AI Data Center Map

AI Data Center Map — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  TIMIT

  TIMIT is a corpus of phonemically and lexically transcribed speech of American English speakers of different sexes and dialects. Each transcribed element has been delineated in time. TIMIT was designed to further acoustic-phonetic knowledge and automatic speech recognition systems. It was commissioned by DARPA and corpus design was a joint effort between the Massachusetts Institute of Technology, SRI International, and Texas Instruments (TI). The speech was recorded at TI, transcribed at MIT, and verified and prepared for publishing by the National Institute of Standards and Technology (NIST). There is also a telephone bandwidth version called NTIMIT (Network TIMIT). TIMIT and NTIMIT are not freely available — either membership of the Linguistic Data Consortium, or a monetary payment, is required for access to the dataset. == Data == TIMIT contains ~5 hours of speech, of 10 sentences spoken by each of 630 speakers. The sentences were randomly sampled from a corpus of 2342 sentences. The speakers were native speakers of American English, classified under 8 major dialect regions: New England, Northern, North Midland, South Midland, Southern, New York City, Western, Army Brat (moved around). The speakers were 70% male and 30% female. Recordings were made in a noise-isolated recording booth at Texas Instrument, using a semi-automatic computer system (STEROIDS) to control the presentation of prompts to the speaker and the recording. Two-channel recordings were made using a Sennheiser HMD 414 headset-mounted microphone and a Brüel & Kjær 1/2" far-field pressure microphone (#4165). The speech was digitized at a sample rate of 20 kHz then and downsampled to 16 kHz. == History == The TIMIT telephone corpus was an early attempt to create a database with speech samples. It was published in the year 1988 on CD-ROM and consists of only 10 sentences per speaker. Two 'dialect' sentences were read by each speaker, as well as another 8 sentences selected from a larger set Each sentence averages 3 seconds long and is spoken by 630 different speakers. It was the first notable attempt in creating and distributing a speech corpus and the overall project has produced costs of 1.5 million US$. An update was released in October 1990. It included full 630-speaker corpus; checked and corrected transcriptions; word-alignment transcriptions; NIST SPHERE-headered waveform files and header manipulation software; phonemic dictionary; new test and training subsets balanced for dialectal and phonetic coverage; more extensive documentation. The full name of the project is DARPA-TIMIT Acoustic-Phonetic Continuous Speech Corpus and the acronym TIMIT stands for Texas Instruments/Massachusetts Institute of Technology. The main reason why a corpus of telephone speech was created was to train speech recognition software. In the Blizzard challenge, different software has the obligation to convert audio recordings into textual data and the TIMIT corpus was used as a standardized baseline.

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  Australian Geoscience Data Cube

  The Australian Geoscience Data Cube (AGDC) is an approach to storing, processing and analyzing large collections of Earth observation data. The technology is designed to meet challenges of national interest by being agile and flexible with vast amounts of layered grid data. The AGDC reduces processing time of traditional image analysis by calibrating, pre-computing known extents, pixel alignment and storing metadata in a cell lattice structure. The temporal-pixel aligned data can often be analysed faster across space and time dimensions than previous scene based techniques. This allows the AGDC to be flexible in tackling future challenges and improve analysis times on every-increasing data repositories of earth observation. The AGDC has also been used internationally to allow countries to maintain ecologically sustainable programs and reduce the difficulty curve of utilizing Remote Sensing data. == Background == The AGDC was originally conceived by Geoscience Australia but is now maintained in a partnership between Geoscience Australia, Commonwealth Scientific and Industrial Research Organisation (CSIRO) and National Computational Infrastructure National Facility (Australia) (NCI). This is made possible by the funding from the partnership and a number of organisations such as National Collaborative Research Infrastructure Strategy (NCRIS). == Analysis ready data, ingestion and indexing == The data processed in the cube is made analysis ready before being ingested and indexed into the AGDC. Analysis ready data is pre-processed data that has applied corrections for instrument calibration (gains and offsets), geolocation (spatial alignment) and radiometry (solar illumination, incidence angle, topography, atmospheric interference). The ingestion process manages the translation of datasets into the storage units while maintaining a database index. The data within the storage and index can be accessed via API calls often compiled within code such as Python (programming language). Example: s2a_l1c = dc.load(product='s2a_level1c_granule',x=(147.36, 147.41), y=(-35.1, -35.15), measurements=['04','03','02'], output_crs='EPSG:4326', resolution=(-0.00025,0.00025)) === Datasets currently stored === Geoscience Australia Landsat Surface Reflectance (1987 to present) Landsat Pixel Quality Landsat Fractional Cover Landsat NDVI === Datasets that have been piloted === USGS Landsat Surface Reflectance SRTM DEM Himawari 8 MODIS Sentinel-2 L1C / S2A Australian Gridded Climate Data == Open source == The AGDC code base is situated in GitHub as an open repository. The core code base moved to the Open Data Cube in early 2017 as part of an international collaboration. Whilst the code base is the Open Data Cube, individual cubes exist as their own right such as the AGDC on the National Computational Infrastructure National Facility (Australia) (NCI) using the High-Performance Computing Cluster HPCC. The core code can be installed on personal computers or public computers (using git) and has many unit tests. Documentation for the code base exists on Read the Docs. == Challenges of the AGDC == The AGDC is designed to meet nationally significant challenges such as the following. Sustainability Environment Water resource management Disaster assist Policy development Community planning Forest preservation Carbon measurement == International awards == The AGDC won the 2016 Content Platform of the Year award from Geospatial World Forum.

