🛡️Information Security: Distributed Denial of Service [DDoS] (Definitions)

"An electronic attack perpetrated by a person who controls legions of hijacked computers. On a single command, the computers simultaneously send packets of data across the Internet at a target computer. The attack is designed to overwhelm the target and stop it from functioning." (Andy Walker, "Absolute Beginner’s Guide To: Security, Spam, Spyware & Viruses", 2005)

"A type of DoS attack in which many (usually thousands or millions) of systems flood the victim with unwanted traffic. Typically perpetrated by networks of zombie Trojans that are woken up specifically for the attack." (Mark Rhodes-Ousley, "Information Security: The Complete Reference" 2nd Ed., 2013)

"A denial of service (DoS) attack that comes from multiple sources at the same time. Attackers often enlist computers into botnets after infecting them with malware. Once infected, the attacker can then direct the infected computers to attack other computers." (Darril Gibson, "Effective Help Desk Specialist Skills", 2014)

"A denial of service technique using numerous hosts to perform the attack. For example, in a network flooding attack, a large number of co-opted computers (e.g., a botnet) send a large volume of spurious network packets to disable a specified target system. See also denial of service; botnet." (O Sami Saydjari, "Engineering Trustworthy Systems: Get Cybersecurity Design Right the First Time", 2018)

"A DoS attack in which multiple systems are used to flood servers with traffic in an attempt to overwhelm available resources (transmission capacity, memory, processing power, and so on), making them unavailable to respond to legitimate users." (William Stallings, "Effective Cybersecurity: A Guide to Using Best Practices and Standards", 2018)

"DDoS stands for distributed denial of service. In this type of an attack, an attacker tends to overwhelm the targeted network in order to make the services unavailable to the intended or legitimate user." (Kirti R Bhatele et al, "The Role of Artificial Intelligence in Cyber Security", Countering Cyber Attacks and Preserving the Integrity and Availability of Critical Systems, 2019)

"In DDoS attack, the incoming network traffic affects a target (e.g., server) from many different compromised sources. Consequently, online services are unavailable due to the attack. The target's resources are affected with different malicious network-based techniques (e.g., flood of network traffic packets)." (Ana Gavrovska & Andreja Samčović, "Intelligent Automation Using Machine and Deep Learning in Cybersecurity of Industrial IoT", 2020)

"This refers to malicious attacks or threats on computer systems to disrupt or break computing activities so that their access and availability is denied to the consumers of such systems or activities." (Heru Susanto et al, "Data Security for Connected Governments and Organisations: Managing Automation and Artificial Intelligence", 2021)

"A denial of service technique that uses numerous hosts to perform the attack." (CNSSI 4009-2015)

"A distributed denial-of-service (DDoS) attack is a malicious attempt to disrupt normal traffic on a targeted server, service or network by overwhelming the target or its surrounding infrastructure with a flood of Internet traffic." (proofpoint) [source]

🛡️Information Security: Denial of Service [DoS] (Definitions)

"A type of attack on a computer system that ties up critical system resources, making the system temporarily unusable." (Tom Petrocelli, "Data Protection and Information Lifecycle Management", 2005)

"Any attack that affects the availability of a service. Reliability bugs that cause a service to crash or hang are usually potential denial-of-service problems." (Mark S Merkow & Lakshmikanth Raghavan, "Secure and Resilient Software Development", 2010)

"This is a technique for overloading an IT system with a malicious workload, effectively preventing its regular service use." (Martin Oberhofer et al, "The Art of Enterprise Information Architecture", 2010)

"Occurs when a server or Web site receives a flood of traffic - much more traffic or requests for service than it can handle, causing it to crash." (Linda Volonino & Efraim Turban, "Information Technology for Management 8th Ed", 2011)

"Causing an information resource to be partially or completely unable to process requests. This is usually accomplished by flooding the resource with more requests than it can handle, thereby rendering it incapable of providing normal levels of service." (Mark Rhodes-Ousley, "Information Security: The Complete Reference, Second Edition" 2nd Ed., 2013)

"Attacks designed to disable a resource such as a server, network, or any other service provided by the company. If the attack is successful, the resource is no longer available to legitimate users." (Darril Gibson, "Effective Help Desk Specialist Skills", 2014)

"An attack from a single attacker designed to disrupt or disable the services provided by an IT system. Compare to distributed denial of service (DDoS)." (Darril Gibson, "Effective Help Desk Specialist Skills", 2014)

"A coordinated attack in which the target website or service is flooded with requests for access, to the point that it is completely overwhelmed." (Faithe Wempen, "Computing Fundamentals: Introduction to Computers", 2015)

"An attack that can result in decreased availability of the targeted system." (Mike Harwood, "Internet Security: How to Defend Against Attackers on the Web" 2nd Ed., 2015)

"An attack that generally floods a network with traffic. A successful DoS attack renders the network unusable and effectively stops the victim organization’s ability to conduct business." (Weiss, "Auditing IT Infrastructures for Compliance" 2nd Ed., 2015)

"A type of cyberattack to degrade the availability of a target system." (O Sami Saydjari, "Engineering Trustworthy Systems: Get Cybersecurity Design Right the First Time", 2018)

"Any action, or series of actions, that prevents a system, or its resources, from functioning in accordance with its intended purpose." (Shon Harris & Fernando Maymi, "CISSP All-in-One Exam Guide" 8th Ed., 2018)

"The prevention of authorized access to resources or the delaying of time-critical operations." (William Stallings, "Effective Cybersecurity: A Guide to Using Best Practices and Standards", 2018)

"An attack shutting down running of a service or network in order to render it inaccessible to its users (whether human person or a processing device)." (Wissam Abbass et al, "Internet of Things Application for Intelligent Cities: Security Risk Assessment Challenges", 2021)

"Actions that prevent the NE from functioning in accordance with its intended purpose. A piece of equipment or entity may be rendered inoperable or forced to operate in a degraded state; operations that depend on timeliness may be delayed." (NIST SP 800-13)

"The prevention of authorized access to resources or the delaying of time-critical operations. (Time-critical may be milliseconds or it may be hours, depending upon the service provided)." (NIST SP 800-12 Rev. 1)

"The prevention of authorized access to a system resource or the delaying of system operations and functions." (NIST SP 800-82 Rev. 2)

🛡️Information Security: Cryptography (Definitions)

"Cryptography is the science of hiding information through ciphers and codes. Cryptographers devise new cryptographic algorithms." (Michael Coles & Rodney Landrum, , "Expert SQL Server 2008 Encryption", 2008)

"The process of converting data into an unreadable form via an encryption algorithm. Cryptography enables information to be sent across communication networks that are assumed to be insecure, without losing confidentiality or the integrity of the information being sent." (Alex Berson & Lawrence Dubov, "Master Data Management and Data Governance", 2010)

"The science (or art) of providing secrecy, integrity, and non-repudiation for data." (Mark S Merkow & Lakshmikanth Raghavan, "Secure and Resilient Software Development", 2010)

"The art or science of rendering plain information unintelligible, and for restoring encrypted information to intelligible form." (Manish Agrawal, "Information Security and IT Risk Management", 2014)

"Science of secret writing that enables an entity to store and transmit data in a form that is available only to the intended individuals." (Adam Gordon, "Official (ISC)2 Guide to the CISSP CBK" 4th Ed., 2015)

"The encoding of data so that it can be decoded only by certain persons. The role of cryptography is to protect data integrity, confidentiality, nonrepudiation, and authentication." (Mike Harwood, "Internet Security: How to Defend Against Attackers on the Web" 2nd Ed., 2015)

"The field of study related to encoded information" (Nell Dale & John Lewis, "Computer Science Illuminated" 6th Ed., 2015)

"The science of secret writing that enables storage and transmission of data in a form that is available only to the intended individuals." (Shon Harris & Fernando Maymi, "CISSP All-in-One Exam Guide" 8th Ed., 2018)

"The study of algorithmic transformations from plain text to encrypted forms in which the unencrypted data cannot be ascertained without possession of the encryption key." (O Sami Saydjari, "Engineering Trustworthy Systems: Get Cybersecurity Design Right the First Time", 2018)

"A technique to secure communication or data." (AICPA)

"Art or science concerning the principles, means, and methods for rendering plain information unintelligible and for restoring encrypted information to intelligible form."(CNSSI 4009-2015 NSA/CSS) 

"The art and science of using mathematics to secure information and create a high degree of trust in the electronic realm." (NISTIR 7316) 

"The discipline that embodies principles, means and methods for providing information security, including confidentiality, data integrity, non-repudiation, and authenticity." (NISTIR 8040)

"The discipline that embodies the principles, means, and methods for the transformation of data in order to hide their semantic content, prevent their unauthorized use, or prevent their undetected modification." (NIST SP 800-59)

💻IT: Virtualization (Definitions)

"Creation of a virtual, as opposed to a real, instance of an entity, such as an operating system, server, storage, or network." (David G Hill, "Data Protection: Governance, Risk Management, and Compliance", 2009)

"The process of partitioning a computer so that multiple operating system instances can run at the same time on a single physical computer." (John Goodson & Robert A Steward, "The Data Access Handbook", 2009)

"A concept that separates business applications and data from hardware resources, allowing companies to pool hardware resources, rather than dedicate servers to application and assign those resources to applications as needed." (Linda Volonino & Efraim Turban, "Information Technology for Management" 8th Ed, 2011)

"A technique that creates logical representations of computing resources that are independent of the underlying physical computing resources." (Carlos Coronel et al, "Database Systems: Design, Implementation, and Management" 9th Ed., 2011)

