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Topic Review
Dead Space (Video Game)
Dead Space is a 2008 survival horror video game developed by EA Redwood Shores and published by Electronic Arts for the PlayStation 3, Xbox 360 and Microsoft Windows. Released in October 2008, the title was the first in the Dead Space franchise and drew inspiration from other works of horror, notably Resident Evil 4 and the Silent Hill series. Set on a mining spaceship overrun by monsters called Necromorphs, the player controls engineer Isaac Clarke from the third-person perspective as he navigates the spaceship, fights Necromorphs, and struggles with increasing psychosis. The player explores areas on the station through the narrative, solving environmental puzzles while finding ammunition and equipment to survive. Isaac wields engineering equipment as weapons. Dead Space was pitched in early 2006 and fit Electronic Arts' priority of creating new intellectual properties. A prototype of the game was developed for the original Xbox console and lasted 18 months. The team aimed for realism and innovation in design. In the pursuit of these goals, the team removed set spawn points for enemies and omitted the heads-up display in favor of presenting information in the world. Immersive and frightening sound design was a large priority during production, and the score by Jason Graves was noted by critics cause of evoke tension and unease. Dead Space debuted weak in sales but eventually sold over a million copies worldwide. The game was met with universal acclaim: reviewers praised its atmosphere, gameplay and sound design. It won and was nominated for multiple industry awards and has been ranked by journalists as one of the greatest video games ever made. To following of the game success, spawned an adaptation for mobile phones, two numbered sequels (released in 2011 and 2013), several spin-off titles, and other related media, including a comic book prequel and an animated film. A remake of Dead Space is currently in development by EA's Motive Studios that set for release in early 2023.
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  • 14 Oct 2022
Topic Review
Shadow of the Beast
Shadow of the Beast is a platform game developed by Reflections and published by Psygnosis in 1989. The original version was released for the Amiga, and was later ported to many other systems. The game was known for its graphics, with many colours on screen and up to twelve levels of parallax scrolling backdrops, and for its atmospheric score composed by David Whittaker that used high-quality instrument samples. It was followed by two sequels, Shadow of the Beast II in 1990 and Shadow of the Beast III in 1993. A remake was released for the PlayStation 4 in May 2016, and included the Amiga original.
  • 595
  • 14 Oct 2022
Topic Review
Scunthorpe Problem
The Scunthorpe problem is the unintentional blocking of websites, e-mails, forum posts or search results by a spam filter or search engine because their text contains a string (or substring) of letters that appear to have an obscene or otherwise unacceptable meaning. Names, abbreviations, and technical terms are most often cited as being affected by the issue. The problem arises since computers can easily identify strings of text within a document, but interpreting words of this kind requires considerable ability to interpret a wide range of contexts, possibly across many cultures, which is an extremely difficult task. As a result, broad blocking rules may result in false positives affecting innocent phrases.
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  • 14 Oct 2022
Topic Review
Domain Name Speculation
Domain name speculation is the practice of identifying and registering or acquiring Internet domain names as an investment with the intent of selling them later for a profit. The main targets of domain name speculation are generic words which can be valuable for type-in traffic and for the dominant position they would have in any field due to their descriptive nature. Hence generic words and phrases such as poker, insurance, travel, creditcards, loan and others are attractive targets of domain speculation in any top-level domain. The speculative characteristics of domain names may be linked to news reports or current events. However, the effective period during which such opportunities exist may be limited. Quick turnaround in the resale of domains is often called domain flipping. Domain flipping may also involve the process of buying a valuable domain name and building a related website around it, all this with the objective of selling the domain and newly built website to an interested party.
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  • 14 Oct 2022
Topic Review
Turing Reduction
In computability theory, a Turing reduction from a problem A to a problem B, is a reduction which solves A, assuming the solution to B is already known (Rogers 1967, Soare 1987). It can be understood as an algorithm that could be used to solve A if it had available to it a subroutine for solving B. More formally, a Turing reduction is a function computable by an oracle machine with an oracle for B. Turing reductions can be applied to both decision problems and function problems. If a Turing reduction of A to B exists then every algorithm for B can be used to produce an algorithm for A, by inserting the algorithm for B at each place where the oracle machine computing A queries the oracle for B. However, because the oracle machine may query the oracle a large number of times, the resulting algorithm may require more time asymptotically than either the algorithm for B or the oracle machine computing A, and may require as much space as both together. The first formal definition of relative computability, then called relative reducibility, was given by Alan Turing in 1939 in terms of oracle machines. Later in 1943 and 1952 Stephen Kleene defined an equivalent concept in terms of recursive functions. In 1944 Emil Post used the term "Turing reducibility" to refer to the concept. A polynomial-time Turing reduction is known as a Cook reduction, after Stephen Cook.
