadata of telecommunication activities including Internet traffic is very widely collected by various national governmental organizations. This data is used for the purposes of traffic analysis and can be used for mass surveillance. Unique metadata standards exist for different disciplines (e.g., museum collections, digital audio files, websites, etc.). Describing the contents and context of data or data files increases its usefulness. For example, a web page may include metadata specifying what software language the page is written in (e.g., HTML), what tools were used to create it, what subjects the page is about, and where to find more information about the subject. This metadata can automatically improve the reader's experience and make it easier for users to find the web page online. A CD may include metadata providing information about the musicians, singers, and songwriters whose work appears on the disc. In many countries, government organizations routinely store metadata about emails, telephone calls, web pages, video traffic, IP connections, and cell phone locations. == Types == There are many distinct types of metadata, including: Descriptive metadata – the descriptive information about a resource. It is used for discovery and identification. It includes elements such as title, abstract, author, and keywords. Structural metadata – metadata about containers of data and indicates how compound objects are put together, for example, how pages are ordered to form chapters. It describes the types, versions, relationships, and other characteristics of digital materials. Administrative metadata – the information to help manage a resource, like resource type, and permissions, and when and how it was created. Reference metadata – the information about the contents and quality of statistical data. Statistical metadata – also called process data, may describe processes that collect, process, or produce statistical data. Legal metadata – provides information about the creator, copyright holder, and public licensing, if provided. Metadata is not strictly bound to one of these categories, as it can describe a piece of data in many other ways. While the metadata application is manifold, covering a large variety of fields, there are specialized and well-accepted models to specify types of metadata. Bretherton & Singley (1994) distinguish between two distinct classes: structural/control metadata and guide metadata. Structural metadata describes the structure of database objects such as tables, columns, keys and indexes. Guide metadata helps humans find specific items and is usually expressed as a set of keywords in a natural language. According to Ralph Kimball, metadata can be divided into three categories: technical metadata (or internal metadata), business metadata (or external metadata), and process metadata. Dan Linstedt, creator of the data vault methodology, says business metadata "...provide[s] definition of the functionality, definition of the data, definition of the elements, and definition of how the data is used within business...business metadata includes business requirements, time-lines, business metrics, business process flows, and business terminology." Business metadata is important because it can greatly facilitate the usefulness of the data to business people. A simple example of business metadata is a glossary entry. Hover functionality in an application or web form can enable a glossary definition to be shown when cursor is on a field or term. Other examples of business metadata include annotation ability within applications. For example, a business user may be viewing a business intelligence (BI) report and notice a trend in the data. The user may have background knowledge as to why this trend occurs. Some business intelligence tools enable the user to create an annotation within the report that explains the trend. Such an annotation can enhance other users' understanding of the data. This example is especially powerful because it is created by a business user for the use of other business people. NISO distinguishes three types of metadata: descriptive, structural, and administrative. Descriptive metadata is typically used for discovery and identification, as information to search and locate an object, such as title, authors, subjects, keywords, and publisher. Structural metadata describes how the components of an object are organized. An example of structural metadata would be how pages are ordered to form chapters of a book. Finally, administrative metadata gives information to help manage the source. Administrative metadata refers to the technical information, such as file type, or when and how the file was created. Two sub-types of administrative metadata are rights management metadata and preservation metadata. Rights management metadata explains intellectual property rights, while preservation metadata contains information to preserve and save a resource. Statistical data repositories have their own requirements for metadata in order to describe not only the source and quality of the data but also what statistical processes were used to create the data, which is of particular importance to the statistical community in order to both validate and improve the process of statistical data production. An additional type of metadata beginning to be more developed is accessibility metadata. Accessibility metadata is not a new concept to libraries; however, advances in universal design have raised its profile. Projects like Cloud4All and GPII identified the lack of common terminologies and models to describe the needs and preferences of users and information that fits those needs as a major gap in providing universal access solutions. Those types of information are accessibility metadata. The Schema.org website has incorporated several accessibility properties based on IMS Global Access for All Information Model Data Element Specification. While the efforts to describe and standardize the varied accessibility needs of information seekers are beginning to become more robust, their adoption into established metadata schemas has not been as developed. For example, while Dublin Core (DC)'s "audience" and MARC 21's "reading level" could be used to identify resources suitable for users with dyslexia and DC's "format" could be used to identify resources available in braille, audio, or large print formats, there is more work to be done. == History == Metadata was traditionally used in the card catalogs of libraries until the 1980s when libraries converted their catalog data to digital databases. In the 2000s, as data and information were increasingly stored digitally, this digital data was described using metadata standards. An early description of "meta data" for computer systems was written by David Griffel and Stuart McIntosh at the MIT Center for International Studies in 1967: "In summary then, we have statements in an object language about subject descriptions of data and token codes for the data. We also have statements in a meta language describing the data relationships and transformations, and ought/is relations between norm and data." == Definition == Metadata means "data about data". Metadata is defined as the data providing information about one or more aspects of the data; it is used to summarize basic information about data that can make tracking and working with specific data easier. Some examples include: Means of creation of the data Source of the data Time and date of creation Creator or author of the data Location on a computer network where the data was created Standards used Data quality For example, a digital image may include metadata that describes the size of the image, its color depth, resolution,

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