"A method for managing hardware assets used at the same time by different users or processes, or both, that makes the part assigned to each user or process appear to act as if it was running on a separate piece of equipment." (Kenneth A Shaw, "Integrated Management of Processes and Information", 2013)

"Virtual memory is the use of a disk to store active areas of memory to make the available memory appear larger. In a virtual environment, one computer runs software that allows it to emulate another machine. This kind of emulation is commonly known as virtualization." (Marcia Kaufman et al, "Big Data For Dummies", 2013)

"A technique common in computing, consisting in the creation of virtual (rather than actual) instance of any element, so it can be managed and used independently. Virtualization has been one of the key tools for resource sharing and software development, and now it is beginning to be applied to the network disciplines." (Diego R López & Pedro A. Aranda, "Network Functions Virtualization: Going beyond the Carrier Cloud", 2015)

"Creation of a simulated environment (hardware platform, operating system, storage, etc.) that allows for central control and scalability." (Adam Gordon, "Official (ISC)2 Guide to the CISSP CBK 4th Ed.", 2015)

"The creation of a virtual version of actual services, applications, or resources." (Mike Harwood, "Internet Security: How to Defend Against Attackers on the Web" 2nd Ed., 2015)

"The process of creating a virtual version of a resource, such as an operating system, hardware platform, or storage device." (Andrew Pham et al, "From Business Strategy to Information Technology Roadmap", 2016)

"A base component of the cloud that consists of software that emulates physical infrastructure." (Richard Ehrhardt, "Cloud Build Methodology", 2017)

"The process of presenting an abstraction of hardware resources to give the appearance of dedicated access and control to hardware resources, while, in reality, those resources are being shared." (O Sami Saydjari, "Engineering Trustworthy Systems: Get Cybersecurity Design Right the First Time", 2018)

💻IT: Search Engine Optimization [SEO] (Definitions)

"The set of techniques and methodologies devoted to improving organic search rankings (not paid search) for a Web site." (Mike Moran & Bill Hunt , "Search Engine Marketing, Inc", 2005)

"The process and strategy of presenting a business on the web to improve the ability of potential customers finding it through natural searches on search engines such as Google, Yahoo!, and Bing." (Gina Abudi & Brandon Toropov, "The Complete Idiot's Guide to Best Practices for Small Business", 2011)

"The process of improving the volume or quality of traffic to a Web site from search engines via unpaid search results." (Linda Volonino & Efraim Turban, "Information Technology for Management 8th Ed", 2011)

"techniques to help ensure that a web site appears as close to the first position on a web search results page as possible." (Bill Holtsnider & Brian D Jaffe, "IT Manager's Handbook" 3rd Ed., 2012)

"Search engine optimization, the set of techniques and methodologies devoted to improving organic search rankings (not paid search) for a Web site." (Mike Moran & Bill Hunt , "Search Engine Marketing, Inc", 2005)

"The process of writing web content so as to increase a page's ranking in online search results." (Faithe Wempen, "Computing Fundamentals: Introduction to Computers", 2015)

"its main function is to increase website visibility. The main search engines use algorithms to rank a website’s position and hence its overall position in the search results. In some instances it can be as simple as structuring the words on a website in a way the search engine operates. " (BCS Learning & Development Limited, "CEdMA Europe", 2019)

🔭Data Science: Problem Solving (Just the Quotes)

"Reflexion is careful and laborious thought, and watchful attention directed to the agreeable effect of one's plan. Invention, on the other hand, is the solving of intricate problems and the discovery of new principles by means of brilliancy and versatility." (Marcus Vitruvius Pollio, "De architectura" ["On Architecture], cca. 15BC)

"The insights gained and garnered by the mind in its wanderings among basic concepts are benefits that theory can provide. Theory cannot equip the mind with formulas for solving problems, nor can it mark the narrow path on which the sole solution is supposed to lie by planting a hedge of principles on either side. But it can give the mind insight into the great mass of phenomena and of their relationships, then leave it free to rise into the higher realms of action." (Carl von Clausewitz, "On War", 1832)

"The correct solution to any problem depends principally on a true understanding of what the problem is." (Arthur M Wellington, "The Economic Theory of Railway Location", 1887)

"He who seeks for methods without having a definite problem in mind seeks for the most part in vain." (David Hilbert, 1902)

"This diagrammatic method has, however, serious inconveniences as a method for solving logical problems. It does not show how the data are exhibited by cancelling certain constituents, nor does it show how to combine the remaining constituents so as to obtain the consequences sought. In short, it serves only to exhibit one single step in the argument, namely the equation of the problem; it dispenses neither with the previous steps, i.e., 'throwing of the problem into an equation' and the transformation of the premises, nor with the subsequent steps, i.e., the combinations that lead to the various consequences. Hence it is of very little use, inasmuch as the constituents can be represented by algebraic symbols quite as well as by plane regions, and are much easier to deal with in this form." (Louis Couturat, "The Algebra of Logic", 1914)

"A great discovery solves a great problem but there is a grain of discovery in the solution of any problem. Your problem may be modest; but if it challenges your curiosity and brings into play your inventive faculties, and if you solve it by your own means, you may experience the tension and enjoy the triumph of discovery." (George Polya, "How to solve it", 1944)

"Success in solving the problem depends on choosing the right aspect, on attacking the fortress from its accessible side." (George Polya, "How to Solve It", 1944)

"[The] function of thinking is not just solving an actual problem but discovering, envisaging, going into deeper questions. Often, in great discovery the most important thing is that a certain question is found." (Max Wertheimer, "Productive Thinking", 1945)

"We can scarcely imagine a problem absolutely new, unlike and unrelated to any formerly solved problem; but if such a problem could exist, it would be insoluble. In fact, when solving a problem, we should always profit from previously solved problems, using their result or their method, or the experience acquired in solving them." (George Polya, 1945)

"I believe, that the decisive idea which brings the solution of a problem is rather often connected with a well-turned word or sentence. The word or the sentence enlightens the situation, gives things, as you say, a physiognomy. It can precede by little the decisive idea or follow on it immediately; perhaps, it arises at the same time as the decisive idea. […]  The right word, the subtly appropriate word, helps us to recall the mathematical idea, perhaps less completely and less objectively than a diagram or a mathematical notation, but in an analogous way. […] It may contribute to fix it in the mind." (George Pólya [in a letter to Jaque Hadamard, "The Psychology of Invention in the Mathematical Field", 1949])

"The problems are solved, not by giving new information, but by arranging what we have known since long." (Ludwig Wittgenstein, "Philosophical Investigations", 1953)

"Solving problems is the specific achievement of intelligence." (George Pólya, 1957)

"Systems engineering embraces every scientific and technical concept known, including economics, management, operations, maintenance, etc. It is the job of integrating an entire problem or problem to arrive at one overall answer, and the breaking down of this answer into defined units which are selected to function compatibly to achieve the specified objectives." (Instrumentation Technology, 1957)

"A problem that is located and identified is already half solved!" (Bror R Carlson, "Managing for Profit", 1961)

"If we view organizations as adaptive, problem-solving structures, then inferences about effectiveness have to be made, not from static measures of output, but on the basis of the processes through which the organization approaches problems. In other words, no single measurement of organizational efficiency or satisfaction - no single time-slice of organizational performance can provide valid indicators of organizational health." (Warren G Bennis, "General Systems Yearbook", 1962)

"Solving problems can be regarded as the most characteristically human activity." (George Pólya, "Mathematical Discovery", 1962)

"The final test of a theory is its capacity to solve the problems which originated it." (George Dantzig, "Linear Programming and Extensions", 1963)

"It is a commonplace of modern technology that there is a high measure of certainty that problems have solutions before there is knowledge of how they are to be solved." (John K Galbraith, "The New Industrial State", 1967)

"An expert problem solver must be endowed with two incompatible qualities, a restless imagination and a patient pertinacity.” (Howard W Eves, “In Mathematical Circles”, 1969)

"The problem-solving approach allows for mental double-clutching. It does not require a direct switch from one point of view to another. It provides a period 'in neutural' where there is an openness to facts and, therefore, a willingness to consider alternative views." (William Reddin, "Managerial Effectiveness", 1970)

"In general, complexity and precision bear an inverse relation to one another in the sense that, as the complexity of a problem increases, the possibility of analysing it in precise terms diminishes. Thus 'fuzzy thinking' may not be deplorable, after all, if it makes possible the solution of problems which are much too complex for precise analysis." (Lotfi A Zadeh, "Fuzzy languages and their relation to human intelligence", 1972)

"If we deal with our problem not knowing, or pretending not to know the general theory encompassing the concrete case before us, if we tackle the problem "with bare hands", we have a better chance to understand the scientist's attitude in general, and especially the task of the applied mathematician." (George Pólya, "Mathematical Methods in Science", 1977)

"Systems represent someone's attempt at solution to problems, but they do not solve problems; they produce complicated responses." (Melvin J Sykes, Maryland Law Review, 1978)

“Solving problems can be regarded as the most characteristically human activity.” (George Polya, 1981)

"The problem solver needs to stand back and examine problem contexts in the light of different 'Ws' (Weltanschauungen). Perhaps he can then decide which 'W' seems to capture the essence of the particular problem context he is faced with. This whole process needs formalizing if it is to be carried out successfully. The problem solver needs to be aware of different paradigms in the social sciences, and he must be prepared to view the problem context through each of these paradigms." (Michael C Jackson, "Towards a System of Systems Methodologies", 1984)

"People in general tend to assume that there is some 'right' way of solving problems. Formal logic, for example, is regarded as a correct approach to thinking, but thinking is always a compromise between the demands of comprehensiveness, speed, and accuracy. There is no best way of thinking." (James L McKenney & Peter G W Keen, Harvard Business Review on Human Relations, 1986)