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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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  Causal AI

  Causal AI is a technique in artificial intelligence that builds a causal model and can thereby make inferences using causality rather than just correlation. One practical use for causal AI is for organisations to explain decision-making and the causes for a decision. Systems based on causal AI, by identifying the underlying web of causality for a behaviour or event, provide insights that solely predictive AI models might fail to extract from historical data. An analysis of causality may be used to supplement human decisions in situations where understanding the causes behind an outcome is necessary, such as quantifying the impact of different interventions, policy decisions or performing scenario planning. A 2024 paper from Google DeepMind demonstrated mathematically that "Any agent capable of adapting to a sufficiently large set of distributional shifts must have learned a causal model". The paper offers the interpretation that learning to generalise beyond the original training set requires learning a causal model, concluding that causal AI is necessary for artificial general intelligence. == History == The concept of causal AI and the limits of machine learning were raised by Judea Pearl, the Turing Award-winning computer scientist and philosopher, in 2018's The Book of Why: The New Science of Cause and Effect. Pearl asserted: “Machines' lack of understanding of causal relations is perhaps the biggest roadblock to giving them human-level intelligence.” In 2020, Columbia University established a Causal AI Lab under Director Elias Bareinboim. Professor Bareinboim's research focuses on causal and counterfactual inference and their applications to data-driven fields in the health and social sciences as well as artificial intelligence and machine learning. Technological research and consulting firm Gartner for the first time included causal AI in its 2022 Hype Cycle report, citing it as one of five critical technologies in accelerated AI automation. Causal AI is closely related to but distinct from fields such as causal inference, explainable AI and causal reasoning. While causal inference focuses on estimating cause-effect relationships (often from observational data), causal AI emphasises the integration of those causal models into AI systems for prediction, planning and adaptation.

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  Poop Map

  Poop Map is a social app where users can track on a map where and when they defecate. In addition to logging location and time of each bowel movement, users can also add a photo, "like" other users' logs, and rate each account. The social elements of the app allow for groups of users to create a competitive league. Certain behaviors unlock achievements in-app. == Development == The app was created by app developer Nino Uzelac. It was launched in July 2013. == Popularity == The app charted at number one on the Apple App Store charts in 2021 after going viral on TikTok. As of September 2024, the app has a 4.8 rating on the App Store and more than 58,000 ratings. It also has more than one million downloads on the Google Play Store. Poop Map is notably popular among hikers, and has been written about in the outdoors magazine Outside.

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  MaPS S.A.

  MaPS S.A. is a software editor founded in 2011 by Thierry Muller. The company is headquartered in Luxembourg. Its platform, called MaPS System, provides Data Management software for Multichannel Marketing. == History and funding == The first version of MaPS System was released under the agency Prem1um S.A. in 2005 in the partnership with Pingroom agency. In combination with MaPS System, Prem1um also provided various consulting services in Marketing, Publishing and Sales. This is where MaPS System takes its names (M stands for Marketing, P for Publishing and S for Sales). In 2011, after being successful, Prem1um S.A. decided to enable the software MaPS System to operate independently under MaPS S.A., as a separate company and editor of the software. The first financial supports were provided by Malta ICI, a Venture Capital firm, and the local partner Chameleon Invest, a seed-capital fund led by Business Angels, who invested €900,000. In a second investment round in 2014 led by Newion Investments, a Venture Capital firm, €1.4 Million were raised, thus amounting to total assets of €2.2 Million. In 2016, the company was taken over by three private investors. In 2018, after two years of continuous growth and European expansion in Belgium, Germany and Switzerland, MaPS S.A acquired Awevo, an e-commerce web agency. == Products == The services included in MaPS System range from the data centralization, Data Governance to an optimized Multichannel Marketing. The software currently includes more than 35 modules for Master Data Management, Product Information Management, Digital Asset Management, Business Process Management including catalogue Publishing features. == Certifications == In 2019, MaPS System and Awevo received "Made in Luxembourg" label, given to the companies whose services are entirely designed in Luxembourg, without any production or development offshoring. MaPS System is a member of ICT Cluster by Luxinnovation.

