In computer programming, a control break is a change in the value of one of the keys on which a file is sorted, which requires some extra processing. For example, with an input file sorted by post code, the number of items found in each postal district might need to be printed on a report, and a heading shown for the next district. Quite often there is a hierarchy of nested control breaks in a program, such as streets within districts within areas, with the need for a grand total at the end. Structured programming techniques have been developed to ensure correct processing of control breaks in languages such as COBOL and to ensure that conditions such as empty input files and sequence errors are handled properly. With fourth-generation languages such as SQL, the programming language should handle most of the details of control breaks automatically.
Frame grabber
A frame grabber is an electronic device that captures (i.e., "grabs") individual, digital still frames from an analog video signal or a digital video stream. It is usually employed as a component of a computer vision system, in which video frames are captured in digital form and then displayed, stored, transmitted, analyzed, or combinations of these. Historically, frame grabber expansion cards were the predominant way to interface cameras to PCs. Other interface methods have emerged since then, with frame grabbers (and in some cases, cameras with built-in frame grabbers) connecting to computers via interfaces such as USB, Ethernet and IEEE 1394 ("FireWire"). Early frame grabbers typically had only enough memory to store a single digitized video frame, whereas many modern frame grabbers can store multiple frames. Modern frame grabbers often are able to perform functions beyond capturing a single video input. For example, some devices capture audio in addition to video, and some devices provide, and concurrently capture frames from multiple video inputs. Other operations may be performed as well, such as deinterlacing, text or graphics overlay, image transformations (e.g., resizing, rotation, mirroring), and conversion to JPEG or other compressed image formats. To satisfy the technological demands of applications such as radar acquisition, manufacturing and remote guidance, some frame grabbers can capture images at high frame rates, high resolutions, or both. == Circuitry == Analog frame grabbers, which accept and process analog video signals, include these circuits: Input signal conditioner that buffers the analog video input signal to protect downstream circuitry Video decoder that converts SD analog video (e.g., NTSC, SECAM, PAL) or HD analog video (e.g., AHD, HD-TVI, HD-CVI) to a digital format Digital frame grabbers, which accept and process digital video streams, include these circuits: Digital video decoder that interfaces to and converts a specific type of digital video source, such as Camera Link, CoaXPress, DVI, GigE Vision, LVDS, or SDI Circuitry common to both analog and digital frame grabbers: Memory for storing the acquired image (i.e., a frame buffer) A bus interface through which a processor can control the acquisition and access the data General purpose I/O for triggering image acquisition or controlling external equipment == Applications == === Healthcare === Frame grabbers are used in medicine for many applications, including telenursing and remote guidance. In situations where an expert at another location needs to be consulted, frame grabbers capture the image or video from the appropriate medical equipment, so it can be sent digitally to the distant expert. === Manufacturing === "Pick and place" machines are often used to mount electronic components on circuit boards during the circuit board assembly process. Such machines use one or more cameras to monitor the robotics that places the components. Each camera is paired with a frame grabber that digitizes the analog video, thus converting the video to a form that can be processed by the machine software. === Network security === Frame grabbers may be used in security applications. For example, when a potential breach of security is detected, a frame grabber captures an image or a sequence of images, and then the images are transmitted across a digital network where they are recorded and viewed by security personnel. === Personal use === In recent years with the rise of personal video recorders like camcorders, mobile phones, etc. video and photo applications have gained ascending prominence. Frame grabbing is becoming very popular on these devices. === Astronomy & astrophotography === Amateur astronomers and astrophotographers use frame grabbers when using analog "low light" cameras for live image display and internet video broadcasting of celestial objects. Frame grabbers are essential to connect the analog cameras used in this application to the computers that store or process the images.
How to Solve it by Computer
How to Solve it by Computer is a computer science book by R. G. Dromey, first published by Prentice-Hall in 1982. It is occasionally used as a textbook, especially in India. It is an introduction to the whys of algorithms and data structures. Features of the book: The design factors associated with problems, The creative process behind coming up with innovative solutions for algorithms and data structures, The line of reasoning behind the constraints, factors and the design choices made. The very fundamental algorithms portrayed by this book are mostly presented in pseudocode and/or Pascal notation.