"A great many problems are easier to solve rigorously if you know in advance what the answer is." (Ian Stewart, "From Here to Infinity", 1987)

"Define the problem before you pursue a solution." (John Williams, Inc. Magazine's Guide to Small Business Success, 1987)

"No matter how complicated a problem is, it usually can be reduced to a simple, comprehensible form which is often the best solution." (Dr. An Wang, Nation's Business, 1987)

"There are many things you can do with problems besides solving them. First you must define them, pose them. But then of course you can also refi ne them, depose them, or expose them or even dissolve them! A given problem may send you looking for analogies, and some of these may lead you astray, suggesting new and different problems, related or not to the original. Ends and means can get reversed. You had a goal, but the means you found didn’t lead to it, so you found a new goal they did lead to. It’s called play. Creative mathematicians play a lot; around any problem really interesting they develop a whole cluster of analogies, of playthings." (David Hawkins, "The Spirit of Play", Los Alamos Science, 1987)

"A scientific problem can be illuminated by the discovery of a profound analogy, and a mundane problem can be solved in a similar way." (Philip Johnson-Laird, "The Computer and the Mind", 1988)

"Anecdotes may be more useful than equations in understanding the problem." (Robert Kuttner, "The New Republic", The New York Times, 1988)

"Most people would rush ahead and implement a solution before they know what the problem is." (Q T Wiles, Inc. Magazine, 1988)

“A mental model is a knowledge structure that incorporates both declarative knowledge (e.g., device models) and procedural knowledge (e.g., procedures for determining distributions of voltages within a circuit), and a control structure that determines how the procedural and declarative knowledge are used in solving problems (e.g., mentally simulating the behavior of a circuit).” (Barbara Y White & John R Frederiksen, “Causal Model Progressions as a Foundation for Intelligent Learning Environments”, Artificial Intelligence 42, 1990)

"An important symptom of an emerging understanding is the capacity to represent a problem in a number of different ways and to approach its solution from varied vantage points; a single, rigid representation is unlikely to suffice." (Howard Gardner, “The Unschooled Mind”, 1991)

“[By understanding] I mean simply a sufficient grasp of concepts, principles, or skills so that one can bring them to bear on new problems and situations, deciding in which ways one’s present competencies can suffice and in which ways one may require new skills or knowledge.” (Howard Gardner, “The Unschooled Mind”, 1991)

"We consider the notion of ‘system’ as an organising concept, before going on to look in detail at various systemic metaphors that may be used as a basis for structuring thinking about organisations and problem situations." (Michael C Jackson, "Creative Problem Solving: Total Systems Intervention", 1991)

“But our ways of learning about the world are strongly influenced by the social preconceptions and biased modes of thinking that each scientist must apply to any problem. The stereotype of a fully rational and objective ‘scientific method’, with individual scientists as logical (and interchangeable) robots, is self-serving mythology.” (Stephen Jay Gould, “This View of Life: In the Mind of the Beholder”, Natural History Vol. 103 (2), 1994)

"The term mental model refers to knowledge structures utilized in the solving of problems. Mental models are causal and thus may be functionally defined in the sense that they allow a problem solver to engage in description, explanation, and prediction. Mental models may also be defined in a structural sense as consisting of objects, states that those objects exist in, and processes that are responsible for those objects’ changing states." (Robert Hafner & Jim Stewart, "Revising Explanatory Models to Accommodate Anomalous Genetic Phenomena: Problem Solving in the ‘Context of Discovery’", Science Education 79 (2), 1995)

"The purpose of a conceptual model is to provide a vocabulary of terms and concepts that can be used to describe problems and/or solutions of design. It is not the purpose of a model to address specific problems, and even less to propose solutions for them. Drawing an analogy with linguistics, a conceptual model is analogous to a language, while design patterns are analogous to rhetorical figures, which are predefined templates of language usages, suited particularly to specific problems." (Peter P Chen [Ed.], "Advances in Conceptual Modeling", 1999)

"The three basic mechanisms of averaging, feedback and division of labor give us a first idea of a how a CMM [Collective Mental Map] can be developed in the most efficient way, that is, how a given number of individuals can achieve a maximum of collective problem-solving competence. A collective mental map is developed basically by superposing a number of individual mental maps. There must be sufficient diversity among these individual maps to cover an as large as possible domain, yet sufficient redundancy so that the overlap between maps is large enough to make the resulting graph fully connected, and so that each preference in the map is the superposition of a number of individual preferences that is large enough to cancel out individual fluctuations. The best way to quickly expand and improve the map and fill in gaps is to use a positive feedback that encourages individuals to use high preference paths discovered by others, yet is not so strong that it discourages the exploration of new paths." (Francis Heylighen, "Collective Intelligence and its Implementation on the Web", 1999)

"What it means for a mental model to be a structural analog is that it embodies a representation of the spatial and temporal relations among, and the causal structures connecting the events and entities depicted and whatever other information that is relevant to the problem-solving talks. […] The essential points are that a mental model can be nonlinguistic in form and the mental mechanisms are such that they can satisfy the model-building and simulative constraints necessary for the activity of mental modeling." (Nancy J Nersessian, "Model-based reasoning in conceptual change", 1999)

"A model is an imitation of reality and a mathematical model is a particular form of representation. We should never forget this and get so distracted by the model that we forget the real application which is driving the modelling. In the process of model building we are translating our real world problem into an equivalent mathematical problem which we solve and then attempt to interpret. We do this to gain insight into the original real world situation or to use the model for control, optimization or possibly safety studies." (Ian T Cameron & Katalin Hangos, "Process Modelling and Model Analysis", 2001)

"[...] a general-purpose universal optimization strategy is theoretically impossible, and the only way one strategy can outperform another is if it is specialized to the specific problem under consideration." (Yu-Chi Ho & David L Pepyne, "Simple explanation of the no-free-lunch theorem and its implications", Journal of Optimization Theory and Applications 115, 2002)

"Mathematical modeling is as much ‘art’ as ‘science’: it requires the practitioner to (i) identify a so-called ‘real world’ problem (whatever the context may be); (ii) formulate it in mathematical terms (the ‘word problem’ so beloved of undergraduates); (iii) solve the problem thus formulated (if possible; perhaps approximate solutions will suffice, especially if the complete problem is intractable); and (iv) interpret the solution in the context of the original problem." (John A Adam, "Mathematics in Nature", 2003)

"What is a mathematical model? One basic answer is that it is the formulation in mathematical terms of the assumptions and their consequences believed to underlie a particular ‘real world’ problem. The aim of mathematical modeling is the practical application of mathematics to help unravel the underlying mechanisms involved in, for example, economic, physical, biological, or other systems and processes." (John A Adam, "Mathematics in Nature", 2003)

"Alternative models are neither right nor wrong, just more or less useful in allowing us to operate in the world and discover more and better options for solving problems." (Andrew Weil," The Natural Mind: A Revolutionary Approach to the Drug Problem", 2004)

“A conceptual model is a mental image of a system, its components, its interactions. It lays the foundation for more elaborate models, such as physical or numerical models. A conceptual model provides a framework in which to think about the workings of a system or about problem solving in general. An ensuing operational model can be no better than its underlying conceptualization.” (Henry N Pollack, “Uncertain Science … Uncertain World”, 2005)

"Graphics is the visual means of resolving logical problems." (Jacques Bertin, "Graphics and Graphic Information Processing", 2011)

"In specific cases, we think by applying mental rules, which are similar to rules in computer programs. In most of the cases, however, we reason by constructing, inspecting, and manipulating mental models. These models and the processes that manipulate them are the basis of our competence to reason. In general, it is believed that humans have the competence to perform such inferences error-free. Errors do occur, however, because reasoning performance is limited by capacities of the cognitive system, misunderstanding of the premises, ambiguity of problems, and motivational factors. Moreover, background knowledge can significantly influence our reasoning performance. This influence can either be facilitation or an impedance of the reasoning process." (Carsten Held et al, "Mental Models and the Mind", 2006)

"Every problem has a solution; it may sometimes just need another perspective.” (Rebecca Mallery et al, "NLP for Rookies", 2009)

"Mental acuity of any kind comes from solving problems yourself, not from being told how to solve them.” (Paul Lockhart, "A Mathematician's Lament", 2009)

"Mostly we rely on stories to put our ideas into context and give them meaning. It should be no surprise, then, that the human capacity for storytelling plays an important role in the intrinsically human-centered approach to problem solving, design thinking." (Tim Brown, "Change by Design: How Design Thinking Transforms Organizations and Inspires Innovation", 2009)

"Mental models are formed over time through a deep enculturation process, so it follows that any attempt to align mental models must focus heavily on collective sense making. Alignment only happens through a process of socialisation; people working together, solving problems together, making sense of the world together." (Robina Chatham & Brian Sutton, "Changing the IT Leader’s Mindset", 2010)

"Mathematical modeling is the application of mathematics to describe real-world problems and investigating important questions that arise from it." (Sandip Banerjee, "Mathematical Modeling: Models, Analysis and Applications", 2014)

"Mental imagery is often useful in problem solving. Verbal descriptions of problems can become confusing, and a mental image can clear away excessive detail to bring out important aspects of the problem. Imagery is most useful with problems that hinge on some spatial relationship. However, if the problem requires an unusual solution, mental imagery alone can be misleading, since it is difficult to change one’s understanding of a mental image. In many cases, it helps to draw a concrete picture since a picture can be turned around, played with, and reinterpreted, yielding new solutions in a way that a mental image cannot." (James Schindler, "Followership", 2014)

“Framing the right problem is equally or even more important than solving it.” (Pearl Zhu, “Change, Creativity and Problem-Solving”, 2017)