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  Time Warp Edit Distance

  In the data analysis of time series, Time Warp Edit Distance (TWED) is a measure of similarity (or dissimilarity) between pairs of discrete time series, controlling the relative distortion of the time units of the two series using the physical notion of elasticity. In comparison to other distance measures, (e.g. DTW (dynamic time warping) or LCS (longest common subsequence problem)), TWED is a metric. Its computational time complexity is O ( n 2 ) {\displaystyle O(n^{2})} , but can be drastically reduced in some specific situations by using a corridor to reduce the search space. Its memory space complexity can be reduced to O ( n ) {\displaystyle O(n)} . It was first proposed in 2009 by P.-F. Marteau. == Definition == δ λ , ν ( A 1 p , B 1 q ) = M i n { δ λ , ν ( A 1 p − 1 , B 1 q ) + Γ ( a p ′ → Λ ) d e l e t e i n A δ λ , ν ( A 1 p − 1 , B 1 q − 1 ) + Γ ( a p ′ → b q ′ ) m a t c h o r s u b s t i t u t i o n δ λ , ν ( A 1 p , B 1 q − 1 ) + Γ ( Λ → b q ′ ) d e l e t e i n B {\displaystyle \delta _{\lambda ,\nu }(A_{1}^{p},B_{1}^{q})=Min{\begin{cases}\delta _{\lambda ,\nu }(A_{1}^{p-1},B_{1}^{q})+\Gamma (a_{p}^{'}\to \Lambda )&{\rm {delete\ in\ A}}\\\delta _{\lambda ,\nu }(A_{1}^{p-1},B_{1}^{q-1})+\Gamma (a_{p}^{'}\to b_{q}^{'})&{\rm {match\ or\ substitution}}\\\delta _{\lambda ,\nu }(A_{1}^{p},B_{1}^{q-1})+\Gamma (\Lambda \to b_{q}^{'})&{\rm {delete\ in\ B}}\end{cases}}} whereas Γ ( α p ′ → Λ ) = d L P ( a p ′ , a p − 1 ′ ) + ν ⋅ ( t a p − t a p − 1 ) + λ {\displaystyle \Gamma (\alpha _{p}^{'}\to \Lambda )=d_{LP}(a_{p}^{'},a_{p-1}^{'})+\nu \cdot (t_{a_{p}}-t_{a_{p-1}})+\lambda } Γ ( α p ′ → b q ′ ) = d L P ( a p ′ , b q ′ ) + d L P ( a p − 1 ′ , b q − 1 ′ ) + ν ⋅ ( | t a p − t b q | + | t a p − 1 − t b q − 1 | ) {\displaystyle \Gamma (\alpha _{p}^{'}\to b_{q}^{'})=d_{LP}(a_{p}^{'},b_{q}^{'})+d_{LP}(a_{p-1}^{'},b_{q-1}^{'})+\nu \cdot (|t_{a_{p}}-t_{b_{q}}|+|t_{a_{p-1}}-t_{b_{q-1}}|)} Γ ( Λ → b q ′ ) = d L P ( b p ′ , b p − 1 ′ ) + ν ⋅ ( t b q − t b q − 1 ) + λ {\displaystyle \Gamma (\Lambda \to b_{q}^{'})=d_{LP}(b_{p}^{'},b_{p-1}^{'})+\nu \cdot (t_{b_{q}}-t_{b_{q-1}})+\lambda } Whereas the recursion δ λ , ν {\displaystyle \delta _{\lambda ,\nu }} is initialized as: δ λ , ν ( A 1 0 , B 1 0 ) = 0 , {\displaystyle \delta _{\lambda ,\nu }(A_{1}^{0},B_{1}^{0})=0,} δ λ , ν ( A 1 0 , B 1 j ) = ∞ f o r j ≥ 1 {\displaystyle \delta _{\lambda ,\nu }(A_{1}^{0},B_{1}^{j})=\infty \ {\rm {{for\ }j\geq 1}}} δ λ , ν ( A 1 i , B 1 0 ) = ∞ f o r i ≥ 1 {\displaystyle \delta _{\lambda ,\nu }(A_{1}^{i},B_{1}^{0})=\infty \ {\rm {{for\ }i\geq 1}}} with a 0 ′ = b 0 ′ = 0 {\displaystyle a'_{0}=b'_{0}=0} === Implementations === An implementation of the TWED algorithm in C with a Python wrapper is available at TWED is also implemented into the Time Series Subsequence Search Python package (TSSEARCH for short) available at [1]. An R implementation of TWED has been integrated into the TraMineR, a R package for mining, describing and visualizing sequences of states or events, and more generally discrete sequence data. Additionally, cuTWED is a CUDA- accelerated implementation of TWED which uses an improved algorithm due to G. Wright (2020). This method is linear in memory and massively parallelized. cuTWED is written in CUDA C/C++, comes with Python bindings, and also includes Python bindings for Marteau's reference C implementation. ==== Python ==== Backtracking, to find the most cost-efficient path: ==== MATLAB ==== Backtracking, to find the most cost-efficient path:

