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  Cognitive computing

  Cognitive computing refers to technology platforms that, broadly speaking, are based on the scientific disciplines of artificial intelligence and signal processing. These platforms encompass machine learning, reasoning, natural language processing, speech recognition and vision (object recognition), human–computer interaction, dialog and narrative generation, among other technologies. == Definition == At present, there is no widely agreed upon definition for cognitive computing in either academia or industry. In general, the term cognitive computing has been used to refer to new hardware and/or software that mimics the functioning of the human brain (2004). In this sense, cognitive computing is a new type of computing with the goal of more accurate models of how the human brain/mind senses, reasons, and responds to stimulus. Cognitive computing applications link data analysis and adaptive page displays (AUI) to adjust content for a particular type of audience. As such, cognitive computing hardware and applications strive to be more affective and more influential by design. The term "cognitive system" also applies to any artificial construct able to perform a cognitive process where a cognitive process is the transformation of data, information, knowledge, or wisdom to a new level in the DIKW Pyramid. While many cognitive systems employ techniques having their origination in artificial intelligence research, cognitive systems, themselves, may not be artificially intelligent. For example, a neural network trained to recognize cancer on an MRI scan may achieve a higher success rate than a human doctor. This system is certainly a cognitive system but is not artificially intelligent. Cognitive systems may be engineered to feed on dynamic data in real-time, or near real-time, and may draw on multiple sources of information, including both structured and unstructured digital information, as well as sensory inputs (visual, gestural, auditory, or sensor-provided). == Cognitive analytics == Cognitive computing-branded technology platforms typically specialize in the processing and analysis of large, unstructured datasets. == Applications == Education Even if cognitive computing can not take the place of teachers, it can still be a heavy driving force in the education of students. Cognitive computing being used in the classroom is applied by essentially having an assistant that is personalized for each individual student. This cognitive assistant can relieve the stress that teachers face while teaching students, while also enhancing the student's learning experience over all. Teachers may not be able to pay each and every student individual attention, this being the place that cognitive computers fill the gap. Some students may need a little more help with a particular subject. For many students, Human interaction between student and teacher can cause anxiety and can be uncomfortable. With the help of Cognitive Computer tutors, students will not have to face their uneasiness and can gain the confidence to learn and do well in the classroom. While a student is in class with their personalized assistant, this assistant can develop various techniques, like creating lesson plans, to tailor and aid the student and their needs. Healthcare Numerous tech companies are in the process of developing technology that involves cognitive computing that can be used in the medical field. The ability to classify and identify is one of the main goals of these cognitive devices. This trait can be very helpful in the study of identifying carcinogens. This cognitive system that can detect would be able to assist the examiner in interpreting countless numbers of documents in a lesser amount of time than if they did not use Cognitive Computer technology. This technology can also evaluate information about the patient, looking through every medical record in depth, searching for indications that can be the source of their problems. Commerce Together with Artificial Intelligence, it has been used in warehouse management systems to collect, store, organize and analyze all related supplier data. All these aims at improving efficiency, enabling faster decision-making, monitoring inventory and fraud detection Human Cognitive Augmentation In situations where humans are using or working collaboratively with cognitive systems, called a human/cog ensemble, results achieved by the ensemble are superior to results obtainable by the human working alone. Therefore, the human is cognitively augmented. In cases where the human/cog ensemble achieves results at, or superior to, the level of a human expert then the ensemble has achieved synthetic expertise. In a human/cog ensemble, the "cog" is a cognitive system employing virtually any kind of cognitive computing technology. Other use cases Speech recognition Sentiment analysis Face detection Risk assessment Fraud detection Behavioral recommendations == Industry work == Cognitive computing in conjunction with big data and algorithms that comprehend customer needs, can be a major advantage in economic decision making. The powers of cognitive computing and artificial intelligence hold the potential to affect almost every task that humans are capable of performing. This can negatively affect employment for humans, as there would be no such need for human labor anymore. It would also increase the inequality of wealth; the people at the head of the cognitive computing industry would grow significantly richer, while workers without ongoing, reliable employment would become less well off. The more industries start to use cognitive computing, the more difficult it will be for humans to compete. Increased use of the technology will also increase the amount of work that AI-driven robots and machines can perform. The influence of competitive individuals in conjunction with artificial intelligence/cognitive computing has the potential to change the course of humankind.

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  Algorithms and Combinatorics

  Algorithms and Combinatorics (ISSN 0937-5511) is a book series in mathematics, and particularly in combinatorics and the design and analysis of algorithms. It is published by Springer Science+Business Media, and was founded in 1987. == Books == The books published in this series include: The Simplex Method: A Probabilistic Analysis (Karl Heinz Borgwardt, 1987, vol. 1) Geometric Algorithms and Combinatorial Optimization (Martin Grötschel, László Lovász, and Alexander Schrijver, 1988, vol. 2; 2nd ed., 1993) Systems Analysis by Graphs and Matroids (Kazuo Murota, 1987, vol. 3) Greedoids (Bernhard Korte, László Lovász, and Rainer Schrader, 1991, vol. 4) Mathematics of Ramsey Theory (Jaroslav Nešetřil and Vojtěch Rödl, eds., 1990, vol. 5) Matroid Theory and its Applications in Electric Network Theory and in Statics (Andras Recszki, 1989, vol. 6) Irregularities of Partitions: Papers from the meeting held in Fertőd, July 7–11, 1986 (Gábor Halász and Vera T. Sós, eds., 1989, vol. 8) Paths, Flows, and VLSI-Layout: Papers from the meeting held at the University of Bonn, Bonn, June 20–July 1, 1988 (Bernhard Korte, László Lovász, Hans Jürgen Prömel, and Alexander Schrijver, eds., 1990, vol. 9) New Trends in Discrete and Computational Geometry (János Pach, ed., 1993, vol. 10) Discrete Images, Objects, and Functions in Z n {\displaystyle \mathbb {Z} ^{n}} (Klaus Voss, 1993, vol. 11) Linear Optimization and Extensions (Manfred Padberg, 1999, vol. 12) The Mathematics of Paul Erdős I (Ronald Graham and Jaroslav Nešetřil, eds., 1997, vol. 13) The Mathematics of Paul Erdős II (Ronald Graham and Jaroslav Nešetřil, eds., 1997, vol. 14) Geometry of Cuts and Metrics (Michel Deza and Monique Laurent, 1997, vol. 15) Probabilistic Methods for Algorithmic Discrete Mathematics (M. Habib, C. McDiarmid, J. Ramirez-Alfonsin, and B. Reed, 1998, vol. 16) Modern Cryptography, Probabilistic Proofs and Pseudorandomness (Oded Goldreich, 1999, vol. 17) Geometric Discrepancy: An Illustrated Guide (Jiří Matoušek, 1999, vol. 18) Applied Finite Group Actions (Adalbert Kerber, 1999, vol. 19) Matrices and Matroids for Systems Analysis (Kazuo Murota, 2000, vol. 20; corrected ed., 2010) Combinatorial Optimization (Bernhard Korte and Jens Vygen, 2000, vol. 21; 5th ed., 2012) The Strange Logic of Random Graphs (Joel Spencer, 2001, vol. 22) Graph Colouring and the Probabilistic Method (Michael Molloy and Bruce Reed, 2002, Vol. 23) Combinatorial Optimization: Polyhedra and Efficiency (Alexander Schrijver, 2003, vol. 24. In three volumes: A. Paths, flows, matchings; B. Matroids, trees, stable sets; C. Disjoint paths, hypergraphs) Discrete and Computational Geometry: The Goodman-Pollack Festschrift (B. Aronov, S. Basu, J. Pach, and M. Sharir, eds., 2003, vol. 25) Topics in Discrete Mathematics: Dedicated to Jarik Nešetril on the Occasion of his 60th birthday (M. Klazar, J. Kratochvíl, M. Loebl, J. Matoušek, R. Thomas, and P. Valtr, eds., 2006, vol. 26) Boolean Function Complexity: Advances and Frontiers (Stasys Jukna, 2012, Vol. 27) Sparsity: Graphs, Structures, and Algorithms (Jaroslav Nešetřil and Patrice Ossona de Mendez, 2012, vol. 28) Optimal Interconnection Trees in the Plane (Marcus Brazil and Martin Zachariasen, 2015, vol. 29) Combinatorics and Complexity of Partition Functions (Alexander Barvinok, 2016, vol. 30)