🔬Data Science: Generative Adversarial Network (Definitions)

"A category of deep learning neural networks that are composed of two competitive neural networks together." (Dulani Meedeniya & Iresha Rubasinghe, "A Review of Supportive Computational Approaches for Neurological Disorder Identification", 2020) 

"A powerful machine learning technique made up of two learning systems that compete with each other in a game-like fashion. Features of the winning system are 'genetically' added to the loser along with random mutations. GANs teach themselves through this 'survival of the fittest' evolutionary model. They 'generate' new solutions through many, often millions, of generations." (Scott R Garrigan, "Frameworks for Integration of Future-Oriented Computational Thinking in K-12 Schools", 2020)

"An artificial intelligence process that includes a 'generator' that produces samples, and a 'discriminator' that differentiates between computer-generated samples and samples derived from 'real-world' sources." (Keram Malicki-Sanchez, "Out of Our Minds: Ontology and Embodied Media in a Post-Human Paradigm", 2020)

"Machine learning framework in which two neural networks compete against each other to win within a gaming environment using a supervised learning pattern." (Jose A R Pinheiro, "Contemporary Imagetics and Post-Images in Digital Media Art: Inspirational Artists and Current Trends (1948-2020)", 2020)

"It refers to a type of neural network that consists of a generative and a discriminative network that contest with each other especially in a game scenario. They are used to generate new data that are statistically similar to the training data." (Vijayaraghavan Varadharajan & J Rian Leevinson, "Next Generation of Intelligent Cities: Case Studies from Europe", 2021)

"A generative adversarial network, or GAN, is a deep neural network framework which is able to learn from a set of training data and generate new data with the same characteristics as the training data." (Thomas Wood)

🔬Data Science: Semi-supervised Learning (Definitions)

"machine learning technique that uses both labelled and unlabelled data for constructing the model." (Óscar Pérez & Manuel Sánchez-Montañés, "Class Prediction in Test Sets with Shifted Distributions", 2009)

"The set of learning algorithms in which the samples in training dataset are all unlabelled." (Jun Jiang & Horace H S Ip, "Active Learning with SVM, Encyclopedia of Artificial Intelligence", 2009) 

"Learning to label new data using both labeled training data plus unlabeled data." (Jesse Read & Albert Bifet, "Multi-Label Classification", 2014)

"A method of empirical concept learning from unlabeled data. The task is to build a model that finds groups of similar examples or that finds dependencies between attribute-value tuples." (Petr Berka, "Machine Learning", 2015)

"Combines the methodology of the supervised learning to process the labeled data with the unsupervised learning to compute the unlabeled data." (Nuno Pombo et al, "Machine Learning Approaches to Automated Medical Decision Support Systems", 2015)

"Estimation of the parameters of a model considering only un-labeled data and without the help of human experts." (Manuel Martín-Merino, "Semi-Supervised Dimension Reduction Techniques to Discover Term Relationships", 2015)

"In this category either the model is developed in such a way that either there are labels exist for all kind of observations or there is no label exist." (Neha Garg & Kamlesh Sharma, "Machine Learning in Text Analysis", 2020)

"It is a machine learning algorithm in which the machine learns from both labeled and unlabeled instances to build a model for predicting the class of unlabeled instances." (Gunjan Ansari et al, "Natural Language Processing in Online Reviews", 2021)

"Semi-supervised learning aims at labeling a set of unlabelled data with the help of a small set of labeled data." (Hari K Kondaveeti et al, "Deep Learning Applications in Agriculture: The Role of Deep Learning in Smart Agriculture", 2021)

"The semi-supervised learning combines both supervised and unsupervised learning algorithms." (M Govindarajan, "Big Data Mining Algorithms", 2021)

🔬Data Science: Unsupervised Learning (Definitions)

"A means of modifying the weights of a neural net without specifying the desired output for any input patterns. Used in self-organizing neural nets for clustering data, extracting principal components, or curve fitting." (Laurene V Fausett, "Fundamentals of Neural Networks: Architectures, Algorithms, and Applications", 1994)

"Learning in which no teacher is used to show the correct response to a given input stimulus; the system must organize itself purely on the basis of the input stimuli it receives. Often synonymous with clustering." (Guido J Deboeck & Teuvo Kohonen, "Visual explorations in finance with self-organizing maps", 2000)

"learning without a priori knowledge about the classification of samples; learning without a teacher. Often the same as formation of clusters, where after these clusters can be labeled. Also optimal allocation of computing resources when only unlabeled, unclassified data are input." (Teuvo Kohonen, "Self-Organizing Maps 3rd Ed.", 2001)

"Analysis methods that do not use any data to guide the technique operations." (Glenn J Myatt, "Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining", 2006)

"Learning techniques that group instances without a pre-specified dependent attribute. Clustering algorithms are usually unsupervised methods for grouping data sets." (Lluís Formiga & Francesc Alías, "GTM User Modeling for aIGA Weight Tuning in TTS Synthesis", Encyclopedia of Artificial Intelligence, 2009)

"Method of machine learning where a model is fit to observations. It is distinguished from supervised learning by the fact that there is no a priori output." (Soledad Delgado et al, "Growing Self-Organizing Maps for Data Analysis", Encyclopedia of Artificial Intelligence, 2009)

"The type of learning that occurs when algorithms adjust the weights in a neural network by reference to a training data set that includes input variables only. Unsupervised learning algorithms attempt to locate clusters in the input data." (Robert Nisbet et al, "Handbook of statistical analysis and data mining applications", 2009)

"Treats all variables the same way so as to determine the different classes based on diverse features observed in the collection of unlabeled data that encompass the sample set. It is assumed that the user is unaware of the classes due to the lack of information sufficiently available." (Nuno Pombo et al, "Machine Learning Approaches to Automated Medical Decision Support Systems", 2015)

"Unsupervised learning refers to a machine learning approach that uses inferential statistical modeling algorithms to discover rather than detect patterns or similarities in data. An unsupervised learning system can identify new patterns, instead of trying to match a set of patterns it encountered during training." (Judith S Hurwitz, "Cognitive Computing and Big Data Analytics", 2015)

"In unsupervised learning, the program gets the same items but has to come up with the categories on its own by discovering the underlying correlations between the items; that is why unsupervised learning is sometimes called statistical pattern recognition." (Robert J Glushko, "The Discipline of Organizing: Professional Edition, 4th Ed", 2016)

"A form of machine learning in which the goal is to identify regularities in the data. These regularities may include clusters of similar instances within the data or regularities between attributes. In contrast to supervised learning, in unsupervised learning no target attribute is defined in the data set." (John D Kelleher & Brendan Tierney, "Data science", 2018)

"Unsupervised learning identifies hidden patterns or intrinsic structures in the data. It is used to draw conclusions from datasets composed of labeled unacknowledged input data." (Soraya Sedkaoui, "Big Data Analytics for Entrepreneurial Success", 2018)

"Unsupervised learning or clustering is a way of discovering hidden structures in unlabeled data. Clustering algorithms aim to discover latent patterns in unlabeled data using features to organize instances into meaningfully dissimilar groups." (Benjamin Bengfort et al, "Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning", 2018)

"A particular form of learning process that takes place without supervision and that affects the training of an artificial neural networks." (Gaetano B Ronsivalle & Arianna Boldi, "Artificial Intelligence Applied: Six Actual Projects in Big Organizations", 2019)

"In this learning, the model does not require labeled data for training. The model learns the nature of data and does predictions." (Aman Kamboj et al, "Ear Localizer: A Deep-Learning-Based Ear Localization Model for Side Face Images in the Wild", 2019)

"A class of machine learning techniques designed to identify features and patterns in data. There is no mapping function to be learned or output values to be achieved. Cluster analysis is an example of unsupervised learning." (Timofei Bogomolov et al, "Identifying Patterns in Fresh Produce Purchases: The Application of Machine Learning Techniques", 2020)

"Unsupervised algorithms mean that a program is provided with some collection of data, with no predetermined dataset being available." (Neha Garg & Kamlesh Sharma, "Machine Learning in Text Analysis", 2020)

"A machine learning technique that involves providing a machine with data that is not labeled, instead allowing for the machine to learn by association." (Sujata Ramnarayan, "Marketing and Artificial Intelligence: Personalization at Scale", 2021)

"Unsupervised Learning aims at inferring the given unlabelled data using a different type of structures present in the data points." (Hari K Kondaveeti et al, "Deep Learning Applications in Agriculture: The Role of Deep Learning in Smart Agriculture", 2021)

"Unsupervised Learning is a type of machine learning in which the algorithm does not need the data with pre-defined labels. Unsupervised machine learning instead categorizes entries within datasets by examining similarities or anomalies and then grouping different entries accordingly." (Accenture)

🔬Data Science: Supervised Learning (Definitions)

"A training paradigm where the neural network is presented with an input pattern and a desired output pattern. The desired output is compared with the neural network output, and the error information is used to adjust the connection weights." (Joseph P Bigus, "Data Mining with Neural Networks: Solving Business Problems from Application Development to Decision Support", 1996)

"Learning in which a system is trained by using a teacher to show the system the desired response to an input stimulus, usually in the form of a desired output." (Guido J Deboeck and Teuvo Kohonen, "Visual explorations in finance with self-organizing maps", 2000)

"learning with a teacher; learning scheme in which the average expected difference between wanted output for training samples, and the true output, respectively, is decreased." (Teuvo Kohonen, "Self-Organizing Maps" 3rd Ed., 2001)

"Supervised learning, or learning from examples, refers to systems that are trained instead of programmed with a set of examples, that is, a set of input-output pairs." (Tomaso Poggio & Steve Smale, "The Mathematics of Learning: Dealing with Data", Notices of the AMS, 2003)