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  ISO 15926

  ISO 15926 is a standard for data integration, sharing, exchange, and hand-over between computer systems. The title, "Industrial automation systems and integration—Integration of life-cycle data for process plants including oil and gas production facilities", is regarded too narrow by the present ISO 15926 developers. Having developed a generic data model and reference data library for process plants, it turned out that this subject is already so wide, that actually any state information may be modelled with it. == History == In 1991 a European Union ESPRIT-, named ProcessBase, started. The focus of this research project was to develop a data model for lifecycle information of a facility that would suit the requirements of the process industries. At the time that the project duration had elapsed, a consortium of companies involved in the process industries had been established: EPISTLE (European Process Industries STEP Technical Liaison Executive). Initially individual companies were members, but later this changed into a situation where three national consortia were the only members: PISTEP (UK), POSC/Caesar (Norway), and USPI-NL (Netherlands). (later PISTEP merged into POSC/Caesar, and USPI-NL was renamed to USPI). EPISTLE took over the work of the ProcessBase project. Initially this work involved a standard called ISO 10303-221 (referred to as "STEP AP221"). In that AP221 we saw, for the first time, an Annex M with a list of standard instances of the AP221 data model, including types of objects. These standard instances would be for reference and would act as a knowledge base with knowledge about the types of objects. In the early nineties EPISTLE started an activity to extend Annex M to become a library of such object classes and their relationships: STEPlib. In the STEPlib activities a group of approx. 100 domain experts from all three member consortia, spread over the various expertises (e.g. Electrical, Piping, Rotating equipment, etc.), worked together to define the "core classes". The development of STEPlib was extended with many additional classes and relationships between classes and published as Open source data. Furthermore, the concepts and relation types from the AP221 and ISO 15926-2 data models were also added to the STEPlib dictionary. This resulted in the development of Gellish English, whereas STEPlib became the Gellish English dictionary. Gellish English is a structured subset of natural English and is a modeling language suitable for knowledge modeling, product modeling and data exchange. It differs from conventional modeling languages (meta languages) as used in information technology as it not only defines generic concepts, but also includes an English dictionary. The semantic expression capability of Gellish English was significantly increased by extending the number of relation types that can be used to express knowledge and information. For modelling-technical reasons POSC/Caesar proposed another standard than ISO 10303, called ISO 15926. EPISTLE (and ISO) supported that proposal, and continued the modelling work, thereby writing Part 2 of ISO 15926. This Part 2 has official ISO IS (International Standard) status since 2003. POSC/Caesar started to put together their own RDL (Reference Data Library). They added many specialized classes, for example for ANSI (American National Standards Institute) pipe and pipe fittings. Meanwhile, STEPlib continued its existence, mainly driven by some members of USPI. Since it was clear that it was not in the interest of the industry to have two libraries for, in essence, the same set of classes, the Management Board of EPISTLE decided that the core classes of the two libraries shall be merged into Part 4 of ISO 15926. This merging process has been finished. Part 4 should act as reference data for part 2 of ISO 15926 as well as for ISO 10303-221 and replaced its Annex M. On June 5, 2007 ISO 15926-4 was signed off as a TS (Technical Specification). In 1999 the work on an earlier version of Part 7 started. Initially this was based on XML Schema (the only useful W3C Recommendation available then), but when Web Ontology Language (OWL) became available it was clear that provided a far more suitable environment for Part 7. Part 7 passed the first ISO ballot by the end of 2005, and an implementation project started. A formal ballot for TS (Technical Specification) was planned for December 2007. However, it was decided then to split Part 7 into more than one part, because the scope was too wide. == Need for ISO15926 == In 2004, the National Institute of Standards and Technology (NIST) released a report on the impact of the lack of digital interoperability in the capital projects industry. The report estimated the cost of inadequate interoperability in the U.S. capital facilities industry to be $15.8 billion per year. This was considered likely to be a conservative figure. == The standard == ISO 15926 has thirteen parts (as of February 2022): Part 1 - Overview and fundamental principles Part 2 - Data model Part 3 - Reference data for geometry and topology Part 4 - Reference Data, the terms used within facilities for the process industry Part 6 - Methodology for the development and validation of reference data (under development) Part 7 - Template methodology Part 8 - OWL/RDF implementation Part 9 - Implementation standards, with the focus on standard web servers, web services, and security (under development) Part 10 - Conformance testing Part 11 - Methodology for simplified industrial usage of reference data (under development) Part 12 - Life cycle integration ontology in Web Ontology Language (OWL2) Part 13 - Integrated lifecycle asset planning === Description === The model and the library are suitable for representing lifecycle information about technical installations and their components. They can also be used for defining the terms used in product catalogs in e-commerce. Another, more limited, use of the standard is as a reference classification for harmonization purposes between shared databases and product catalogues that are not based on ISO 15926. The purpose of ISO 15926 is to provide a Lingua Franca for computer systems, thereby integrating the information produced by them. Although set up for the process industries with large projects involving many parties, and involving plant operations and maintenance lasting decades, the technology can be used by anyone willing to set up a proper vocabulary of reference data in line with Part 4. In Part 7 the concept of Templates is introduced. These are semantic constructs, using Part 2 entities, that represent a small piece of information. These constructs then are mapped to more efficient classes of n-ary relations that interlink the Nodes that are involved in the represented information. In Part 8 the Part 7 Templates are defined in OWL and instantiated in RDF. For validation and reasoning purposes all are represented in First-Order Logic as well. In Part 9 these Node and Template instances are stored in an RDF triple store, set up to a standard schema and an API. Each participating computer system maps its data from its internal format to such ISO-standard Node and Template instances. Data can be "handed over" from one triple store to another in cases where data custodianship is handed over (e.g. from a contractor to a plant owner, or from a manufacturer to the owners of the manufactured goods). Hand-over can be for a part of all data, whilst maintaining full referential integrity. Documents are user-definable. They are defined in XML Schema and they are, in essence, only a structure containing cells that make reference to instances of Templates. This represents a view on all lifecycle data: since the data model is a 4D (space-time) model, it is possible to present the data that was valid at any given point in time, thus providing a true historical record. It is expected that this will be used for Knowledge Mining. Data can be queried by means of SPARQL. In any implementation a restricted number of triple stores can be involved, with different access rights. This is done by means of creating a CPF Server (= Confederation of Participating Façades). An Ontology Browser allows for access to one or more triple stores in a given CPF, depending on the access rights. == Projects and applications == There are a number of projects working on the extension of the ISO 15926 standard in different application areas. === Capital-intensive projects === Within the application of Capital Intensive projects, some cooperating implementation projects are running: The DEXPI project: The objective of DEXPI is to develop and promote a general standard for the process industry covering all phases of the lifecycle of a (petro-)chemical plant, ranging from specification of functional requirements to assets in operation. Finalised projects include: The EDRC Project of FIATECH Capturing Equipment Data Requirements Using ISO 15926 and Assessing Conforma

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  Adobe Presenter Video Express

  Adobe Presenter Video Express is screencasting and video editing software developed by Adobe Systems. == Description == Adobe Presenter Video Express is primarily used as a software by video creators, to record and mix webcam and screen video feeds. It allows users to simultaneously record video from their webcam and the screen, and easily mix the 2 tracks with a simple user interface. Users can change the background in their recorded video without needing equipment like a green screen. This is unlike other video tools which rely on chroma keying technology, and only work with green or blue screens. They can also add annotations and quizzes to their content and publish the video to MP4 or HTML5 formats. == List of notable features == === Record and mix, screen and webcam === Support for simultaneous recording of screen and webcam video feeds, with a simple editing interface to mix the two video streams. This lets the author rapidly create screencasts, software demos, etc. === Make my background awesome === This feature allows authors to change the background of their webcam recording without needing a green screen, provided they use a solid-colored backdrop which contrasts well against them. Authors can select images, videos or even the screen recording as their background. === In-video quizzing === Authors can insert quizzes within their video content. On success/failure attempts, the author can decide what message to display, and can also configure the video to jump to a certain point and play. Quizzes are published as part of the interactive HTML 5 player, which cannot be hosted on YouTube and Vimeo. === LMS Reporting === Authors can publish to any SCORM compliant LMS (Learning Management System) for quiz reporting, or to Adobe Captivate Prime. === In-app assets and branding === Adobe Presenter Video Express ships with a large number of branding videos, backgrounds and video filters to help authors create studio quality videos. === MP4 and HTML5 Output === The tool publishes a single MP4 video file containing all the video content, within an HTML 5 wrapper that contains the interactive player. The interactive HTML 5 player can be hosted on any website. == Common uses == === Screencasting === Screencasting is the process of recording one's computer screen as a video, usually with an audio voice over, to create a software demonstration, tutorial, presentation, etc. Adobe Presenter Video Express supports simultaneous recording of full screen video and microphone audio for creating screencasts. === Product marketing and demos === The ability to record the webcam video in addition to everything that is visible on the screen in Adobe Presenter Video Express, allows the author to add their personality to their screencasts. Features like video mixing and 'make my background awesome' further enhance the presentation, allowing effortless creation of marketing and demo videos. === Education === Adobe Presenter Video Express supports in-video quizzes and LMS reporting, along with screencasting and webcam recording. These features make it a powerful tool for creating educational content. == Differences from Adobe Presenter and Adobe Captivate == Adobe Presenter is a Microsoft PowerPoint plug-in for converting PowerPoint slides into interactive eLearning content, available only on Windows. Starting with Adobe Presenter 8, the video creation tool Adobe Presenter Video Express was bundled with every purchase of Adobe Presenter. From September 2015, Adobe Presenter Video Express 11 was also made available as a stand-alone product on Windows and Mac. A subscription license for Adobe Presenter Video Express, valid on Windows and Mac, is available for $9.99/month. Adobe Presenter Video Express continues to be bundled with purchases of Adobe Presenter on Windows as well. Adobe Captivate is an authoring tool for creating numerous forms of interactive eLearning content. Unlike Adobe Presenter, it uses a proprietary editing interface instead of Microsoft PowerPoint. While it is possible to create screen captures with Adobe Captivate, you cannot record the webcam feed. Adobe Captivate does not bundle Adobe Presenter or Adobe Presenter Video Express.