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  EJB QL

  EJB QL or EJB-QL is a portable database query language for Enterprise Java Beans. It was used in Java EE applications. Compared to SQL, however, it is less complex but less powerful as well. == History == The language has been inspired, especially EJB3-QL, by the native Hibernate Query Language. In EJB3 It has been mostly replaced by the Java Persistence Query Language. == Differences == EJB QL is a database query language similar to SQL. The used queries are somewhat different from relational SQL, as it uses a so-called "abstract schema" of the enterprise beans instead of the relational model. In other words, EJB QL queries do not use tables and their components, but enterprise beans, their persistent state, and their relationships. The result of an SQL query is a set of rows with a fixed number of columns. The result of an EJB QL query is either a single object, a collection of entity objects of a given type, or a collection of values retrieved from CMP fields. One has to understand the data model of enterprise beans in order to write effective queries.

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  Kinodynamic planning

  In robotics and motion planning, kinodynamic planning is a class of problems for which velocity, acceleration, and force/torque bounds must be satisfied, together with kinematic constraints such as avoiding obstacles. The term was coined by Bruce Donald, Pat Xavier, John Canny, and John Reif. Donald et al. developed the first polynomial-time approximation schemes (PTAS) for the problem. By providing a provably polynomial-time ε-approximation algorithm, they resolved a long-standing open problem in optimal control. Their first paper considered time-optimal control ("fastest path") of a point mass under Newtonian dynamics, amidst polygonal (2D) or polyhedral (3D) obstacles, subject to state bounds on position, velocity, and acceleration. Later they extended the technique to many other cases, for example, to 3D open-chain kinematic robots under full Lagrangian dynamics. == Modern approaches == Since the foundational theoretical work of the 1990s, the field has evolved significantly with new algorithmic approaches that address the computational and practical limitations of early methods. === Sampling-based methods === Many practical heuristic algorithms based on stochastic optimization and iterative sampling have been developed by a wide range of authors to address the kinodynamic planning problem. Popular approaches include extensions of RRT algorithms such as RRT for kinodynamic systems, and sampling-based methods like Model Predictive Path Integral (MPPI) control. These stochastic techniques have been shown to work well in practice and can handle complex, high-dimensional state spaces more efficiently than deterministic methods. However, all motion planning methods are subject to the PSPACE-hardnesss of classical motion planning even without dynamics, which means (assuming the usual structural complexity conjectures) they all can be worst-case exponential-time in the state-space dimension (the number of degrees of freedom). On the other hand, the deterministic methods have provable guarantees of completeness, accuracy, and complexity (for fixed dimension, they are polynomial-time not only in the geometric complexity, but also in ( 1 / ε ) {\displaystyle (1/\varepsilon )} , the closeness of the desired approximation), whereas most of the recent heuristic/stochastic methods sacrifice at least one of these criteria. === Mixed-integer optimization approaches === Recent advances in mixed-integer programming have enabled new deterministic approaches to kinodynamic planning. These methods formulate the planning problem as an optimization task that simultaneously determines the spatial path and control sequence while respecting all kinodynamic constraints. By using techniques such as McCormick envelopes to handle bilinear constraints, these approaches can provide globally optimal solutions with mathematical guarantees while achieving significant computational speedups over traditional methods. === Genetic algorithm approaches === Genetic algorithms have also been adapted for kinodynamic planning, particularly for gradient-free optimization in challenging terrain. These methods use evolutionary computation to optimize trajectories over receding horizons, with specialized mutation operators that ensure vehicle controls remain within operational limits. This approach is particularly useful when dealing with non-differentiable cost functions or when gradient information is unavailable or unreliable. === Three-dimensional terrain planning === The foundational theoretical work of the 1990s was extended to higher degrees of freedom, and even to n {\displaystyle n} -link, 3D open-chain kinematic robots under full Lagrangian dynamics. However, many of the subsequent heuristic techniques (typically employing stochastic optimization) were confined to planar environments. More recent kinodynamic planning has extended beyond these planar environments to handle complex 3D terrains represented as simplicial complexes or triangular meshes. This advancement is particularly important for applications such as autonomous vehicle navigation in off-road environments, where elevation changes and terrain geometry significantly impact vehicle dynamics. These methods must account for pitch angles, surface curvature, and the coupling between terrain geometry and vehicle kinodynamic constraints. == Performance and guarantees == The landscape of performance guarantees in kinodynamic planning has evolved considerably. While early heuristic methods could not guarantee optimality, recent mixed-integer approaches have demonstrated the ability to find globally optimal solutions with proven constraint satisfaction. Experimental comparisons have shown that modern optimization-based planners can achieve execution times several orders of magnitude faster than sampling-based methods while maintaining strict adherence to kinodynamic constraints. However, the choice of method often depends on the specific application requirements. Sampling-based methods remain valuable for their ability to quickly find feasible solutions in high-dimensional spaces and their robustness to modeling uncertainties. Optimization-based methods excel when optimality guarantees and constraint compliance are critical, particularly in safety-critical applications. == Applications == Kinodynamic planning finds applications across numerous domains including: Autonomous vehicles: Path planning for cars, trucks, and other ground vehicles that must respect acceleration, steering, and velocity limits Aerial robotics: Trajectory planning for quadrotors and other unmanned aerial vehicles with dynamic constraints Manipulation: Planning for robotic arms where joint velocities, accelerations, and torques are limited Legged locomotion: Footstep and trajectory planning for walking and running robots Space robotics: Planning under thrust and fuel constraints for spacecraft and rovers