"Methods, which use a response variable to guide the analysis." (Glenn J Myatt, "Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining", 2006)

"A learning method in which there are two distinct phases to the operation. In the first phase each possible solution to a problem is assessed based on the input signal that is propagated through the system producing output respond. The actual respond produced is then compared with a desired response, generating error signals that are then used as a guide to solve the given problems using supervised learning algorithms". (Masoud Mohammadian, "Supervised Learning of Fuzzy Logic Systems", 2009)

"The set of learning algorithms in which the samples in the training dataset are all labelled." (Jun Jiang & Horace H S Ip, "Active Learning with SVM", Encyclopedia of Artificial Intelligence, 2009) 

"type of learning where the objective is to learn a function that associates a desired output (‘label’) to each input pattern. Supervised learning techniques require a training dataset of examples with their respective desired outputs. Supervised learning is traditionally divided into regression (the desired output is a continuous variable) and classification (the desired output is a class label)." (Óscar Pérez & Manuel Sánchez-Montañés, "Class Prediction in Test Sets with Shifted Distributions", 2009)

"Supervised learning is a type of machine learning that requires labeled training data." (Ivan Idris, "Python Data Analysis", 2014)

"Supervised learning refers to an approach that teaches the system to detect or match patterns in data based on examples it encounters during training with sample data." (Judith S Hurwitz, "Cognitive Computing and Big Data Analytics", 2015)

"The knowledge is obtained through a training which includes a data set called the training sample which is structured according to the knowledge base supported by human experts as physicians in medical context, and databases. It is assumed that the user knows beforehand the classes and the instances of each class." (Nuno Pombo et al, "Machine Learning Approaches to Automated Medical Decision Support Systems", 2015)

"In supervised learning, a machine learning program is trained with sample items or documents that are labeled by category, and the program learns to assign new items to the correct categories." (Robert J Glushko, "The Discipline of Organizing: Professional Edition" 4th Ed., 2016)

"A form of machine learning in which the goal is to learn a function that maps from a set of input attribute values for an instance to an estimate of the missing value for the target attribute of the same instance." (John D Kelleher & Brendan Tierney, "Data science", 2018)

"A supervised learning algorithm applies a known set of input data and drives a model to produce reasonable predictions for responses to new data. Supervised learning develops predictive models using classification and regression techniques." (Soraya Sedkaoui, "Big Data Analytics for Entrepreneurial Success", 2018)

"It consists in learning from data with a known-in-advance outcome that is predicted based on a set of inputs, referred to as 'features'." (Iva Mihaylova, "Applications of Artificial Neural Networks in Economics and Finance", 2018)

"Supervised learning is the data mining task of inferring a function from labeled training data." (Dharmendra S Rajput et al, "Investigation on Deep Learning Approach for Big Data: Applications and Challenges", 2018)

"A particular form of learning process that takes place under supervision and that affects the training of an artificial neural networks." (Gaetano B Ronsivalle & Arianna Boldi, "Artificial Intelligence Applied: Six Actual Projects in Big Organizations", 2019)

"A type of machine learning in which output datasets train the machine to generate the desired algorithms, like a teacher supervising a student." (Kirti R Bhatele et al, "The Role of Artificial Intelligence in Cyber Security", 2019)

"In this learning, the model needs a labeled data for training. The model knows in advance the answer to the questions it must predict and tries to learn the relationship between input and output." (Aman Kamboj et al, "EarLocalizer: A Deep-Learning-Based Ear Localization Model for Side Face Images in the Wild", 2019)

"A machine learning task designed to learn a function that maps an input onto an output based on a set of training examples (training data). Each training example is a pair consisting of a vector of inputs and an output value. A supervised learning algorithm analyzes the training data and infers a mapping function. A simple example of supervised learning is a regression model." (Timofei Bogomolov et al, "Identifying Patterns in Fresh Produce Purchases: The Application of Machine Learning Techniques", 2020)

"Supervised algorithms mean that a system is developed or modeled on predetermined set of sample data." (Neha Garg & Kamlesh Sharma, "Machine Learning in Text Analysis", 2020)

"A machine learning technique that involves providing a machine with data that is labeled." (Sujata Ramnarayan, "Marketing and Artificial Intelligence: Personalization at Scale", 2021)

"It is machine learning algorithm in which the model learns from ample amount of available labeled data to predict the class of unseen instances." (Gunjan Ansari et al, "Natural Language Processing in Online Reviews", 2021)

"Supervised learning aims at developing a function for a set of labeled data and outputs." (Hari K Kondaveeti et al, "Deep Learning Applications in Agriculture: The Role of Deep Learning in Smart Agriculture", 2021)

"The supervised learning algorithms are trained with a complete set of data and thus, the supervised learning algorithms are used to predict/forecast." (M Govindarajan, "Big Data Mining Algorithms", 2021)

"Supervised Learning is a type of machine learning in which an algorithm takes a labelled data set (data that’s been organized and described), deduces key features characterizing each label, and learns to recognize them in new unseen data." (Accenture)

🔬Data Science: Deep Learning (Definitions)

"Deep learning is an area of machine learning that emerged from the intersection of neural networks, artificial intelligence, graphical modeling, optimization, pattern recognition and signal processing." (N D Lewis, "Deep Learning Made Easy with R: A Gentle Introduction for Data Science", 2016)

"Methods that are used to train models with several levels of abstraction from the raw input to the output. For example, in visual recognition, the lowest level is an image composed of pixels. In layers as we go up, a deep learner combines them to form strokes and edges of different orientations, which can then be combined to detect longer lines, arcs, corners, and junctions, which in turn can be combined to form rectangles, circles, and so on. The units of each layer may be thought of as a set of primitives at a different level of abstraction." (Ethem Alpaydın, "Machine learning : the new AI", 2016)

"A branch of machine learning to whose architectures belong deep ANNs. The term “deep” denotes the application of multiple layers with a complex structure." (Iva Mihaylova, "Applications of Artificial Neural Networks in Economics and Finance", 2018)

"A deep-learning model is a neural network that has multiple (more than two) layers of hidden units (or neurons). Deep networks are deep in terms of the number of layers of neurons in the network. Today many deep networks have tens to hundreds of layers. The power of deep-learning models comes from the ability of the neurons in the later layers to learn useful attributes derived from attributes that were themselves learned by the neurons in the earlier layers." (John D Kelleher & Brendan Tierney, "Data science", 2018)

"Also known as deep structured learning or hierarchical learning is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms." (Soraya Sedkaoui, "Big Data Analytics for Entrepreneurial Success", 2018)

"Deep learning broadly describes the large family of neural network architectures that contain multiple, interacting hidden layers." (Benjamin Bengfort et al, "Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning", 2018)

"It is a part of machine learning approach used for learning data representations." (Dharmendra S Rajput et al, "Investigation on Deep Learning Approach for Big Data: Applications and Challenges", 2018)

"The ability of a neural network to improve its learning process." (David Natingga, "Data Science Algorithms in a Week" 2nd Ed., 2018)

"A learning algorithm using a number of layers for extracting and learning feature hierarchies before providing an output for any input." (Tanu Wadhera & Deepti Kakkar, "Eye Tracker: An Assistive Tool in Diagnosis of Autism Spectrum Disorder", 2019)

"a machine-learning technique that extends standard artificial neural network models to many layers representing different levels of abstraction, say going from individual pixels of an image through to recognition of objects." (David Spiegelhalter, "The Art of Statistics: Learning from Data", 2019)

"A part of a broader family of machine learning methods based on learning data representations." (Nil Goksel & Aras Bozkurt, "Artificial Intelligence in Education: Current Insights and Future Perspectives", 2019)

"A recent method of machine learning based on neural networks with more than one hidden layer." (Samih M Jammoul et al, "Open Source Software Usage in Education and Research: Network Traffic Analysis as an Example", 2019)

"A subbranch of machine learning which inspires from the artificial neural network. It has eliminated the need to design handcrafted features as in deep learning features are automatically learned by the model from the data." (Aman Kamboj et al, "Ear Localizer: A Deep-Learning-Based Ear Localization Model for Side Face Images in the Wild", 2019)

"It is class of one machine learning algorithms that can be supervised, unsupervised, or semi-supervised. It uses multiple layers of processing units for feature extraction and transformation." (Siddhartha Kumar Arjaria & Abhishek S Rathore, "Heart Disease Diagnosis: A Machine Learning Approach", 2019)

"Is the complex, unsupervised processing of unstructured data in order to create patterns used in decision making, patterns that are analogous to those of the human brain." (Samia H Rizk, "Risk-Benefit Evaluation in Clinical Research Practice", 2019)

"The ability for machines to autonomously mimic human thought patterns through artificial neural networks composed of cascading layers of information." (Kirti R Bhatele et al, "The Role of Artificial Intelligence in Cyber Security", 2019)

"The method for solving problems that have more probabilistic calculations based on artificial neural networks." (Tolga Ensari et al, "Overview of Machine Learning Approaches for Wireless Communication", 2019)

"A category of machine learning methods which is inspired by the artificial neural networks" (Shouvik Chakraborty & Kalyani Mali, "An Overview of Biomedical Image Analysis From the Deep Learning Perspective", 2020)

"A sub-field of machine learning which is based on the algorithms and layers of artificial networks." (S Kayalvizhi & D Thenmozhi, "Deep Learning Approach for Extracting Catch Phrases from Legal Documents", 2020)

"A type of machine learning based on artificial neural networks. It can be supervised, unsupervised, or semi-supervised, and it uses an artificial neural network with multiple layers between the input and output layers." (Timofei Bogomolov et al, "Identifying Patterns in Fresh Produce Purchases: The Application of Machine Learning Techniques", 2020)