  • 4.5K
  • 14 Oct 2022
Topic Review
Support Vector Machine
In machine learning, support-vector machines (SVMs, also support-vector networks) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis. The Support Vector Machine (SVM) algorithm is a popular machine learning tool that offers solutions for both classification and regression problems. Developed at AT&T Bell Laboratories by Vapnik with colleagues (Boser et al., 1992, Guyon et al., 1993, Vapnik et al., 1997), it presents one of the most robust prediction methods, based on the statistical learning framework or VC theory proposed by Vapnik and Chervonekis (1974) and Vapnik (1982, 1995). Given a set of training examples, each marked as belonging to one or the other of two categories, an SVM training algorithm builds a model that assigns new examples to one category or the other, making it a non-probabilistic binary linear classifier (although methods such as Platt scaling exist to use SVM in a probabilistic classification setting). An SVM model is a representation of the examples as points in space, mapped so that the examples of the separate categories are divided by a clear gap that is as wide as possible. New examples are then mapped into that same space and predicted to belong to a category based on the side of the gap on which they fall. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces. When data are unlabelled, supervised learning is not possible, and an unsupervised learning approach is required, which attempts to find natural clustering of the data to groups, and then map new data to these formed groups. The support-vector clustering algorithm, created by Hava Siegelmann and Vladimir Vapnik, applies the statistics of support vectors, developed in the support vector machines algorithm, to categorize unlabeled data, and is one of the most widely used clustering algorithms in industrial applications.
  • 4.5K
  • 14 Oct 2022
Topic Review
Up Series
Lua error in package.lua at line 80: module 'Module:Television infoboxes disambiguation check' not found. The Up Series is a series of documentary films produced by Granada Television for ITV that have followed the lives of fourteen British children since 1964, when they were seven years old. So far the documentary has had eight episodes spanning 49 years (one episode every seven years) all of which were broadcast on ITV, apart from the 6th episode which was broadcast on BBC One. In a 2005 Channel 4 programme, the series topped the list of The 50 Greatest Documentaries. The children were selected to represent the range of socio-economic backgrounds in Britain at that time, with the explicit assumption that each child's social class predetermines their future. Every seven years, the director, Michael Apted, films material from those of the fourteen who choose to participate. The last installment, 56 Up, premiered in May 2012; Apted has stated that filming for 63 Up will occur in late 2018, for release in spring 2019. Apted has also been reported as saying: "I hope to do 84 Up when I'll be 99." The aim of the series is stated at the beginning of 7 Up as: "Why do we bring these children together? Because we want to get a glimpse of England in the year 2000. The shop steward and the executive of the year 2000 are now seven years old."
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  • 14 Oct 2022
Topic Review
Java Persistence API
The Java Persistence API (JPA), in 2019 renamed to Jakarta Persistence, is a Java application programming interface specification that describes the management of relational data in applications using Java Platform, Standard Edition and Java Platform, Enterprise Edition/Jakarta EE. Persistence in this context covers three areas: The reference implementation for JPA is EclipseLink.
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  • 14 Oct 2022
Topic Review
CHICKEN (Scheme Implementation)
CHICKEN is a compiler and interpreter for the Scheme programming language that compiles Scheme code to standard C. It is mostly R5RS compliant and offers many extensions to the standard. The newer R7RS standard is supported through an extension library. CHICKEN is free software available under the BSD license. It is implemented mostly in Scheme, with some parts in C for performance or to make embedding into C programs easier.
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Topic Review
History of the Portable Document Format (PDF)
The Portable Document Format was created in the early 1990s by Adobe Systems, introduced at the Windows and OS|2 Conference in January 1993 and remained a proprietary format until it was released as an open standard in 2008. Since then, it is under control of International Organization for Standardization Committee of volunteer industry experts. PDF was developed to share documents, including text formatting and inline images, among computer users of disparate platforms who may not have access to mutually-compatible application software.It was created by a research and developement team called Camelot, led by Adobe's co-founder John Warnock. PDF was one among a number of competing formats such as DjVu, Envoy, Common Ground Digital Paper, Farallon Replica and even Adobe's own PostScript format. In those early years before the rise of the World Wide Web and HTML documents, PDF was popular mainly in desktop publishing workflows. PDF's adoption in the early days of the format's history was slow. Adobe Acrobat, Adobe's suite for reading and creating PDF files, was not freely available; early versions of PDF had no support for external hyperlinks, reducing its usefulness on the Internet; the larger size of a PDF document compared to plain text required longer download times over the slower modems common at the time; and rendering PDF files was slow on the less powerful machines of the day. Adobe distributed its Adobe Reader (now Acrobat Reader) program free of charge from version 2.0 onwards, and continued supporting the original PDF, which eventually became the de facto standard for fixed-format electronic documents. In 2008 Adobe Systems' PDF Reference 1.7 became ISO 32000:1:2008. Thereafter, further development of PDF (including PDF 2.0) is conducted by ISO's TC 171 SC 2 WG 8 with the participation of Adobe Systems and other subject matter experts.
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  • 14 Oct 2022
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