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  Algorithm engineering

  Algorithm engineering focuses on the design, analysis, implementation, optimization, profiling and experimental evaluation of computer algorithms, bridging the gap between algorithmics theory and practical applications of algorithms in software engineering. It is a general methodology for algorithmic research. == Origins == In 1995, a report from an NSF-sponsored workshop "with the purpose of assessing the current goals and directions of the Theory of Computing (TOC) community" identified the slow speed of adoption of theoretical insights by practitioners as an important issue and suggested measures to reduce the uncertainty by practitioners whether a certain theoretical breakthrough will translate into practical gains in their field of work, and tackle the lack of ready-to-use algorithm libraries, which provide stable, bug-free and well-tested implementations for algorithmic problems and expose an easy-to-use interface for library consumers. But also, promising algorithmic approaches have been neglected due to difficulties in mathematical analysis. The term "algorithm engineering" was first used with specificity in 1997, with the first Workshop on Algorithm Engineering (WAE97), organized by Giuseppe F. Italiano. == Difference from algorithm theory == Algorithm engineering does not intend to replace or compete with algorithm theory, but tries to enrich, refine and reinforce its formal approaches with experimental algorithmics (also called empirical algorithmics). This way it can provide new insights into the efficiency and performance of algorithms in cases where the algorithm at hand is less amenable to algorithm theoretic analysis, formal analysis pessimistically suggests bounds which are unlikely to appear on inputs of practical interest, the algorithm relies on the intricacies of modern hardware architectures like data locality, branch prediction, instruction stalls, instruction latencies which the machine model used in Algorithm Theory is unable to capture in the required detail, the crossover between competing algorithms with different constant costs and asymptotic behaviors needs to be determined. == Methodology == Some researchers describe algorithm engineering's methodology as a cycle consisting of algorithm design, analysis, implementation and experimental evaluation, joined by further aspects like machine models or realistic inputs. They argue that equating algorithm engineering with experimental algorithmics is too limited, because viewing design and analysis, implementation and experimentation as separate activities ignores the crucial feedback loop between those elements of algorithm engineering. === Realistic models and real inputs === While specific applications are outside the methodology of algorithm engineering, they play an important role in shaping realistic models of the problem and the underlying machine, and supply real inputs and other design parameters for experiments. === Design === Compared to algorithm theory, which usually focuses on the asymptotic behavior of algorithms, algorithm engineers need to keep further requirements in mind: Simplicity of the algorithm, implementability in programming languages on real hardware, and allowing code reuse. Additionally, constant factors of algorithms have such a considerable impact on real-world inputs that sometimes an algorithm with worse asymptotic behavior performs better in practice due to lower constant factors. === Analysis === Some problems can be solved with heuristics and randomized algorithms in a simpler and more efficient fashion than with deterministic algorithms. Unfortunately, this makes even simple randomized algorithms difficult to analyze because there are subtle dependencies to be taken into account. === Implementation === Huge semantic gaps between theoretical insights, formulated algorithms, programming languages and hardware pose a challenge to efficient implementations of even simple algorithms, because small implementation details can have rippling effects on execution behavior. The only reliable way to compare several implementations of an algorithm is to spend an considerable amount of time on tuning and profiling, running those algorithms on multiple architectures, and looking at the generated machine code. === Experiments === See: Experimental algorithmics === Application engineering === Implementations of algorithms used for experiments differ in significant ways from code usable in applications. While the former prioritizes fast prototyping, performance and instrumentation for measurements during experiments, the latter requires thorough testing, maintainability, simplicity, and tuning for particular classes of inputs. === Algorithm libraries === Stable, well-tested algorithm libraries like LEDA play an important role in technology transfer by speeding up the adoption of new algorithms in applications. Such libraries reduce the required investment and risk for practitioners, because it removes the burden of understanding and implementing the results of academic research. == Conferences == Two main conferences on Algorithm Engineering are organized annually, namely: Symposium on Experimental Algorithms (SEA), established in 1997 (formerly known as WEA). SIAM Meeting on Algorithm Engineering and Experiments (ALENEX), established in 1999. The 1997 Workshop on Algorithm Engineering (WAE'97) was held in Venice (Italy) on September 11–13, 1997. The Third International Workshop on Algorithm Engineering (WAE'99) was held in London, UK in July 1999. The first Workshop on Algorithm Engineering and Experimentation (ALENEX99) was held in Baltimore, Maryland on January 15–16, 1999. It was sponsored by DIMACS, the Center for Discrete Mathematics and Theoretical Computer Science (at Rutgers University), with additional support from SIGACT, the ACM Special Interest Group on Algorithms and Computation Theory, and SIAM, the Society for Industrial and Applied Mathematics.