"An extension of machine learning approach, which uses neural network." (Neha Garg & Kamlesh Sharma, "Machine Learning in Text Analysis", 2020)

"Deep learning (also known as deep structured learning or hierarchical learning) is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, semi-supervised or unsupervised." (R Murugan, "Implementation of Deep Learning Neural Network for Retinal Images", 2020)

 "Deep learning is a collection of algorithms used in machine learning, used to model high-level abstractions in data through the use of model architectures, which are composed of multiple nonlinear transformations. It is part of a broad family of methods used for machine learning that are based on learning representations of data." (Edward T Chen, "Deep Learning and Sustainable Telemedicine", 2020)

"Deep learning is a collection of neural-network techniques that generally use multiple layers." (Alex Thomas, "Natural Language Processing with Spark NLP", 2020)

"Deep learning is a kind of machine learning technique with automatic image interpretation and feature learning facility. The different deep learning algorithms are convolutional neural network (CNN), deep neural network (DNN), recurrent neural network (RNN), genetic adversarial networks (GAN), etc." (Rajandeep Kaur & Rajneesh Rani, "Comparative Study on ASD Identification Using Machine and Deep Learning", 2020)

"Deep learning is a subset of machine learning that models high-level abstractions in data by means of network architectures, which are composed of multiple nonlinear transformations." (Loris Nanni et al, "Digital Recognition of Breast Cancer Using TakhisisNet: An Innovative Multi-Head Convolutional Neural Network for Classifying Breast Ultrasonic Images", 2020)

"In contradistinction to surface or superficial learning, deep learning is inextricably associated with long-term retention of pertinent and solid knowledge, based on a thorough and critical understanding of the object of study, be it curricular content or not." (Leonor M Martínez-Serrano, "The Pedagogical Potential of Design Thinking for CLIL Teaching: Creativity, Critical Thinking, and Deep Learning", 2020)

"Is a group of methods that allow multilayer computing models to work with data that has an abstraction hierarchy." (Heorhii Kuchuk et al, "Application of Deep Learning in the Processing of the Aerospace System's Multispectral Images", 2020)

"It is a part of machine learning intended for learning form large amounts of data, as in the case of experience-based learning. It can be considered that feature engineering in deep learning-based models is partly left to the machine. In the case of artificial neural networks, deep neural networks are expected to have various layers within architectures for solving complex problems with higher accuracy compared to traditional machine learning. Moreover, high performance automatic results are expected without human intervention." (Ana Gavrovska & Andreja Samčović, "Intelligent Automation Using Machine and Deep Learning in Cybersecurity of Industrial IoT", 2020)

"Is a subset of AI and machine learning that uses multi-layered artificial neural networks to learn from data that is unstructured or unlabeled." (Lejla Banjanović-Mehmedović & Fahrudin Mehmedović, "Intelligent Manufacturing Systems Driven by Artificial Intelligence in Industry 4.0", 2020)

"This method is also called as hierarchical learning or deep structured learning. It is one of the machine learning method that is based on learning methods like supervised, semi-supervised or unsupervised. The only difference between deep learning and other machine learning algorithm is that deep learning method uses big data as input." (Anumeera Balamurali & Balamurali Ananthanarayanan,"Develop a Neural Model to Score Bigram of Words Using Bag-of-Words Model for Sentiment Analysis", 2020)

"A form of machine learning which uses multi-layered architectures to automatically learn complex representations of the input data. Deep models deliver state-of-the-art results across many fields, e.g. computer vision and NLP." (Vincent Karas & Björn W Schuller, "Deep Learning for Sentiment Analysis: An Overview and Perspectives", 2021)

"A sub branch of Artificial intelligence in which we built the DL model and we don’t need to specify any feature to the learning model . In case of DL the model will classify the data based on the input data." (Ajay Sharma, "Smart Agriculture Services Using Deep Learning, Big Data, and IoT", 2021)

"A sub-set of machine learning in artificial intelligence (AI) with network capabilities supporting learning unsupervised from unstructured data." (Mark Schofield, "Gamification Tools to Facilitate Student Learning Engagement in Higher Education: A Burden or Blessing?", 2021)

"A subarea of machine learning, which adopts a deeper and more complex neural structure to reach state-of-the-art accuracy in a given problem. Commonly applied in machine learning areas, such as classification and prediction." (Jinnie Shin et al, "Automated Essay Scoring Using Deep Learning Algorithms", 2021)

"A subset of a broader family of machine learning methods that makes use of multiple layers to extract data from raw input in order to learn its features." (R Karthik et al, "Performance Analysis of GAN Architecture for Effective Facial Expression Synthesis", 2021)

"An artificial intelligence function that imitates the workings of the human brain in processing data and creating patterns for use in decision making." (Wissam Abbass et al, "Internet of Things Application for Intelligent Cities: Security Risk Assessment Challenges", 2021)

"Another term for unsupervised learning that includes reinforcement learning in which the machine responds to reaching goals given input data and constraints. Deep learning deals with multiple layers simulating neural networks with ability to process immense amount of data." (Sujata Ramnarayan, "Marketing and Artificial Intelligence: Personalization at Scale", 2021)

"Application of multi neuron, multi-layer neural networks to perform learning tasks." (Revathi Rajendran et al, "Convergence of AI, ML, and DL for Enabling Smart Intelligence: Artificial Intelligence, Machine Learning, Deep Learning, Internet of Things", 2021)

 "Deep learning approach is a subfield of the machine learning technique. The concepts of deep learning influenced by neuron and brain structure based on ANN (Artificial Neural Network)." (Sayani Ghosal & Amita Jain, "Research Journey of Hate Content Detection From Cyberspace", 2021)

"Deep learning is a compilation of algorithms used in machine learning, and used to model high-level abstractions in data through the use of model architectures." (M Srikanth Yadav & R Kalpana, "A Survey on Network Intrusion Detection Using Deep Generative Networks for Cyber-Physical Systems", 2021)

"Deep learning is a subfield of machine learning that uses artificial neural networks to predict, classify, and generate data." (Usama A Khan & Josephine M Namayanja, "Reevaluating Factor Models: Feature Extraction of the Factor Zoo", 2021)

"Deep leaning is a subset of machine learning to solve complex problems/datasets." (R Suganya et al, "A Literature Review on Thyroid Hormonal Problems in Women Using Data Science and Analytics: Healthcare Applications", 2021)

"Deep learning is a type of machine learning that can process a wider range of data resources, requires less data preprocessing by humans, and can often produce more accurate results than traditional machine-learning approaches. In deep learning, interconnected layers of software-based calculators known as 'neurons' form a neural network. The network can ingest vast amounts of input data and process them through multiple layers that learn increasingly complex features of the data at each layer. The network can then make a determination about the data, learn if its determination is correct, and use what it has learned to make determinations about new data. For example, once it learns what an object looks like, it can recognize the object in a new image." (Bistra K Vassileva, "Artificial Intelligence: Concepts and Notions", 2021)

"Deep learning refers to artificial neural networks that mimic the workings of the human brain in the formation of patterns used in data processing and decision-making. Deep learning is a subset of machine learning. They are artificial intelligence networks capable of learning from unstructured or unlabeled data." (Atakan Gerger, "Technologies for Connected Government Implementation: Success Factors and Best Practices", 2021)

"It is a machine learning method using multiple layers of nonlinear processing units to extract features from data." (Sercan Demirci et al, "Detection of Diabetic Retinopathy With Mobile Application Using Deep Learning", 2021)

"It is a subarea of machine learning, where the models are built using multiple layers of artificial neural networks for learning useful patterns from raw data." (Gunjan Ansari et al, "Natural Language Processing in Online Reviews", 2021)

"It is an artificial intelligence technology that imitates the role of the human brain in data processing and the development of decision-making patterns." (Mehmet A Cifci, "Optimizing WSNs for CPS Using Machine Learning Techniques", 2021)

"One part of the broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised." (Jan Bosch et al, "Engineering AI Systems: A Research Agenda", 2021)

"Part of Machine Learning, where methods of higher complexity are used for training data representation." (Andrej Zgank et al, "Embodied Conversation: A Personalized Conversational HCI Interface for Ambient Intelligence", 2021)

"Sub-domain in the field of machine learning that deals with the use of algorithms inspired by human brain cells to solve complex real-world problems." (Shatakshi Singhet al, "A Survey on Intelligence Tools for Data Analytics", 2021)

"This is also a subset of AI where unstructured data is processed using layers of neural networks to identify, predict and detect patterns. Deep learning is used when there is a large amount of unlabeled data and problem is too complex to be solved using machine learning algorithms. Deep learning algorithms are used in computer vision and facial recognition systems." (Vijayaraghavan Varadharajan & Akanksha Rajendra Singh, "Building Intelligent Cities: Concepts, Principles, and Technologies", 2021)

"A rapidly evolving machine learning technique used to build, train, and test neural networks that probabilistically predict outcomes and/or classify unstructured data." (Forrester)

"Deep Learning is a subset of machine learning concerned with large amounts of data with algorithms that have been inspired by the structure and function of the human brain, which is why deep learning models are often referred to as deep neural networks. It is is a part of a broader family of machine learning methods based on learning data representations, as opposed to traditional task-specific algorithms." (Databricks) [source]

"Deep Learning refers to complex multi-layer neural nets.  They are especially suitable for image and voice recognition, and for unsupervised tasks with complex, unstructured data." (Statistics.com)

"is a machine learning methodology where a system learns the patterns in data by automatically learning a hierarchical layer of features. " (Accenture)

⛏️Data Management: Hashing (Definitions)