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  PCVC Speech Dataset

  The PCVC (Persian Consonant Vowel Combination) Speech Dataset is a Modern Persian speech corpus for speech recognition and also speaker recognition. The dataset contains sound samples of Modern Persian combination of vowel and consonant phonemes from different speakers. Every sound sample contains just one consonant and one vowel So it is somehow labeled in phoneme level. This dataset consists of 23 Persian consonants and 6 vowels. The sound samples are all possible combinations of vowels and consonants (138 samples for each speaker). The sample rate of all speech samples is 48000 which means there are 48000 sound samples in every 1 second. Every sound sample starts with consonant then continues with vowel. In each sample, in average, 0.5 second of each sample is speech and the rest is silence. Each sound sample ends with silence. All of sound samples are denoised with "Adaptive noise reduction" algorithm. Compared to Farsdat speech dataset and Persian speech corpus it is more easy to use because it is prepared in .mat data files. Also it is more based on phoneme based separation and all samples are denoised. == Contents == The corpus is downloadable from its Kaggle web page, and contains the following: .mat data files of sound samples in a 23630000 matrix, in which 23 is number of consonants, 6 is the number of vowels and 30000 is the length of sound sample.

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  Social information architecture

  Social information architecture, also known as social iA, is a sub-domain of information architecture which deals with the social aspects of conceptualizing, modeling and organizing information. It has become more relevant because of the rise of social media and Web 2.0 in recent times. == Approach == There are different approaches to the explanation of social information architecture. === Architecture model (internal space) === Architects designing a physical community space, have to consider how the architecture will shape social interactions. A long hallway of offices creates an utterly different dynamic than desks with arranged in an open space. One might foster individuality, privacy, propriety; the other: collaboration, distraction, communalism. Still, physical spaces can be flexibly repurposed and worked around if the inhabitants desire a social dynamic not instantly afforded by the space. Office doors can be left open to invite easier interaction. Partitions can be raised between adjacent desks to limit distraction and increase privacy. That's physical architecture. The information architectures of online communities are far more deterministic and far less flexible. They literally define the social architecture by pre-specifying in immutable computer code what information you have access to, who you can talk to, where you can go. In the online world, information architecture = social architecture. === Social dialogue and information model (external space) === All major brands use information architecture to market their products online, it is then commonly wrapped under the umbrella phrase 'digital strategy'. Information architecture used for strategic purposes encompasses brand SEO, strategic placement of virals, social media presence etc. Charities, news outlets and social dialogue forums can make a much more specific use of the same tools for positive and important social purposes. Social Information Architecture is perceived as the socially conscious wing of commercial information architecture and function to exchange information and ideas between people and groups. Social iA can pick up on conflicting issues that are treated with misunderstanding between cultures and leaves individuals and societies vulnerable to exploitation and manipulation. Since the net has such a far reach it is obvious to use it for meaningful and coordinated social dialogue. Example of such issues are faith, environment, politics, climate change, war, injustice and other social challenges. Information architecture can help create frameworks in which sharing information brings people together, inspires and encourages them to participate in a forward thinking and unfragmented way. One of its core activities is to spread messages that bring people from opposite sites of social and cultural spectrums together and to confront uncomfortable subject head on. == How does social information architecture work? == Social iA utilizes a variety of Web2.0 applications to filter relevant or valuable information and weave them in appropriate information repository or provide feedback to interesting channels. Social iA makes strategic use of Search Engines, Social Media, Google Algorithms, as well as websites, video & news channels. It ‘reads’ or 'listens' to social conversations and search engine queries and engages with the net actively to gather clues about the world's pulse on the internet. It assesses data, social & political trends, and respond with targeted campaigns to give people ideas, as well as help people with making sense of information. == Principals == Dan Brown in his paper 8 Principals of Social Information Architecture enlists the following principals: 1. The principle of objects: Treat content as a living, breathing thing, with a lifecycle, behaviors and attributes. 2. The principle of choices: Create pages that offer meaningful choices to users, keeping the range of choices available focused on a particular task. 3. The principle of disclosure: Show only enough information to help people understand what kinds of information they'll find as they dig deeper. 4. The principle of exemplars: Describe the contents of categories by showing examples of the contents. 5. The principle of front doors: Assume at least half of the website's visitors will come through some page other than the home page. 6. The principle of multiple classification: Offer users several different classification schemes to browse the site's content. 7. The principle of focused navigation: Don't mix apples and oranges in your navigation scheme. 8. The principle of growth: Assume the content you have today is a small fraction of the content you will have tomorrow. == What can social information architecture achieve? == Social information architecture has many potentials in terms of fostering social connections and how information is shared in social spaces on the web.