Library and information scientist
A library and information scientist, also known as a library scholar, is a researcher or academic who specializes in the field of library and information science and often participates in scholarly writing about and related to library and information science. A library and information scientist is neither limited to any one subfield of library and information science nor any one particular type of library. These scientists come from all information-related sectors including library and book history. == University of Chicago Graduate Library School == The University of Chicago Graduate Library School was established in 1928 to grant a graduate degree in librarianship with an emphasis on research. The program expanded the concept of librarianship, focused on scientific inquiry and established it as a domain for scientific study. In The Spirit of Inquiry: The Graduate Library School at Chicago, 1921-51 Richardson reviewed the history of the School and its impact on the discipline. == Bibliometric mappings == Bibliometric methods have been used to create maps of library and information science, thus identifying the most important researchers as well as their relative connections (or distances) and identifying emerging trends related to LIS publications within the field. White and McCain (1998) made a map of information science and Åström (2002), Chen, Ibekwe-SanJuan, and Hou (2010), Janssens, Leta, Glanzel, and De Moor (2006), and Zhao and Strotmann (2008) constructed some later maps of library and information science. Jabeen, Yun, Rafiq, and Jabeen (2015) mapped the growth and trends of LIS publications. == Notable library and information scientists == See also Beta Phi Mu Award, Award of Merit - Association for Information Science and Technology, Justin Winsor Prize (library)
Knuth–Plass line-breaking algorithm
The Knuth–Plass algorithm is a line-breaking algorithm designed for use in Donald Knuth's typesetting program TeX. It integrates the problems of text justification and hyphenation into a single algorithm by using a discrete dynamic programming method to minimize a loss function that attempts to quantify the aesthetic qualities desired in the finished output. The algorithm works by dividing the text into a stream of three kinds of objects: boxes, which are non-resizable chunks of content, glue, which are flexible, resizeable elements, and penalties, which represent places where breaking is undesirable (or, if negative, desirable). The loss function, known as "badness", is defined in terms of the deformation of the glue elements, and any extra penalties incurred through line breaking. Making hyphenation decisions follows naturally from the algorithm, but the choice of possible hyphenation points within words, and optionally their preference weighting, must be performed first, and that information inserted into the text stream in advance. Knuth and Plass' original algorithm does not include page breaking, but may be modified to interface with a pagination algorithm, such as the algorithm designed by Plass in his PhD thesis. Typically, the cost function for this technique should be modified so that it does not count the space left on the final line of a paragraph; this modification allows a paragraph to end in the middle of a line without penalty. The same technique can also be extended to take into account other factors such as the number of lines or costs for hyphenating long words. == Computational complexity == A naive brute-force exhaustive search for the minimum badness by trying every possible combination of breakpoints would take an impractical O ( 2 n ) {\displaystyle O(2^{n})} time. The classic Knuth-Plass dynamic programming approach to solving the minimization problem is a worst-case O ( n 2 ) {\displaystyle O(n^{2})} algorithm but usually runs much faster, in close to linear time. Solving for the Knuth-Plass optimum can be shown to be a special case of the convex least-weight subsequence problem, which can be solved in O ( n ) {\displaystyle O(n)} time. Methods to do this include the SMAWK algorithm. == Simple example of minimum raggedness metric == For the input text AAA BB CC DDDDD with line width 6, a greedy algorithm that puts as many words on a line as possible while preserving order before moving to the next line, would produce: ------ Line width: 6 AAA BB Remaining space: 0 CC Remaining space: 4 DDDDD Remaining space: 1 The sum of squared space left over by this method is 0 2 + 4 2 + 1 2 = 17 {\displaystyle 0^{2}+4^{2}+1^{2}=17} . However, the optimal solution achieves the smaller sum 3 2 + 1 2 + 1 2 = 11 {\displaystyle 3^{2}+1^{2}+1^{2}=11} : ------ Line width: 6 AAA Remaining space: 3 BB CC Remaining space: 1 DDDDD Remaining space: 1 The difference here is that the first line is broken before BB instead of after it, yielding a better right margin and a lower cost 11.