"A technique for providing fast access to data based on a key value by determining the physical storage location of that data." (Jan L Harrington, "Relational Database Dessign: Clearly Explained" 2nd Ed., 2002)

"A mathematical technique for assigning a unique number to each record in a file." (S. Sumathi & S. Esakkirajan, "Fundamentals of Relational Database Management Systems", 2007)

"A technique that transforms a key value via an algorithm to a physical storage location to enable quick direct access to data. The algorithm is typically referred to as a randomizer, because the goal of the hashing routine is to spread the key values evenly throughout the physical storage." (Craig S Mullins, "Database Administration", 2012)

"A mathematical technique in which an infinite set of input values is mapped to a finite set of output values, called hash values. Hashing is useful for rapid lookups of data in a hash table." (Oracle, "Database SQL Tuning Guide Glossary", 2013)

"An algorithm converts data values into an address" (Daniel Linstedt & W H Inmon, "Data Architecture: A Primer for the Data Scientist", 2014)

"The technique used for ordering and accessing elements in a collection in a relatively constant amount of time by manipulating the element’s key to identify the element’s location in the collection" (Nell Dale et al, "Object-Oriented Data Structures Using Java" 4th Ed., 2016)

"The application of an algorithm to a search key to derive a physical storage location." (George Tillmann, "Usage-Driven Database Design: From Logical Data Modeling through Physical Schmea Definition", 2017)

"Hashing is the process of mapping data values to fixed-size hash values (hashes). Common hashing algorithms are Message Digest 5 (MD5) and Secure Hashing Algorithm (SHA). It’s impossible to turn a hash value back into the original data value." (Piethein Strengholt, "Data Management at Scale", 2020)

"A mathematical technique in which an infinite set of input values is mapped to a finite set of output values, called hash values. Hashing is useful for rapid lookups of data in a hash table." (Oracle, "Oracle Database Concepts")

"A process used to convert data into a string of numbers and letters." (AICPA)

"A technique for arranging a set of items, in which a hash function is applied to the key of each item to determine its hash value. The hash value identifies each item's primary position in a hash table, and if this position is already occupied, the item is inserted either in an overflow table or in another available position in the table." (IEEE 610.5-1990)

🌡️Performance Management: Brainstorming (Definitions)

"A group of people working together to generate ideas." (Timothy J  Kloppenborg et al, "Project Leadership", 2003)

"A creative technique used to come up with ideas or concepts. In Product Management, brainstorming can be used for product ideation or general problem solving." (Steven Haines, "The Product Manager's Desk Reference", 2008)

"A general data gathering and creativity technique that can be used to identify risks, ideas, or solutions to issues by using a group of team members or subject matter experts." (For Dummies, "PMP Certification All-in-One For Dummies" 2nd Ed., 2013)

"A group technique for discovering creative solutions to a problem." (Rod Stephens, "Beginning Software Engineering", 2015)

"A problem-solving meeting with specific rules intended to generate a wide range of ideas." (Pamela Schure & Brian Lawley, "Product Management For Dummies", 2017)

"A general data gathering and creativity technique that can be used to identify risks, ideas, or solutions to issues by using a group of team members or subject-matter experts." (Cynthia Stackpole, "PMP® Certification All-in-One For Dummies", 2011)

"An idea generation process in which individuals or groups are instructed to generate ideas without criticizing or censoring themselves or one another." (Rachel Heinen et al, "Tools for the Process: Technology to Support Creativity and Innovation", 2015)

"A technique that helps a team to generate ideas" (ITIL)

🚧Project Management: Program Evaluation and Review Technique (Definitions)

"A method of depicting, scheduling, and prioritizing a complex set of activities in a way that supports effective project management. This method provides excellent visibility into a project's progress and potential obstacles and risks." (Steven Haines, "The Product Manager's Desk Reference", 2008)

"A technique for estimating that applies as weighted average of optimistic, pessimistic, and most likely estimates when there is uncertainty with the individual activity estimates." (Project Management Institute, "Practice Standard for Project Estimating", 2010)

"A model for project or process management to evaluate tasks involved in the project or process in order to find the shortest duration possible." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"Evaluates the probable duration of a project by calculating a weighted average of the best-case estimate, most likely case estimate, and worst-case estimate." (Bonnie Biafore, "Successful Project Management: Applying Best Practices and Real-World Techniques with Microsoft® Project", 2011)

"A statistical approach to estimating that uses a weighted average of three values: optimistic, pessimistic, and most likely." (Bonnie Biafore & Teresa Stover, "Your Project Management Coach: Best Practices for Managing Projects in the Real World", 2012)

"An estimating technique that starts with a network chart and combines optimistic, best estimate and pessimistic estimates to produce an overall estimate of the most likely duration and standard deviation (spread of likely durations) for a project activity." (Mike Clayton, "Brilliant Project Leader", 2012)

"An event-oriented network analysis technique used to estimate project duration when there is a high degree of uncertainty with individual activity duration estimates. PERT applies the critical path method to a weighted average duration estimate. It is considered a probabilistic method." (Peter Oakander et al, "CPM Scheduling for Construction: Best Practices and Guidelines", 2014)

📈Graphical Representation: Mind Map (Definitions)

"A visual note-taking process that pares thoughts to key words and pictures illustrating the relationships among concepts." (Ruth C Clark & Chopeta Lyons, "Graphics for Learning", 2004)

"A mind map consists of a central concept which acts as a headline for the map and the branches that represent the aspects of the main concept. A mind map allows summarizing and decomposition of the key aspects of a complex problem or issue." (Hannu Kivijärvi et al, "A Support System for the Strategic Scenario Process", 2008) 

"A mind map is a diagram uses intuition to depict words, ideas or other items in branches around a central key word or idea." (Wan Ng & Ria Hanewald, "Concept Maps as a Tool for Promoting Online Collaborative Learning in Virtual Teams with Pre-Service Teachers", 2010)

[mind mapping:] "A process that brainstorms ideas, words, tasks or other elements and arranges them in groups around a central notion."  (Wan Ng & Ria Hanewald, "Concept Maps as a Tool for Promoting Online Collaborative Learning in Virtual Teams with Pre-Service Teachers", 2010)

[mind-mapping:] "A technique that uses multiple levels of detail for a texture. This technique selects from among the different sizes of an image available, or possibly combines the two nearest sized matches to produce the final fragments used for texturing." (Graham Sellers et al, "OpenGL SuperBible: Comprehensive Tutorial and Reference" 5th Ed., 2010)

"Refers to a technique for the graphical representation of information items, enabling visualization. A mindmap has a radial structure: it is constructed by starting from a central information item, around which other information items are organized like rays from a star, except that each ray can in turn be subdivided in a plurality of finer rays, and so on. The 'rays' are linear, going from an upstream point to downstream, more secondary points, and so on." (Humbert Lesca & Nicolas Lesca, "Weak Signals for Strategic Intelligence: Anticipation Tool for Managers", 2011)

"Powerful techniques you can utilize to increase your comprehension of written materials." (Jeffrey Magee, "The Managerial Leadership Bible", 2015)

[mind-mapping:] "A technique used to consolidate ideas created through individual brainstorming sessions into a single map to reflect commonality and differences in understanding and to generate new ideas." (Project Management Institute, "A Guide to the Project Management Body of Knowledge (PMBOK Guide)", 2017)

[mind mapping:] "A method to brainstorm thoughts while showing relationships of the parts to the whole." (Errick D Farmer et al, "Digital Course Redesign to Increase Student Engagement and Success", 2019)

"A diagram used to represent words, ideas, tasks, or other items linked to and arranged around a central keyword or idea. Mind maps are  used to generate, visualize, structure, and classify ideas, and as an aid in study, organization, problem solving, decision making, and writing." (Software Quality Assurance)

🛢DBMS: SQL Injection (Definitions)

"SQL injection is a technique that exploits security vulnerabilities in the application layer and middle tier, allowing users to execute arbitrary SQL statements on a server." (Michael Coles, "Pro T-SQL 2008 Programmer's Guide", 2008)

"A security vulnerability that occurs in the persistence/database layer of a Web application. This vulnerability is derived from the incorrect escaping of variables embedded in SQL statements. It is in fact an instance of a more general class of vulnerabilities based on poor input validation and bad design that can occur whenever one programming or scripting language is embedded inside another." (Mark S Merkow & Lakshmikanth Raghavan, "Secure and Resilient Software Development", 2010)

"A form of Web hacking whereby SQL statements are specified in a Web form to expose data to the attacker." (Craig S Mullins, "Database Administration", 2012)

"SQL injection is a technique that exploits security vulnerabilities in the application layer and middle tier, allowing users to execute arbitrary SQL statements on a server." (Jay Natarajan et al, "Pro T-SQL 2012 Programmer's Guide 3rd Ed", 2012)

"The process of manipulating a web application to run SQL commands sent by an attacker." (Mark Rhodes-Ousley, "Information Security: The Complete Reference, Second Edition, 2nd Ed.", 2013)

"A technique that exploits security vulnerabilities in the application layer and middle tier, allowing users to execute arbitrary SQL statements on a server." (Miguel Cebollero et al, "Pro T-SQL Programmer’s Guide 4th Ed", 2015)

🌁Software Engineering: Software Engineering [SE] (Definitions)

"Software engineering is the establishment and use of sound engineering principles in order to obtain economically software that is reliable and works efficiently on real machines." (Peter Naur & Brian Randell, 1968)

“[Software engineering is the] establishment and use of sound engineering principles to obtain economically software that is reliable and works on real machines efficiently. (Friedrich Bauer, "Software Engineering", Information Processing, 1972) 