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  Wearable computer

  A wearable computer, also known as a body-borne computer or wearable, is a computing device worn on the body. The definition of 'wearable computer' may be narrow or broad, extending to smartphones or even ordinary wristwatches. Wearables may be for general use, in which case they are just a particularly small example of mobile computing. Alternatively, they may be for specialized purposes such as fitness trackers. They may incorporate special sensors such as accelerometers, heart rate monitors, or on the more advanced side, electrocardiogram (ECG) and blood oxygen saturation (SpO2) monitors. Under the definition of wearable computers, we also include novel user interfaces such as Google Glass, an optical head-mounted display controlled by gestures. It may be that specialized wearables will evolve into general all-in-one devices, as happened with the convergence of PDAs and mobile phones into smartphones. Wearables are typically worn on the wrist (e.g. fitness trackers), hung from the neck (like a necklace), strapped to the arm or leg (electronic tagging), or on the head (as glasses or a helmet), though some have been located elsewhere (e.g. on a finger or in a shoe). Devices carried in a pocket or bag – such as smartphones and before them, pocket calculators and PDAs, may or may not be regarded as 'worn'. Wearable computers have various technical issues common to other mobile computing, such as batteries, heat dissipation, software architectures, wireless and personal area networks, and data management. Many wearable computers are active all the time, e.g. processing or recording data continuously. == Applications == Wearable computers are not only limited to computers such as fitness trackers that are worn on wrists; they also include wearables such as heart pacemakers and other prosthetics. They are used most often in research that focuses on behavioral modeling, health monitoring systems, IT and media development, where the person wearing the computer actually moves or is otherwise engaged with his or her surroundings. Wearable computers have been used for the following: general-purpose computing (e.g. smartphones and smartwatches) sensory integration, e.g. to help people see better or understand the world better (whether in task-specific applications like camera-based welding helmets or for everyday use like Google Glass) behavioral modeling health care monitoring systems service management electronic textiles and fashion design, e.g. Microsoft's 2011 prototype "The Printing Dress". Wearable computing is the subject of active research, especially the form-factor and location on the body, with areas of study including user interface design, augmented reality, and pattern recognition. The use of wearables for specific applications, for compensating disabilities or supporting elderly people steadily increases. == Operating systems == The dominant operating systems for wearable computing are: FreeRTOS is a real-time operating system kernel for embedded devices; most of the Smartbands that are currently available in the market are based on FreeRTOS, which include Huawei, Honor, Lenovo, realme, TCL and Xiaomi smartbands. LiteOS is a lightweight open source real-time operating system that is part of Huawei's "1+8+N" Internet of Things solution. Tizen OS from Samsung (there was an announcement in May 2021 that Wear OS and Tizen OS will merge and will be called simply Wear.) watchOS watchOS is a proprietary mobile operating system developed by Apple Inc. to run on the Apple Watch. Wear OS Wear OS (previously known as Android Wear) is a smartwatch operating system developed by Google Inc. == History == Due to the varied definitions of wearable and computer, the first wearable computer could be as early as the first abacus on a necklace, a 16th-century abacus ring, a wristwatch and 'finger-watch' owned by Queen Elizabeth I of England, or the covert timing devices hidden in shoes to cheat at roulette by Thorp and Shannon in the 1960s and 1970s. However, a general-purpose computer is not merely a time-keeping or calculating device, but rather a user-programmable item for arbitrary complex algorithms, interfacing, and data management. By this definition, the wearable computer was invented by Steve Mann, in the late 1970s: Steve Mann, a professor at the University of Toronto, was hailed as the father of the wearable computer and the ISSCC's first virtual panelist, by moderator Woodward Yang of Harvard University (Cambridge Mass.). The development of wearable items has taken several steps of miniaturization from discrete electronics over hybrid designs to fully integrated designs, where just one processor chip, a battery, and some interface conditioning items make the whole unit. === 1500s === Queen Elizabeth I of England received a watch from Robert Dudley in 1571, as a New Year's present; it may have been worn on the forearm rather than the wrist. She also possessed a 'finger-watch' set in a ring, with an alarm that prodded her finger. === 1600s === The Qing dynasty saw the introduction of a fully functional abacus on a ring, which could be used while it was being worn. === 1960s === In 1961, mathematicians Edward O. Thorp and Claude Shannon built some computerized timing devices to help them win a game of roulette. One such timer was concealed in a shoe and another in a pack of cigarettes. Various versions of this apparatus were built in the 1960s and 1970s. Thorp refers to himself as the inventor of the first "wearable computer". In other variations, the system was a concealed cigarette-pack-sized analog computer designed to predict the motion of roulette wheels. A data-taker would use microswitches hidden in his shoes to indicate the speed of the roulette wheel, and the computer would indicate an octant of the roulette wheel to bet on by sending musical tones via radio to a miniature speaker hidden in a collaborator's ear canal. The system was successfully tested in Las Vegas in June 1961, but hardware issues with the speaker wires prevented it from being used beyond test runs. This was not a wearable computer because it could not be re-purposed during use; rather it was an example of task-specific hardware. This work was kept secret until it was first mentioned in Thorp's book Beat the Dealer (revised ed.) in 1966 and later published in detail in 1969. === 1970s === Pocket calculators became mass-market devices in 1970, starting in Japan. Programmable calculators followed in the late 1970s, being somewhat more general-purpose computers. The HP-01 algebraic calculator watch by Hewlett-Packard was released in 1977. A camera-to-tactile vest for the blind, launched by C.C. Collins in 1977, converted images into a 1024-point, ten-inch square tactile grid on a vest. === 1980s === The 1980s saw the rise of more general-purpose wearable computers. In 1981, Steve Mann designed and built a backpack-mounted 6502-based wearable multimedia computer with text, graphics, and multimedia capability, as well as video capability (cameras and other photographic systems). Mann went on to be an early and active researcher in the wearables field, especially known for his 1994 creation of the Wearable Wireless Webcam, the first example of lifelogging. Seiko Epson released the RC-20 Wrist Computer in 1984. It was an early smartwatch, powered by a computer on a chip. In 1989, Reflection Technology marketed the Private Eye head-mounted display, which scans a vertical array of LEDs across the visual field using a vibrating mirror. This display gave rise to several hobbyist and research wearables, including Gerald "Chip" Maguire's IBM/Columbia University Student Electronic Notebook, Doug Platt's Hip-PC, and Carnegie Mellon University's VuMan 1 in 1991. The Student Electronic Notebook consisted of the Private Eye, Toshiba diskless AIX notebook computers (prototypes), a stylus based input system and a virtual keyboard. It used direct-sequence spread spectrum radio links to provide all the usual TCP/IP based services, including NFS mounted file systems and X11, which all ran in the Andrew Project environment. The Hip-PC included an Agenda palmtop used as a chording keyboard attached to the belt and a 1.44 megabyte floppy drive. Later versions incorporated additional equipment from Park Engineering. The system debuted at "The Lap and Palmtop Expo" on 16 April 1991. VuMan 1 was developed as part of a Summer-term course at Carnegie Mellon's Engineering Design Research Center, and was intended for viewing house blueprints. Input was through a three-button unit worn on the belt, and output was through Reflection Tech's Private Eye. The CPU was an 8 MHz 80188 processor with 0.5 MB ROM. === 1990s === In the 1990s PDAs became widely used, and in 1999 were combined with mobile phones in Japan to produce the first mass-market smartphone. In 1993, the Private Eye was used in Thad Starner's wearable, based on Doug Platt's system and built from a kit from Park Enterprises, a Pri