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  Xulvi-Brunet–Sokolov algorithm

  Xulvi-Brunet and Sokolov's algorithm generates networks with chosen degree correlations. This method is based on link rewiring, in which the desired degree is governed by parameter ρ. By varying this single parameter it is possible to generate networks from random (when ρ = 0) to perfectly assortative or disassortative (when ρ = 1). This algorithm allows to keep network's degree distribution unchanged when changing the value of ρ. == Assortative model == In assortative networks, well-connected nodes are likely to be connected to other highly connected nodes. Social networks are examples of assortative networks. This means that an assortative network has the property that almost all nodes with the same degree are linked only between themselves. The Xulvi-Brunet–Sokolov algorithm for this type of networks is the following. In a given network, two links connecting four different nodes are chosen randomly. These nodes are ordered by their degrees. Then, with probability ρ, the links are randomly rewired in such a way that one link connects the two nodes with the smaller degrees and the other connects the two nodes with the larger degrees. If one or both of these links already existed in the network, the step is discarded and is repeated again. Thus, there will be no self-connected nodes or multiple links connecting the same two nodes. Different degrees of assortativity of a network can be achieved by changing the parameter ρ. Assortative networks are characterized by highly connected groups of nodes with similar degree. As assortativity grows, the average path length and clustering coefficient increase. == Disassortative model == In disassortative networks, highly connected nodes tend to connect to less-well-connected nodes with larger probability than in uncorrelated networks. Examples of such networks include biological networks. The Xulvi-Brunet and Sokolov's algorithm for this type of networks is similar to the one for assortative networks with one minor change. As before, two links of four nodes are randomly chosen and the nodes are ordered with respect to their degrees. However, in this case, the links are rewired (with probability p) such that one link connects the highest connected node with the node with the lowest degree and the other link connects the two remaining nodes randomly with probability 1 − ρ. Similarly, if the new links already existed, the previous step is repeated. This algorithm does not change the degree of nodes and thus the degree distribution of the network.

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  Skyline operator

  The skyline operator is the subject of an optimization problem and computes the Pareto optimum on tuples with multiple dimensions. This operator is an extension to SQL proposed by Börzsönyi et al. to filter results from a database to keep only those objects that are not dominated by any other point on all dimensions. The name skyline comes from the view on Manhattan from the Hudson River, where those buildings can be seen that are not hidden by any other. A building is visible if it is not dominated by a building that is taller or closer to the river (two dimensions, distance to the river minimized, height maximized). Another application of the skyline operator involves selecting a hotel for a holiday. The user wants the hotel to be both cheap and close to the beach. However, hotels that are close to the beach may also be expensive. In this case, the skyline operator would only present those hotels that are not worse than any other hotel in both price and distance to the beach. == Formal specification == The skyline operator returns tuples that are not dominated by any other tuple. A tuple dominates another if it is at least as good in all dimensions and better in at least one dimension. Formally, we can think of each tuple as a vector p , q ∈ R n {\displaystyle p,q\in \mathbb {R} ^{n}} . p {\displaystyle p} dominates q {\displaystyle q} (written: p ≻ q {\displaystyle p\succ q} ) if p {\displaystyle p} is at least as good as q {\displaystyle q} in every dimension, and superior in at least one: p ≻ q ⇔ ∀ i ∈ [ n ] . p [ i ] ⪰ q [ i ] ∧ ∃ j ∈ [ n ] . p [ j ] ≻ q [ j ] . {\displaystyle p\succ q\Leftrightarrow \forall i\in [n].p[i]\succeq q[i]\wedge \exists j\in [n].p[j]\succ q[j].} Dominance ( p ≻ q {\displaystyle p\succ q} ) can be defined as any strict partial ordering, for example greater (with ≻:=> {\displaystyle \succ :=>} and ⪰:=≥ {\displaystyle \succeq :=\geq } ) or less (with ≻:=< {\displaystyle \succ :=<} and ⪰:=≤ {\displaystyle \succeq :=\leq } ). Assuming two dimensions and defining dominance in both dimensions as greater, we can compute the skyline in SQL-92 as follows: == Proposed syntax == As an extension to SQL, Börzsönyi et al. proposed the following syntax for the skyline operator: where d1, ... dm denote the dimensions of the skyline and MIN, MAX and DIFF specify whether the value in that dimension should be minimised, maximised or simply be different. Without an SQL extension, the SQL query requires an antijoin with not exists: == Implementation == The skyline operator can be implemented directly in SQL using current SQL constructs, but this has been shown to be very slow in disk-based database systems. Other algorithms have been proposed that make use of divide and conquer, indices, MapReduce and general-purpose computing on graphics cards. Skyline queries on data streams (i.e. continuous skyline queries) have been studied in the context of parallel query processing on multicores, owing to their wide diffusion in real-time decision making problems and data streaming analytics. Exasol features a native implementation.

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  Connectionist expert system

  Connectionist expert systems are artificial neural network (ANN) based expert systems where the ANN generates inferencing rules e.g., fuzzy-multi layer perceptron where linguistic and natural form of inputs are used. Apart from that, rough set theory may be used for encoding knowledge in the weights better and also genetic algorithms may be used to optimize the search solutions better. Symbolic reasoning methods may also be incorporated (see hybrid intelligent system). (Also see expert system, neural network, clinical decision support system.)

