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  • CrewAI

    CrewAI

    CrewAI is an open-source software framework and platform for building AI agents and multi-agent systems. Written primarily in Python, it is used to define artificial-intelligence agents, assign tasks to them, and coordinate their work through agent teams and workflows. The framework is associated with CrewAI Inc., a startup developing enterprise tools for automating business workflows with large language model-based agents. == History == CrewAI was first released on the Python Package Index in December 2023. The project was created by João Moura and later developed by CrewAI Inc. and open-source contributors. In October 2024, TechCrunch reported that CrewAI had raised $18 million across seed and Series A funding rounds from investors including Boldstart Ventures, Craft Ventures, Earl Grey Capital, and Insight Partners. The report also stated that Andrew Ng and HubSpot co-founder Dharmesh Shah had invested in the company. SiliconANGLE described the company as the developer of an open-source framework for building artificial-intelligence agents and reported that the funding consisted of a seed round led by Boldstart Ventures and a Series A led by Insight Partners. By late 2024, CrewAI had introduced commercial enterprise products built on top of its open-source components. TechCrunch reported that the company's enterprise offering added access controls, analytics, support, and templates for workflow automation. == Features == CrewAI is designed around groups of agents, sometimes called "crews", that can be assigned roles, goals, and tasks. The framework supports agent collaboration, task delegation, tool use, memory, and knowledge sources for retrieval-augmented generation workflows. The project describes two main building blocks: "Crews", which are used for autonomous agent collaboration, and "Flows", which are used for more controlled event-driven workflows. The framework is independent of LangChain and is released under the MIT License. It can be installed as a Python package and is commonly used with external large language model APIs or local models, depending on the developer's configuration. == Business model == CrewAI combines an open-source framework with commercial enterprise products. Its enterprise products are intended for organizations that need to build, monitor, and manage agent-based automations with additional security, observability, and administrative controls.

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  • Conversational AI Platforms Reviews: What Actually Works in 2026

    Conversational AI Platforms Reviews: What Actually Works in 2026

    Shopping for the best conversational AI platform? An conversational AI platform is software that uses machine learning to help you get more done — it keeps getting smarter as the underlying models improve. Pricing, accuracy, and the size of the model behind the tool are the three factors that most affect daily usefulness. Whether you are a beginner or a pro, the right conversational AI platform slots into your workflow and pays for itself fast. We tested the leading options and ranked them by quality, value, and ease of use.