AI literacy
AI literacy or artificial intelligence literacy is "a set of competencies that enables individuals to critically evaluate AI technologies; communicate and collaborate effectively with AI; and use AI as a tool online, at home, and in the workplace." AI is employed in a variety of applications, including self-driving automobiles, virtual assistants and text generation by generative AI models. Users of these tools should be able to make informed decisions. AI literacy may have an impact on students' future employment prospects. With the rise of generative AI platforms, AI literacy has become a topic of conversation in the field of education. Some think AI literacy is essential for school and college students, while others restrict or prohibit the use of AI in assignments, viewing it as a form of academic dishonesty. However, many researchers and educational institutions promote a more nuanced approach, encouraging critical engagement with AI while developing policies that balance academic integrity with opportunities for learning. == Definitions == Other definitions of AI literacy include the ability to understand, use, monitor, and critically reflect on AI applications. That use of the term usually refers to teaching skills and knowledge to the general public, particularly those who are not adept in AI and the ability to understand, use, evaluate, and ethically navigate AI. As research into AI literacy is still emerging and focused on developing context-specific skills, there is not yet a single, broadly agreed-upon definition. AI literacy is linked to other forms of literacy. AI literacy requires digital literacy, whereas scientific and computational literacy may inform it. Data literacy also significantly overlaps with it. == Categories == AI literacy encompasses multiple categories, including a theoretical understanding of how artificial intelligence works, the usage of artificial intelligence technologies, and the critical appraisal of artificial intelligence, and its ethics. === Know and understand AI === Knowledge and understanding of AI refers to a basic understanding of what artificial intelligence is and how it works. This includes familiarity with machine learning algorithms and the limitations and biases present in AI systems. Users who know and understand AI should be familiar with various technologies that use artificial intelligence, including cognitive systems, robotics and machine learning. This includes recognizing that large language models (LLMs) are machine learning models trained on extensive datasets which generate new text rather than retrieving pre-written responses. === Use and apply AI === Using and applying AI refers to the ability to use AI tools to solve problems and perform tasks such as programming and analyzing big data. Some consider prompt engineering, the practice of designing effective prompts to guide generative AI platforms more effectively, as another competency within AI literacy. === Evaluate and create AI === Evaluation and creation refers to the ability to critically evaluate the quality and reliability of AI systems. It also refers to designing and building fair and ethical AI systems. To evaluate correctly, users should also learn in which areas AI is strong, and in which areas it is weak. === AI ethics === AI ethics refers to understanding the moral implications of AI, and the making informed decisions regarding the use of AI tools. This area includes considerations such as: Accountability: Hold AI actors accountable for the operation of AI systems and adherence to ethical ideals. Accuracy: Identify and report sources of error and uncertainty in algorithms and data. Auditability: Enable other parties to audit and assess algorithm behavior via transparent information sharing. Explainability: Make sure that algorithmic judgments and the underlying data can be presented in simple language. Fairness: Prevent biases and consider varied viewpoints. To do so, increase the diversity of researchers in the field. Human Centricity and Well-being: Prioritize human well-being in AI development and deployment. Human rights Alignment: Ensure that technology do not infringe internationally recognized human rights. Inclusivity: Make AI accessible to everyone. Progress: Choose high value initiatives. Responsibility, accountability, and transparency: Foster trust via responsibility, accountability, and fairness. Robustness and Security: Make AI systems safe, secure, and resistant to manipulation or data breach. Sustainability: Choose implementations that generate long-term, useful benefits. Environmental Implications: How this tool impacts the environment, any restrictions or laws, if this impact is worth the effects or not. === Enabling AI === Support AI by developing associated knowledge and skills such as programming and statistics. == Promoting AI literacy == Several governments have recognized the need to promote AI literacy, including among adults. Such programs have been published in the United States, China, Germany and Finland. Programs intended for the general public usually consist of short and easy to understand online study units. Programs intended for children are usually project-based. Programs for students at colleges and universities often address the specific professional needs of the student, depending on their field of study. Beyond the education system, AI literacy can also be developed in the community, for example in museums. === Schools === Schools use diverse pedagogies to promote AI literacy. These include: Performing a Turing test with an intelligent agent Creating chatbots Building apps using Blockly-based programming Project-based learning Building robots Data visualization Training AI models Artificial intelligence curricula can improve students' understanding of topics such as machine learning, neural networks, and deep learning. === Higher education === Before the second decade of the 21st century, artificial intelligence was studied mainly in STEM courses. Later, projects emerged to increase artificial intelligence education, specifically to promote AI literacy. Most courses start with one or more study units that deal with basic questions such as what artificial intelligence is, where it comes from, what it can do and what it can't do. Most courses also refer to machine learning and deep learning. Some of the courses deal with moral issues in artificial intelligence. In Ireland, the Higher Education Authority published Generative AI in Higher Education Teaching & Learning: Policy Framework in December 2025, which encouraged higher education institutions to embed AI literacy across programmes as a core graduate attribute. ==== Disciplinary policy ==== As a response to the increase of generative AI use in education, several disciplines formed committees or task forces to examine context-specific approaches toward AI literacy. In spring 2025, the Modern Language Association and Conference on College Composition and Communication Joint Task Force finished development of three working papers, a guide on AI literacy for students, and a collection of resources addressing AI use in writing. The task force emphasized the need for "a culture of critical AI literacy" and included guidelines not only for students but also educators and institutions, highlighting the need for modeling ethical AI use in planning processes. Similarly, a committee formed by the American Historical Association Council published "Guiding Principles for Artificial Intelligence in History Education" which encouraged "clear and transparent engagement with generative AI." The guidelines demonstrate the value of criticality when working with generative AI in thinking and research.