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  Block swap algorithms

  In computer algorithms, block swap algorithms swap two regions of elements of an array. It is simple to swap two non-overlapping regions of an array of equal size. However, it is not as simple to swap two contiguous regions of an array of unequal sizes (algorithms that perform such swapping are called rotation algorithms). A few well-known algorithms can accomplish this: Bentley's juggling (also known as the dolphin algorithm), Gries-Mills rotation, triple reversal algorithm, conjoined triple reversal algorithm (also known as the trinity rotation) and Successive rotation. == Triple reversal algorithm == The triple reversal algorithm is the simplest to explain, using rotations. A rotation is an in-place reversal of array elements. This method swaps two elements of an array from outside in within a range. The rotation works for an even or odd number of array elements. The reversal algorithm uses three in-place rotations to accomplish an in-place block swap: Rotate region A Rotate region B Rotate region AB Where A and B are adjacent regions of an array that together form the region AB. Gries-Mills and reversal algorithms perform better than Bentley's juggling, because of their cache-friendly memory access pattern behavior. The triple reversal algorithm parallelizes well, because rotations can be split into sub-regions, which can be rotated independently of others.

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  Manifold hypothesis

  The manifold hypothesis posits that many high-dimensional data sets that occur in the real world actually lie along low-dimensional latent manifolds inside that high-dimensional space. As a consequence of the manifold hypothesis, many data sets that appear to initially require many variables to describe, can actually be described by a comparatively small number of variables, linked to the local coordinate system of the underlying manifold. It is suggested that this principle underpins the effectiveness of machine learning algorithms in describing high-dimensional data sets by considering a few common features. The manifold hypothesis is related to the effectiveness of nonlinear dimensionality reduction techniques in machine learning. Many techniques of dimensional reduction make the assumption that data lies along a low-dimensional submanifold, such as manifold sculpting, manifold alignment, and manifold regularization. The major implications of this hypothesis is that Machine learning models only have to fit relatively simple, low-dimensional, highly structured subspaces within their potential input space (latent manifolds). Within one of these manifolds, it's always possible to interpolate between two inputs, that is to say, morph one into another via a continuous path along which all points fall on the manifold. The ability to interpolate between samples is the key to generalization in deep learning. == The information geometry of statistical manifolds == An empirically-motivated approach to the manifold hypothesis focuses on its correspondence with an effective theory for manifold learning under the assumption that robust machine learning requires encoding the dataset of interest using methods for data compression. This perspective gradually emerged using the tools of information geometry thanks to the coordinated effort of scientists working on the efficient coding hypothesis, predictive coding and variational Bayesian methods. The argument for reasoning about the information geometry on the latent space of distributions rests upon the existence and uniqueness of the Fisher information metric. In this general setting, we are trying to find a stochastic embedding of a statistical manifold. From the perspective of dynamical systems, in the big data regime this manifold generally exhibits certain properties such as homeostasis: We can sample large amounts of data from the underlying generative process. Machine Learning experiments are reproducible, so the statistics of the generating process exhibit stationarity. In a sense made precise by theoretical neuroscientists working on the free energy principle, the statistical manifold in question possesses a Markov blanket.