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  Geospatial metadata

  Geospatial metadata (also geographic metadata) is a type of metadata applicable to geographic data and information. Such objects may be stored in a geographic information system (GIS) or may simply be documents, data-sets, images or other objects, services, or related items that exist in some other native environment but whose features may be appropriate to describe in a (geographic) metadata catalog (may also be known as a data directory or data inventory). == Definition == ISO 19115:2013 "Geographic Information – Metadata" from ISO/TC 211, the industry standard for geospatial metadata, describes its scope as follows: [This standard] provides information about the identification, the extent, the quality, the spatial and temporal aspects, the content, the spatial reference, the portrayal, distribution, and other properties of digital geographic data and services. ISO 19115:2013 also provides for non-digital mediums: Though this part of ISO 19115 is applicable to digital data and services, its principles can be extended to many other types of resources such as maps, charts, and textual documents as well as non-geographic data. The U.S. Federal Geographic Data Committee (FGDC) describes geospatial metadata as follows: A metadata record is a file of information, usually presented as an XML document, which captures the basic characteristics of a data or information resource. It represents the who, what, when, where, why and how of the resource. Geospatial metadata commonly document geographic digital data such as Geographic Information System (GIS) files, geospatial databases, and earth imagery but can also be used to document geospatial resources including data catalogs, mapping applications, data models and related websites. Metadata records include core library catalog elements such as Title, Abstract, and Publication Data; geographic elements such as Geographic Extent and Projection Information; and database elements such as Attribute Label Definitions and Attribute Domain Values. == History == The growing appreciation of the value of geospatial metadata through the 1980s and 1990s led to the development of a number of initiatives to collect metadata according to a variety of formats either within agencies, communities of practice, or countries/groups of countries. For example, NASA's "DIF" metadata format was developed during an Earth Science and Applications Data Systems Workshop in 1987, and formally approved for adoption in 1988. Similarly, the U.S. FGDC developed its geospatial metadata standard over the period 1992–1994. The Spatial Information Council of Australia and New Zealand (ANZLIC), a combined body representing spatial data interests in Australia and New Zealand, released version 1 of its "metadata guidelines" in 1996. ISO/TC 211 undertook the task of harmonizing the range of formal and de facto standards over the approximate period 1999–2002, resulting in the release of ISO 19115 "Geographic Information – Metadata" in 2003 and a subsequent revision in 2013. As of 2011 individual countries, communities of practice, agencies, etc. have started re-casting their previously used metadata standards as "profiles" or recommended subsets of ISO 19115, occasionally with the inclusion of additional metadata elements as formal extensions to the ISO standard. The growth in popularity of Internet technologies and data formats, such as Extensible Markup Language (XML) during the 1990s led to the development of mechanisms for exchanging geographic metadata on the web. In 2004, the Open Geospatial Consortium released the current version (3.1) of Geography Markup Language (GML), an XML grammar for expressing geospatial features and corresponding metadata. With the growth of the Semantic Web in the 2000s, the geospatial community has begun to develop ontologies for representing semantic geospatial metadata. Some examples include the Hydrology and Administrative ontologies developed by the Ordnance Survey in the United Kingdom. == ISO 19115: Geographic information – Metadata == ISO 19115 is a standard of the International Organization for Standardization (ISO). The standard is part of the ISO geographic information suite of standards (19100 series). ISO 19115 and its parts define how to describe geographical information and associated services, including contents, spatial-temporal purchases, data quality, access and rights to use. The objective of this International Standard is to provide a clear procedure for the description of digital geographic data-sets so that users will be able to determine whether the data in a holding will be of use to them and how to access the data. By establishing a common set of metadata terminology, definitions and extension procedures, this standard promotes the proper use and effective retrieval of geographic data. ISO 19115 was revised in 2013 to accommodate growing use of the internet for metadata management, as well as add many new categories of metadata elements (referred to as codelists) and the ability to limit the extent of metadata use temporally or by user. == ISO 19139 Geographic information Metadata XML schema implementation == ISO 19139:2012 provides the XML implementation schema for ISO 19115 specifying the metadata record format and may be used to describe, validate, and exchange geospatial metadata prepared in XML. The standard is part of the ISO geographic information suite of standards (19100 series), and provides a spatial metadata XML (spatial metadata eXtensible Mark-up Language (smXML)) encoding, an XML schema implementation derived from ISO 19115, Geographic information – Metadata. The metadata includes information about the identification, constraint, extent, quality, spatial and temporal reference, distribution, lineage, and maintenance of the digital geographic data-set. == Metadata directories == Also known as metadata catalogues or data directories. (need discussion of, and subsections on GCMD, FGDC metadata gateway, ASDD, European and Canadian initiatives, etc. etc.) GIS Inventory – National GIS Inventory System which is maintained by the US-based National States Geographic Information Council (NSGIC) as a tool for the entire US GIS Community. Its primary purpose is to track data availability and the status of geographic information system (GIS) implementation in state and local governments to aid the planning and building of statewide spatial data infrastructures (SSDI). The Random Access Metadata for Online Nationwide Assessment (RAMONA) database is a critical component of the GIS Inventory. RAMONA moves its FGDC-compliant metadata (CSDGM Standard) for each data layer to a web folder and a Catalog Service for the Web (CSW) that can be harvested by Federal programs and others. This provides far greater opportunities for discovery of user information. The GIS Inventory website was originally created in 2006 by NSGIC under award NA04NOS4730011 from the Coastal Services Center, National Oceanic and Atmospheric Administration, U.S. Department of Commerce. The Department of Homeland Security has been the principal funding source since 2008 and they supported the development of the Version 5 during 2011/2012 under Order Number HSHQDC-11-P-00177. The Federal Emergency Management Agency and National Oceanic and Atmospheric Administration have provided additional resources to maintain and improve the GIS Inventory. Some US Federal programs require submission of CSDGM-Compliant Metadata for data created under grants and contracts that they issue. The GIS Inventory provides a very simple interface to create the required Metadata. GCMD - Global Change Master Directory's goal is to enable users to locate and obtain access to Earth science data sets and services relevant to global change and Earth science research. The GCMD database holds more than 20,000 descriptions of Earth science data sets and services covering all aspects of Earth and environmental sciences. ECHO - The EOS Clearing House (ECHO) is a spatial and temporal metadata registry, service registry, and order broker. It allows users to more efficiently search and access data and services through the Reverb Client or Application Programmer Interfaces (APIs). ECHO stores metadata from a variety of science disciplines and domains, totalling over 3400 Earth science data sets and over 118 million granule records. GoGeo - GoGeo is a service run by EDINA (University of Edinburgh) and is supported by Jisc. GoGeo allows users to conduct geographically targeted searches to discover geospatial datasets. GoGeo searches many data portals from the HE and FE community and beyond. GoGeo also allows users to create standards compliant metadata through its Geodoc metadata editor. == Geospatial metadata tools == There are many proprietary GIS or geospatial products that support metadata viewing and editing on GIS resources. For example, ESRI's ArcGIS Desktop, SOCET GXP, Autodesk's AutoCAD Map 3D 2008, Arcitecta's Mediaflux and Intergraph's Geo

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  BioCreative

  BioCreAtIvE (A critical assessment of text mining methods in molecular biology) consists in a community-wide effort for evaluating information extraction and text mining developments in the biological domain. It was preceded by the Knowledge Discovery and Data Mining (KDD) Challenge Cup for detection of gene mentions. == Community Challenges == === First edition (2004-2005) === Three main tasks were posed at the first BioCreAtIvE challenge: the entity extraction task, the gene name normalization task, and the functional annotation of gene products task. The data sets produced by this contest serve as a Gold Standard training and test set to evaluate and train Bio-NER tools and annotation extraction tools. === Second edition (2006-2007) === The second BioCreAtIvE challenge (2006-2007) had also 3 tasks: detection of gene mentions, extraction of unique idenfiers for genes and extraction information related to physical protein-protein interactions. It counted with participation of 44 teams from 13 countries. === Third edition (2011-2012) === The third edition of BioCreative included for the first time the InterActive Task (IAT), designed to evaluate the practical usability of text mining tools in real-world biocuration tasks. === Fifth edition (2016) === BioCreative V had 5 different tracks, including an interactive task (IAT) for usability of text mining systems and a track using the BioC format for curating information for BioGRID.