Single-source publishing
Single-source publishing, also known as single-sourcing publishing, is a content management method which allows the same source content to be used across different forms of media and more than one time. The labor-intensive and expensive work of editing need only be carried out once, on only one document; that source document (the single source of truth) can then be stored in one place and reused. This reduces the potential for error, as corrections are only made one time in the source document. The benefits of single-source publishing primarily relate to the editor rather than the user. The user benefits from the consistency that single-sourcing brings to terminology and information. This assumes the content manager has applied an organized conceptualization to the underlying content (A poor conceptualization can make single-source publishing less useful). Single-source publishing is sometimes used synonymously with multi-channel publishing though whether or not the two terms are synonymous is a matter of discussion. == Definition == While there is a general definition of single-source publishing, there is no single official delineation between single-source publishing and multi-channel publishing, nor are there any official governing bodies to provide such a delineation. Single-source publishing is most often understood as the creation of one source document in an authoring tool and converting that document into different file formats or human languages (or both) multiple times with minimal effort. Multi-channel publishing can either be seen as synonymous with single-source publishing, or similar in that there is one source document but the process itself results in more than a mere reproduction of that source. == History == The origins of single-source publishing lie, indirectly, with the release of Windows 3.0 in 1990. With the eclipsing of MS-DOS by graphical user interfaces, help files went from being unreadable text along the bottom of the screen to hypertext systems such as WinHelp. On-screen help interfaces allowed software companies to cease the printing of large, expensive help manuals with their products, reducing costs for both producer and consumer. This system raised opportunities as well, and many developers fundamentally changed the way they thought about publishing. Writers of software documentation did not simply move from being writers of traditional bound books to writers of electronic publishing, but rather they became authors of central documents which could be reused multiple times across multiple formats. The first single-source publishing project was started in 1993 by Cornelia Hofmann at Schneider Electric in Seligenstadt, using software based on Interleaf to automatically create paper documentation in multiple languages based on a single original source file. XML, developed during the mid- to late-1990s, was also significant to the development of single-source publishing as a method. XML, a markup language, allows developers to separate their documentation into two layers: a shell-like layer based on presentation and a core-like layer based on the actual written content. This method allows developers to write the content only one time while switching it in and out of multiple different formats and delivery methods. In the mid-1990s, several firms began creating and using single-source content for technical documentation (Boeing Helicopter, Sikorsky Aviation and Pratt & Whitney Canada) and user manuals (Ford owners manuals) based on tagged SGML and XML content generated using the Arbortext Epic editor with add-on functions developed by a contractor. The concept behind this usage was that complex, hierarchical content that did not lend itself to discrete componentization could be used across a variety of requirements by tagging the differences within a single document using the capabilities built into SGML and XML. Ford, for example, was able to tag its single owner's manual files so that 12 model years could be generated via a resolution script running on the single completed file. Pratt & Whitney, likewise, was able to tag up to 20 subsets of its jet engine manuals in single-source files, calling out the desired version at publication time. World Book Encyclopedia also used the concept to tag its articles for American and British versions of English. Starting from the early 2000s, single-source publishing was used with an increasing frequency in the field of technical translation. It is still regarded as the most efficient method of publishing the same material in different languages. Once a printed manual was translated, for example, the online help for the software program which the manual accompanies could be automatically generated using the method. Metadata could be created for an entire manual and individual pages or files could then be translated from that metadata with only one step, removing the need to recreate information or even database structures. Although single-source publishing is now decades old, its importance has increased urgently as of the 2010s. As consumption of information products rises and the number of target audiences expands, so does the work of developers and content creators. Within the industry of software and its documentation, there is a perception that the choice is to embrace single-source publishing or render one's operations obsolete. == Criticism == Editors using single-source publishing have been criticized for below-standard work quality, leading some critics to describe single-source publishing as the "conveyor belt assembly" of content creation. While heavily used in technical translation, there are risks of error in regard to indexing. While two words might be synonyms in English, they may not be synonyms in another language. In a document produced via single-sourcing, the index will be translated automatically and the two words will be rendered as synonyms. This is because they are synonyms in the source language, while in the target language they are not.