"(1) The application of a systematic, disciplined, quantifiable approach to the development, operation, and maintenance of software. (2) The study of approaches as in (1)." (Sandy Shrum et al, "CMMI®: Guidelines for Process Integration and Product Improvement", 2003)

"(1) The disciplined and systematic application of methods, techniques, and tools to the development, operation, and maintenance of software and software-intensive systems. (2) The building of large, complex software-intensive systems by teams of engineers and programmers. (3) The study of the activities defined in (1) and (2)." (Richard D Stutzke, "Estimating Software-Intensive Systems: Projects, Products, and Processes", 2005)

"A discipline that advocates a systematic approach of developing high-quality software on a large-scale while taking into account the factors of sustainability and longevity, as well as, organizational constraints of time and resources." (Pankaj Kamthan, "Pair Modeling", 2008)

"Application of a systematic, disciplined, quantifiable approach to the development of Information Systems/Software." (Leonor Teixeira et al, "Web-Enabled System Design for Managing Clinical Information", 2008)

"Techniques, models, and processes to develop quality software" (M J Escalona & G Aragón, "The Use of Metamodels in Web Requirements to Assure the Consistence", 2008)

"To engineer the development of software comprises a rigorous analysis and design of the product, the application of formal methods in the development process an in the resulting product description, and modularization of the relevant parts of the software to allow for malleability and for reusability." (Alke Martens & Andreas Harrer, "Software Engineering in e-Learning Systems", 2008)

"It is the computer science discipline concerned with creating and maintaining software applications by applying technologies and practices from computer science, project management, engineering, application domains, and other fields." (Graciela D S Hadad & Jorge H Doorn, "Creating Software System Context Glossaries", 2009)

"Software engineering is a well-established discipline that groups together a set of techniques and methodologies for improving software quality and structuring the development process." (Ghita K Mostéfaoui, "Software Engineering for Mobile Multimedia: A Roadmap", 2009)

"The software engineering discipline covers the development of software systems. Software engineers focus on applying systematic, disciplined, and quantifiable approaches to the development, operation, and maintenance of software." (Rick Gibson, "Software and Systems Engineering Integration", 2009)

"A discipline that advocates a systematic approach of developing high-quality software on a large-scale while taking into account the factors of sustainability and longevity, as well as, organizational constraints of resources." (Pankaj Kamthan, "A Social Web Perspective of Software Engineering Education", 2010)

"Is a systematic and disciplined approach to developing software, which applies both computer science and engineering principles and practices to the creation, operation and maintenance of software systems." (Christian Scholz et al, "From the Lab to the Factory Floor: Engineering Software for Wireless Sensor Networks", 2012)

"Is the study and application of engineering to the design, development, and maintenance of software. In short, the application of a systematic, disciplined, quantifiable approach to the development, operation, and maintenance of software." (Rosanna Costaguta, "Data Mining Applications in Computer-Supported Collaborative Learning", 2015)

"An engineering discipline that involves with all aspect of software development that applies engineering approaches in order to deliver high quality software products." (Seyed R Shahamiri, "The Challenges of Teaching and Learning Software Programming to Novice Students", 2018)

"The application of a systematic, disciplined, quantifiable approach to the development, operation, and maintenance of software; that is, the application of engineering to software." (Tamer Abdou et al, "Developing a Glossary for Software Projects", Encyclopedia of Information Science and Technology, Fourth Edition, 2018)

"Software engineering is the discipline and best practices used in developing software." (Alex Thomas, "Natural Language Processing with Spark NLP", 2020)

"The application of engineering to the development of software in a systematic method." (Shanmuganathan Vasanthapriyan & Kalpani M U Arachchi, "Effectiveness of Scrum and Kanban on Agile-Based Software Maintenance Projects", 2020)

"The application of a systematic, disciplined, quantifiable approach to the development, operation, and maintenance of software, and the study of these approaches." (Antonio A M Costa, "Disciplined Teams vs. Agile Teams: Differences and Similarities in Software Development", 2021)

"The systematic application of engineering approaches to the development of software." (Jan Bosch et al, "Engineering AI Systems: A Research Agenda", 2021)

"It is the establishment and use of robust, targeted engineering principles to obtain economic, reliable, efficient software that satisfies the user needs." (Mirna Muñoz, "Boosting the Competitiveness of Organizations With the Use of Software Engineering", 2021)

"The application of engineering to the development of software in a systematic method." (Kamalendu Pal & Bill Karakostas, "Software Testing Under Agile, Scrum, and DevOps", 2021)

"The application and the study of systematic, disciplined, quantifiable approaches to the development, operation, and maintenance of software." (IEEE 610.12)

🌁Software Engineering: Inspection (Definitions)

"Visual examination of work products to detect errors, violations of development standards, and other problems." (Richard D Stutzke, "Estimating Software-Intensive Systems: Projects, Products, and Processes", 2005)

"The process of examining a component, subassembly, subsystem, or product for off-target performance, variability, and defects during either product development or manufacturing. The focus is typically on whether the item under inspection is within the allowable tolerances. As with all processes, inspection itself is subject to variability, and out-of-spec parts or functions might pass inspection inadvertently." (Clyde M Creveling, "Six Sigma for Technical Processes: An Overview for R Executives, Technical Leaders, and Engineering Managers", 2006)

"A core technique in software quality assurance where a group of reviewers independently and systematically examine software artifacts to find defects. Inspections are highly effective where software testing is not possible, in particular, for textual specifications and design documents." (Frank Padberg, "Counting the Hidden Defects in Software Documents", 2010)

"Examining or measuring to verify whether an activity, component, product, result, or service conforms to specified requirements. " (Cynthia Stackpole, "PMP® Certification All-in-One For Dummies®", 2011)

"A type of review that relies on visual examination of documents to detect defects - for example, violations of development standards and nonconformance to higher-level documentation. Inspection is the most formal review technique and therefore always based on a documented procedure." (Tilo Linz et al, "Software Testing Foundations" 4th Ed., 2014)

"A verification method in which one member of a team reads the program or design aloud line by line and the others point out errors" (Nell Dale & John Lewis, "Computer Science Illuminated" 6th Ed., 2015)

"Examination of a work product to determine whether it conforms to documented standards." (Project Management Institute, "A Guide to the Project Management Body of Knowledge (PMBOK® Guide )", 2017)

 "A type of review that relies on visual examination of documents to detect defects, e.g. violations of development standards and non- conformance to higher level documentation. The most formal review technique and therefore always based on a documented procedure" (IEEE 610, IEEE 1028)

🌁Software Engineering: Black-Box Testing (Definitions)

"A specification-based test that looks at a system or unit exclusively from the outside, that is, over its public interface;" (Johannes Link & Peter Fröhlich, "Unit Testing in Java", 2003)

"This test compares the externally observable behavior at the external software interfaces (without knowledge of their structure) with the desired behavior. Black-Box tests are frequently equated with »functional tests«, although they can of course also include non-functional tests. See also White-box test." (Lars Dittmann et al, "Automotive SPICE in Practice", 2008)

"A software testing methodology that looks at available inputs for an application and the expected outputs from each input." (Mike Harwood, "Internet Security: How to Defend Against Attackers on the Web" 2nd Ed., 2015)

"A test designed by someone who doesn’t know how the code works internally." (Rod Stephens, "Beginning Software Engineering", 2015)

"A testing technique that tests the functionality of the application under test without knowledge of the internal code structure, implementation details, and internal paths of the software." (Pooja Kaplesh & Severin K Y Pang, "Software Testing", Software Engineering for Agile Application Development, 2020)

"A method of software testing that examines the functionality of an application without peering into its internal structures or workings. This method of test can be applied to virtually every level of software testing: unit, integration, system and acceptance." (NIST SP 800-192)

"A test methodology that assumes no knowledge of the internal structure and implementation detail of the assessment object." (CNSSI 4009-2015)

[black-box test design technique:] "Procedure to derive and/or select test cases based on an analysis of the specification, either functional or non-functional, of a component or system without reference to its internal structure." (ISTQB)

"Testing, either functional or non-functional, without reference to the internal structure of the component or system." (ISTQB)

🌁Software Engineering: White-box Testing (Definitions)

"An implementation-based test, in contrast to a specification-based test" (Johannes Link & Peter Fröhlich, "Unit Testing in Java", 2003)

"This test is derived knowing the inner structure of the software and based on the program code, design, interface descriptions, and so on. White-box tests are also called» structure based tests." (Lars Dittmann et al, "Automotive SPICE in Practice", 2008)

[white box test design technique:] "Any technique used to derive and/or select test cases based on an analysis of the internal structure of the test object (see also structural test)." (Tilo Linz et al, "Software Testing Foundations" 4th Ed., 2014)

"A software testing methodology that examines the code of an application. This contrasts with black box testing, which focuses only on inputs and outputs of an application." (Mike Harwood, "Internet Security: How to Defend Against Attackers on the Web" 2nd Ed., 2015)

"A test designed by someone who knows how the code works internally. That person can guess where problems may lie and create tests specifically to look for those problems." (Rod Stephens, "Beginning Software Engineering", 2015)

"The testing method where test cases are generated in order to test a program at a source code level." Pedro Delgado-Pérez et al, "Mutation Testing", 2015)

"A testing technique to test the internal structure, design and coding of a software solution." (Pooja Kaplesh & Severin K Y Pang, "Software Testing, Software Engineering for Agile Application Development", 2020)

"A test methodology that assumes explicit and substantial knowledge of the internal structure and implementation detail of the assessment object." (NIST SP 800-137)

[white-box test design technique:] "Procedure to derive and select test cases based on an analysis of the internal structure of a component or system." (ISTQB)

"Testing based on an analysis of the internal structure of the component or system." (ISTQB)