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  Spatial computing

  Spatial computing refers to 3D human–computer interaction techniques that are perceived by users as taking place in the real world, in and around their bodies and physical environments, instead of constrained to and perceptually behind computer screens or in purely virtual worlds. This concept inverts the long-standing practice of teaching people to interact with computers in digital environments, and instead teaches computers to better understand and interact with people more naturally in the human world. This concept overlaps with and encompasses others including extended reality, augmented reality, mixed reality, natural user interface, contextual computing, affective computing, and ubiquitous computing. The usage for labeling and discussing these adjacent technologies is imprecise. Spatial computing devices include sensors—such as RGB cameras, depth cameras, 3D trackers, inertial measurement units, or other tools—to sense and track nearby human bodies (including hands, arms, eyes, legs, mouths) during ordinary interactions with people and computers in a 3D space. They further use computer vision to attempt to understand real world scenes, such as rooms, streets or stores, to read labels, to recognize objects, create 3D maps, and more. Quite often they also use extended reality and mixed reality to superimpose virtual 3D graphics and virtual 3D audio onto the human visual and auditory system as a way of providing information more naturally and contextually than traditional 2D screens. Spatial computing often refers to personal computing devices like headsets and headphones, but other human-computer interactions that leverage real-time spatial positioning for displays, like projection mapping or cave automatic virtual environment displays, can also be considered spatial computing if they leverage human-computer input for the participants. == History == The term "spatial computing" apparently originated in the field of GIS around 1985 or earlier to describe computations on large-scale geospatial information. Early examples of spatial computing in GIS include ArcInfo and its iterations, initially released in 1981, a part of ArcGIS along with ArcEditor, which together provide mapping, analysis, editing, and geoprocessing for geodatabases. This is somewhat related to the modern use, but on the scale of continents, cities, and neighborhoods. Modern spatial computing is more centered on the human scale of interaction, around the size of a living room or smaller. But it is not limited to that scale in the aggregate. In the early 1990s, as field of virtual reality was beginning to be commercialized beyond academic and military labs, a startup called Worldesign in Seattle used the term Spatial Computing to describe the interaction between individual people and 3D spaces, operating more at the human end of the scale than previous GIS examples may have contemplated. The company built a CAVE-like environment it called the Virtual Environment Theater, whose 3D experience was of a virtual flyover of the Giza Plateau, circa 3000 BC. Robert Jacobson, CEO of Worldesign, attributes the origins of the term to experiments at the Human Interface Technology Lab, at the University of Washington, under the direction of Thomas A. Furness III. Jacobson was a co-founder of that lab before spinning off this early VR startup. In 1997, an academic publication by T. Caelli, Peng Lam, and H. Bunke called "Spatial Computing: Issues in Vision, Multimedia and Visualization Technologies" introduced the term more broadly for academic audiences, focusing on a variety of topics such as image processing, dead reckoning navigation, object recognition, and visualizing spatial data. The specific term "spatial computing" was later referenced again in 2003 by Simon Greenwold, as "human interaction with a machine in which the machine retains and manipulates referents to real objects and spaces". MIT Media Lab alumnus John Underkoffler gave a TED talk in 2010 giving a live demo of the multi-screen, multi-user spatial computing systems being developed by Oblong Industries, which sought to bring to life the futuristic interfaces conceptualized by Underkoffler in the films Minority Report and Iron Man. Google Earth, initially released by Keyhole Inc. in 2001 and re-released by Google in 2005 can be considered a capable GIS and includes advanced geospatial tools and capabilities. == Notable instances of the use of spatial computing == In 2019, Microsoft HoloLens released a video outlining Airbus' partnership with Microsoft Azure to utilize the latter's mixed reality services for streamlining and improving the aircraft design process, as well as reducing the error in development. Airbus utilized the HoloLens 2 to this end, and the executive vice president of engineering claimed that their design process' validation phases were "hugely accelerated by 80 percent", as well as "strongly believe[d]" that up to 30% improvements in their industrial tasks could be attained with the HoloLens 2. During the presentational video, Airbus cited the maturity of Microsoft Azure services as "key" for their usage of the HoloLens 2. Also in 2019, the U.S. army partnered with Microsoft to produce a HoloLens based Integrated Visual Augmentation System (IVAS) to enhance infantry members by giving troops various abilities, including but not limited to using holographs to train, projecting 3D maps into their vision, and seeing through smoke and corners. Microsoft received tens of thousands of hours of feedback for their systems by 2021. Sergeant Marc Krugh at the time claimed that Microsoft's partnership has already caused the army to rethink some of its troops' operation strategy. == Products == === Apple Vision Pro === Apple announced Apple Vision Pro, a device it markets as a "spatial computer", on June 5, 2023. It includes several features such as Spatial Audio, two 4K micro-OLED displays, the Apple R1 chip and eye tracking, and released in the United States on February 2, 2024. In announcing the platform, Apple invoked its history of popularizing 2D graphical user interfaces that supplanted prior human-computer interface mechanisms such as the command line. Apple suggests the introduction of spatial computing as a new category of interactive device, on the same level of importance as the introduction of the 2D GUI. Apple Vision Pro runs on a new operating system called visionOS, which combines eye tracking, gesture recognition, and voice input to enable immersive interaction without physical controllers. The platform is aimed at productivity, entertainment, collaboration, and enterprise use cases. === Magic Leap === Magic Leap had also previously used the term “spatial computing” to describe its own devices. Its first headset, the Magic Leap 1, was released on August 8, 2018. Magic Leap’s technology enables the display of content into the real world using an optical see-through head-mounted display, which projects an overlay of a virtual world into the user’s field of view. This allows for an experience where the physical and digital worlds are perceived simultaneously. === Microsoft Hololens === On February 24, 2019, Microsoft released the HoloLens 2, which includes mixed reality tools and can generate interactable, manipulatable holograms in 3D space. The holograms in question can be related to a physical object or completely independent and free-floating. The Azure Spatial Anchors cloud service was released simultaneously, which gives the holograms capability to persist across time and many individuals' devices. === Meta Quest === The Meta Quest 3, a mixed reality gaming headset that includes spatial audio, two color cameras, and grants the ability to interact with virtual characters released on October 9, 2023, at a notably cheaper price than the Apple Vision Pro, but with reduced capabilities. === Snap Spectacles === Spectacles (product) are augmented reality glasses developed by Snap Inc.. The latest generation includes a 46-degree stereoscopic display, adjustable tint, and Snapdragon processors. Spectacles allow users to interact with a collection of augmented reality experiences designed for education, entertainment, and utility. Currently, the device is in the hands of selected developers and creators, as part of an experimental AR ecosystem focused on creativity, use case exploration and expression.