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  ISO 15926

  ISO 15926 is a standard for data integration, sharing, exchange, and hand-over between computer systems. The title, "Industrial automation systems and integration—Integration of life-cycle data for process plants including oil and gas production facilities", is regarded too narrow by the present ISO 15926 developers. Having developed a generic data model and reference data library for process plants, it turned out that this subject is already so wide, that actually any state information may be modelled with it. == History == In 1991 a European Union ESPRIT-, named ProcessBase, started. The focus of this research project was to develop a data model for lifecycle information of a facility that would suit the requirements of the process industries. At the time that the project duration had elapsed, a consortium of companies involved in the process industries had been established: EPISTLE (European Process Industries STEP Technical Liaison Executive). Initially individual companies were members, but later this changed into a situation where three national consortia were the only members: PISTEP (UK), POSC/Caesar (Norway), and USPI-NL (Netherlands). (later PISTEP merged into POSC/Caesar, and USPI-NL was renamed to USPI). EPISTLE took over the work of the ProcessBase project. Initially this work involved a standard called ISO 10303-221 (referred to as "STEP AP221"). In that AP221 we saw, for the first time, an Annex M with a list of standard instances of the AP221 data model, including types of objects. These standard instances would be for reference and would act as a knowledge base with knowledge about the types of objects. In the early nineties EPISTLE started an activity to extend Annex M to become a library of such object classes and their relationships: STEPlib. In the STEPlib activities a group of approx. 100 domain experts from all three member consortia, spread over the various expertises (e.g. Electrical, Piping, Rotating equipment, etc.), worked together to define the "core classes". The development of STEPlib was extended with many additional classes and relationships between classes and published as Open source data. Furthermore, the concepts and relation types from the AP221 and ISO 15926-2 data models were also added to the STEPlib dictionary. This resulted in the development of Gellish English, whereas STEPlib became the Gellish English dictionary. Gellish English is a structured subset of natural English and is a modeling language suitable for knowledge modeling, product modeling and data exchange. It differs from conventional modeling languages (meta languages) as used in information technology as it not only defines generic concepts, but also includes an English dictionary. The semantic expression capability of Gellish English was significantly increased by extending the number of relation types that can be used to express knowledge and information. For modelling-technical reasons POSC/Caesar proposed another standard than ISO 10303, called ISO 15926. EPISTLE (and ISO) supported that proposal, and continued the modelling work, thereby writing Part 2 of ISO 15926. This Part 2 has official ISO IS (International Standard) status since 2003. POSC/Caesar started to put together their own RDL (Reference Data Library). They added many specialized classes, for example for ANSI (American National Standards Institute) pipe and pipe fittings. Meanwhile, STEPlib continued its existence, mainly driven by some members of USPI. Since it was clear that it was not in the interest of the industry to have two libraries for, in essence, the same set of classes, the Management Board of EPISTLE decided that the core classes of the two libraries shall be merged into Part 4 of ISO 15926. This merging process has been finished. Part 4 should act as reference data for part 2 of ISO 15926 as well as for ISO 10303-221 and replaced its Annex M. On June 5, 2007 ISO 15926-4 was signed off as a TS (Technical Specification). In 1999 the work on an earlier version of Part 7 started. Initially this was based on XML Schema (the only useful W3C Recommendation available then), but when Web Ontology Language (OWL) became available it was clear that provided a far more suitable environment for Part 7. Part 7 passed the first ISO ballot by the end of 2005, and an implementation project started. A formal ballot for TS (Technical Specification) was planned for December 2007. However, it was decided then to split Part 7 into more than one part, because the scope was too wide. == Need for ISO15926 == In 2004, the National Institute of Standards and Technology (NIST) released a report on the impact of the lack of digital interoperability in the capital projects industry. The report estimated the cost of inadequate interoperability in the U.S. capital facilities industry to be $15.8 billion per year. This was considered likely to be a conservative figure. == The standard == ISO 15926 has thirteen parts (as of February 2022): Part 1 - Overview and fundamental principles Part 2 - Data model Part 3 - Reference data for geometry and topology Part 4 - Reference Data, the terms used within facilities for the process industry Part 6 - Methodology for the development and validation of reference data (under development) Part 7 - Template methodology Part 8 - OWL/RDF implementation Part 9 - Implementation standards, with the focus on standard web servers, web services, and security (under development) Part 10 - Conformance testing Part 11 - Methodology for simplified industrial usage of reference data (under development) Part 12 - Life cycle integration ontology in Web Ontology Language (OWL2) Part 13 - Integrated lifecycle asset planning === Description === The model and the library are suitable for representing lifecycle information about technical installations and their components. They can also be used for defining the terms used in product catalogs in e-commerce. Another, more limited, use of the standard is as a reference classification for harmonization purposes between shared databases and product catalogues that are not based on ISO 15926. The purpose of ISO 15926 is to provide a Lingua Franca for computer systems, thereby integrating the information produced by them. Although set up for the process industries with large projects involving many parties, and involving plant operations and maintenance lasting decades, the technology can be used by anyone willing to set up a proper vocabulary of reference data in line with Part 4. In Part 7 the concept of Templates is introduced. These are semantic constructs, using Part 2 entities, that represent a small piece of information. These constructs then are mapped to more efficient classes of n-ary relations that interlink the Nodes that are involved in the represented information. In Part 8 the Part 7 Templates are defined in OWL and instantiated in RDF. For validation and reasoning purposes all are represented in First-Order Logic as well. In Part 9 these Node and Template instances are stored in an RDF triple store, set up to a standard schema and an API. Each participating computer system maps its data from its internal format to such ISO-standard Node and Template instances. Data can be "handed over" from one triple store to another in cases where data custodianship is handed over (e.g. from a contractor to a plant owner, or from a manufacturer to the owners of the manufactured goods). Hand-over can be for a part of all data, whilst maintaining full referential integrity. Documents are user-definable. They are defined in XML Schema and they are, in essence, only a structure containing cells that make reference to instances of Templates. This represents a view on all lifecycle data: since the data model is a 4D (space-time) model, it is possible to present the data that was valid at any given point in time, thus providing a true historical record. It is expected that this will be used for Knowledge Mining. Data can be queried by means of SPARQL. In any implementation a restricted number of triple stores can be involved, with different access rights. This is done by means of creating a CPF Server (= Confederation of Participating Façades). An Ontology Browser allows for access to one or more triple stores in a given CPF, depending on the access rights. == Projects and applications == There are a number of projects working on the extension of the ISO 15926 standard in different application areas. === Capital-intensive projects === Within the application of Capital Intensive projects, some cooperating implementation projects are running: The DEXPI project: The objective of DEXPI is to develop and promote a general standard for the process industry covering all phases of the lifecycle of a (petro-)chemical plant, ranging from specification of functional requirements to assets in operation. Finalised projects include: The EDRC Project of FIATECH Capturing Equipment Data Requirements Using ISO 15926 and Assessing Conforma

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  The Citation Project

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

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