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  Trustworthy computing

  The term trustworthy computing (TwC) has been applied to computing systems that are inherently secure, available, and reliable. It is particularly associated with the Microsoft initiative of the same name, launched in 2002. == History == Until 1995, there were restrictions on commercial traffic over the Internet. On, May 26, 1995, Bill Gates sent the "Internet Tidal Wave" memorandum to Microsoft executives assigning "...the Internet this highest level of importance..." but Microsoft's Windows 95 was released without a web browser as Microsoft had not yet developed one. The success of the web had caught them by surprise but by mid 1995, they were testing their own web server, and on August 24, 1995, launched a major online service, The Microsoft Network (MSN). The National Research Council recognized that the rise of the Internet simultaneously increased societal reliance on computer systems while increasing the vulnerability of such systems to failure and produced an important report in 1999, "Trust in Cyberspace". This report reviews the cost of un-trustworthy systems and identifies actions required for improvement. == Microsoft and Trustworthy Computing == Bill Gates launched Microsoft's "Trustworthy Computing" initiative with a January 15, 2002 memo, referencing an internal whitepaper by Microsoft CTO and Senior Vice President Craig Mundie. The move was reportedly prompted by the fact that they "...had been under fire from some of its larger customers–government agencies, financial companies and others–about the security problems in Windows, issues that were being brought front and center by a series of self-replicating worms and embarrassing attacks." such as Code Red, Nimda, Klez and Slammer. Four areas were identified as the initiative's key areas: Security, Privacy, Reliability, and Business Integrity, and despite some initial scepticism, at its 10-year anniversary it was generally accepted as having "...made a positive impact on the industry...". The Trustworthy Computing campaign was the main reason why Easter eggs disappeared from Windows, Office and other Microsoft products.

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  Nike+iPod

  The Nike+iPod Sport Kit is an activity tracker device, developed by Nike, Inc., which measures and records the distance and pace of a walk or run. The Nike+iPod consists of a small transmitter device attached to or embedded in a shoe, which communicates with either the Nike+ Sportband, or a receiver plugged into an iPod Nano. It can also work directly with a 2nd Generation iPod Touch (or higher), iPhone 3GS, iPhone 4, iPhone 4S, iPhone 5, The Nike+iPod was announced on May 23, 2006. On September 7, 2010, Nike released the Nike+ Running App (originally called Nike+ GPS) on the App Store, which used a tracking engine powered by MotionX that does not require the separate shoe sensor or pedometer. This application works using the accelerometer and GPS of the iPhone and the accelerometer of the iPod Touch, which does not have a GPS chip. Nike+Running is compatible with the iPhone 6 and iPhone 6 Plus down to iPhone 3GS and iPod touch. On June 21, 2012, Nike released Nike+ Running App for Android. The current app is compatible with all Android phones running 4.0.3 and up. == Overview == The sensor and iPod kit were revealed on May 20, 2006. The kit stores information such as the elapsed time of the workout, the distance traveled, pace, and calories burned by the individual. Nike+ was a collaboration between Nike and Apple; the platform consisted of an iPod, a wireless chip, Nike shoes that accepted the wireless chip, an iTunes membership, and a Nike+ online community. iPods using Nike iPod require a sensor and remote. The next upgraded product was the Sportband kit, which was announced in April 2008. The kit allows users to store run information without the iPod Nano. The Sportband consists of two parts: a rubber holding strap which is worn around the wrist, and a receiver which resembles a USB key-disk. The receiver displays information comparable to that of the iPod kit on the built-in display. After a run, the receiver can be plugged straight into a USB port and the software will upload the run information automatically to the Nike+ website. As of August 2008 "Nike+iPod for the Gym" launched, allowing users to record their cardio workouts directly to their iPods. No Sport kit or shoe sensor is required; all that is needed is a compatible iPod (1st–6th generation iPod Nano or 2nd/3rd gen iPod Touch) and an enabled piece of cardio equipment. As of March 2009, the seven largest commercial equipment providers were shipping enabled equipment (Life Fitness, Technogym, Precor USA, Star Trac, Cybex International, Matrix Fitness and Free Motion). The models of compatible cardio equipment include treadmills, stationary bicycles, stair climbers, ellipticals, and others such as Precor's Adaptive Motion Trainer. Once the user syncs an iPod with iTunes, the cardio workouts are automatically stored at Nikeplus.com, where each workout is visualized and tracked based on the number of calories burned. The calories are converted to "CardioMiles", at a ratio of 100:1, allowing cardio users to take full advantage of all the tools and features of Nikeplus.com, and allow them to engage in challenges with other runners, walkers and cardio users, using a common currency. With the release of the second-generation iPod Touch in 2008, Apple Inc. included a built-in ability to receive Nike+ signals, which allowed the iPod to connect directly to the wireless sensor thus eliminating the need for an external receiver to be connected. Apple also added this capability to the iPhone 3GS (released 2009), iPhone 4 (2010), and third-generation iPod Touch (2009). Those devices use their Broadcom Bluetooth chipset to receive the signals. On June 7, 2010, Polar and Nike introduced the Polar WearLink+ that works with Nike+. This new product works with the Nike+ SportBand and the fifth generation iPod nano in conjunction with the Nike+ iPod Sport Kit. Polar WearLink+ that works with Nike+ communicates directly with the fifth generation iPod nano and Nike+ SportBand using a proprietary digital protocol but it is dual-mode so it is also compatible with most Polar training computers (all those using 5 kHz analog transmission technology). Nike+ had 18 million global users as of April 2013. One year later, Nike updated the number of global users to 28 million. In iOS 6.1.2 (and possibly higher), a hole in the compatibility for the app has allowed jailbroken iPad users to use the native Nike + iPod iPhone and iPod app by moving the app bundle and setting permissions for the app. On April 30, 2018, Nike retired services for legacy Nike wearable devices, such as the Nike+ FuelBand and the Nike+ SportWatch GPS, and previous versions of apps, including Nike Run Club and Nike Training Club version 4.X and lower. Likewise, Nike no longer supported the Nike+ Connect software that transferred data to a NikePlus Profile or the Nike+ Fuel/FuelBand and Nike+ Move apps. == Sports kit equipment == The kit consists of two pieces: a piezoelectric sensor with a Nordic Semiconductor nRF2402 transmitter that is mounted under the inner sole of the shoe and a receiver that connects to the iPod. They communicate using a 2.4 GHz wireless radio and use Nordic Semiconductor's "ShockBurst" network protocol. The wireless data is encrypted in transit, but some uniquely identifying data is sent in the plain. The wireless protocol was reverse engineered and documented by Dmitry Grinberg in 2011. Nike recommends that the shoe be a Nike+ model with a special pocket in which to place the device. Nike has released the sensor for individual sale meaning that consumers no longer have to purchase the whole set (the iPod receiver and sensor). As the sensor battery cannot be replaced, a new one must be purchased every time the battery runs out. Aftermarket solutions are available to users who do not want to use shoes with built-in or hand-made pockets for the foot sensor, such as shoe pouches and containment devices designed to affix the sensor against the shoe laces. No matter how the sensor is integrated with the user's shoes, care must be taken that it is firmly fixed in place and will not jerk around while in use, which would degrade the accuracy. == Sports kit usage == The Sports Kit can be used to track running, which it refers to as "workouts". New workouts are started by plugging the receiving unit into the iPod, then navigating through the iPod menu system. The user chooses a goal for the workout, which might be to cover a specific distance, or burn a number of calories, or work out for a specified time. A workout can also be started without a goal, which is called a "Basic Workout". When the workout goal has been set, the receiver seeks the sensor, possibly asking the user to "walk around to activate [the] sensor". The user then must press the center button on the iPod to begin the workout. Audio feedback is provided in the user's choice of generic male or female voice by the iPod over the course of the workout, depending on the type of workout chosen. For goal-oriented workouts, the feedback will correspond to significant milestones toward the goal. In a distance workout, for example, the audio feedback will inform the user as each mile or kilometer has been completed, as well as the half-way point of the workout, and a countdown of four 100-meter increments at the end of the workout. The iPod's control wheel functions change slightly during a workout. The Pause button now not only pauses the music but also the workout. Similarly, the Menu button is used to access the controls to end the workout. The Forward and Back buttons are unchanged, performing audio track skip and reverse functions. The Center button has two functions: audio feedback about the current distance, time, and pace are provided when the button is tapped once, while if the button is held down the iPod skips to the "PowerSong" - an audio track chosen by the user, generally intended for motivation. In addition to the in-workout audio feedback, there are pre-recorded congratulations provided by Lance Armstrong, Tiger Woods, Joan Benoit Samuelson, and Paula Radcliffe whenever a user achieves a personal best (such as fastest mile, fastest 5K, fastest 10K, longest run yet) or reaches certain long-term milestones (such as 250 miles, 500 kilometers). This "celebrity feedback" is heard after the usual end-of-run statistics. While the Sports Kit can be used immediately after purchase, it will report more accurate results if it is calibrated before the first usage and then regularly afterwards. For calibration, the user finds a fixed known distance of at least 0.25 mile or 400 meters and then sets the Nike+ to calibration mode for the walk or run over that distance. When the walk or run is complete, the device calibrates itself and future workout reporting will reflect statistics closer to that individual user's workout style. Consumer Reports magazine tested the device and found it accurate as long as you keep an even pace. In workouts with varied pa

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  Virtual data room

  A virtual data room (sometimes called a VDR or Deal Room) is an online repository of information that is used for the storing and distribution of documents. In many cases, a virtual data room is used to facilitate the due diligence process during an M&A transaction, loan syndication, or private equity and venture capital transactions. This due diligence process has traditionally used a physical data room to accomplish the disclosure of documents. For reasons of cost, efficiency and security, virtual data rooms have widely replaced the more traditional physical data room. A virtual data room is an extranet to which the bidders and their advisers are given access via the internet. An extranet is essentially a website with limited controlled access, using a secure log-on supplied by the vendor, which can be disabled at any time, by the vendor, if a bidder withdraws. Much of the information released is confidential and restrictions are applied to the viewer's ability to release this to third parties (by means of forwarding, copying or printing). This can be effectively applied to protect the data using digital rights management. The virtual data room provides access to secure documents for authorized users through a dedicated web site, or through secure agent applications. In the process of mergers and acquisitions the data room is set up as part of the central repository of data relating to companies or divisions being acquired or sold. The data room enables the interested parties to view information relating to the business in a controlled environment where confidentiality can be preserved. Conventionally this was achieved by establishing a supervised, physical data room in secure premises with controlled access. In most cases, with a physical data room, only one bidder team can access the room at a time. A virtual data room is designed to have the same advantages as a conventional data room (controlling access, viewing, copying and printing, etc.) with fewer disadvantages. Due to their increased efficiency, many businesses and industries have moved to using virtual data rooms instead of physical data rooms. In 2006, a spokesperson for a company which sets up virtual deal rooms was reported claiming that the process reduced the bidding process by about thirty days compared to physical data rooms. In the process of startup fundraising, a virtual data room is set up to be a central location for key data, documents, and financials. These are shared with venture capital and angel investors and allows them to streamline due diligence. == Application == Any business dealing with private data can apply VDRs when secure transaction processing is required. This includes financial institutions that need to negotiate confidential customer information without involving third parties. VDRs have traditionally been used for IPOs and real estate asset management. Technology companies may use them to exchange and review code or confidential data needed for operations. The same is true for clients, who entrust their valuable code only to the most qualified people in the organisation. The code is not something that can be printed out and brought in a folder. It resides on a computer and must be used together. VDR can find application in any business that manages data in the form of documents, especially law firms, financial advisers or the B2B sector. The latter work with documents that must always be handled and controlled confidentially, and it is difficult to store them securely when they are on a server that other people can access. In addition, in B2B, it is important to close the deal as quickly as possible: the average sales cycle is one to three months. VDR can be compared to a locked filing cabinet where all those folders and documents are kept. It automates the mathematics of pricing to prevent revenue leakage, and initially integrates CRM to ensure accurate synchronisation of all account data, which is important for B2B in particular and sales in general. While virtual data rooms offer many advantages, they are not suitable for every industry. For example, some governments may decide to continue using physical data rooms for highly confidential information sharing. The damage from potential cyberattacks and data breaches exceeds the benefits offered by virtual data rooms. In such cases, the use of VDRs is not considered. Data breaches have particularly affected the US healthcare system from March 2021 to March 2022 - according to IBM Security the cost of the breach was a record high of $10.1 million.

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