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  • Yorick Wilks

    Yorick Wilks

    Yorick Alexander Wilks FBCS (27 October 1939 – 14 April 2023) was a British computer scientist. He was an emeritus professor of artificial intelligence at the University of Sheffield, visiting professor of artificial intelligence at Gresham College (a post created especially for him), senior research fellow at the Oxford Internet Institute, senior scientist at the Florida Institute for Human and Machine Cognition, and a member of the Epiphany Philosophers. In February 2023, Wilks joined WiredVibe as Director of AI and a Board Member, with the goal of commercializing his previous research and ideas. He remained in this role until his death, which occurred shortly before WiredVibe was acquired by AKY X, a company that continues to build on his legacy and contributions. == Biography == Wilks was born in Gerrards Cross, Buckinghamshire in England. He was educated at Torquay Boys' Grammar School, followed by Pembroke College, Cambridge, where he read Philosophy, joined the Epiphany Philosophers and obtained his Doctor of Philosophy degree (1968) under Professor R. B. Braithwaite for the thesis 'Argument and Proof'; he was an early pioneer in meaning-based approaches to the understanding of natural language content by computers. His main early contribution in the 1970s was called "Preference Semantics" (Wilks, 1973; Wilks and Fass, 1992), an algorithmic method for assigning the "most coherent" interpretation to a sentence in terms of having the maximum number of internal preferences of its parts (normally verbs or adjectives) satisfied. That early work was hand-coded with semantic entries (of the order of some hundreds) as was normal at the time, but since then has led to the empirical determinations of preferences (chiefly of English verbs) in the 1980s and 1990s. A key component of the notion of preference in semantics was that the interpretation of an utterance is not a well- or ill-formed notion, as was argued in Chomskyan approaches, such as those of Jerry Fodor and Jerrold Katz. It was rather that a semantic interpretation was the best available, even though some preferences might not be satisfied. So, in "The machine answered the question with a low whine" the agent of "answer" does not satisfy that verb's preference for a human answerer—which would cause it to be deemed ill-formed by Fodor and Katz—but is accepted as sub-optimal or metaphorical, and, now, conventional. The function of the algorithm is not to determine well-formedness at all but to make the optimal selection of word-senses to participate in the overall interpretation. Thus, in "The Pole answered..." the system will always select the human sense of the agent and not the inanimate one if it gives a more coherent interpretation overall. Preference Semantics is thus some of the earliest computational work—with programs run at Systems Development Corporation in Santa Monica in 1967 in LISP on an IBM360—in the now established field of word sense disambiguation. This approach was used in the first operational machine translation system based principally on meaning structures and built by Wilks at Stanford Artificial Intelligence Laboratory in the early 1970s (Wilks, 1973) at the same time and place as Roger Schank was applying his "Conceptual Dependency" approach to machine translation. The LISP code of Wilks' system was in The Computer Museum, Boston. Wilks was elected a fellow of the American and European Associations for Artificial Intelligence, of the British Computer Society, a member of the UK Computing Research Committee, and a permanent member of ICCL, the International Committee on Computational Linguistics. He was professor of artificial intelligence at the University of Sheffield and a senior research fellow at the Oxford Internet Institute. In 1991 he received a Defense Advanced Projects Agency grant on interlingual pragmatics-based machine translation and in 1994 he received a grant by the Engineering and Physical Sciences Research Council to investigate in the field of large-scale information extraction (LaSIE); in the following years he would obtain more grants to carry on exploring the field of information extraction (AVENTINUS, ECRAN, PASTA...). In the 1990s Wilks also became interested in modelling human-computer dialogue and the team led by David Levy and him as chief researcher won the Loebner Prize in 1997. He was the founding director of the EU funded Companions Project on creating long-term computer companions for people. At his Festschrift in 2007 at the British Computer Society in London a volume of his own papers was presented along with a volume of essays in his honour. He was awarded the Antonio Zampolli prize in honour of his lifetime work at the LREC 2008 conference on 28 May 2008, and the Lifetime Achievement Award at the ACL 2008 conference on 18 June 2008. In 2009, he was awarded the British Computer Society's Lovelace Medal, its annual award for research achievement, and was awarded the Fellowship of the Association for Computing Machinery. In 1998, Wilks became head of the Department of Computer Science of the University of Sheffield, where he had started working in the year 1993 as professor of artificial intelligence, a post he still held. In 1993 he became the founding director of the Institute of Language, Speech and Hearing (ILASH). Wilks also set up the Natural Language Processing Group of the University of Sheffield. In 1994 he (along with Rob Gaizauskas and Hamish Cunningham) designed GATE, an advanced NLP architecture that has been widely distributed. National Life Stories conducted an oral history interview (C1672/24) with Yorick Wilks in 2016 for its Science and Religion collection held by the British Library. Wilks died on 14 April 2023, at the age of 83. == Awards == Wilks received many awards: (2009) Elected Fellow of the Association for Computing Machinery (2009) Lovelace Medal by the British Computer Society (2008) Zampolli Prize (ELRA, awarded at LREC in Marrakech, Morocco) (2008) Lifetime Achievement Award (Association for Computational Linguistics, in Columbus) (2006) Visiting Professor, University of Oxford (2004) Elected to UK Computing Research Committee (2004) Elected Fellow, British Computer Society (2003) Visiting Fellow, Oxford Internet Institute (1998) Elected Fellow of European Association for Artificial Intelligence (1997) Elected Fellow, EPSRC College of Computing (1991) Visiting Fellow, Trinity Hall, Cambridge (1991) Elected Fellow of the American Association for Artificial Intelligence (1983) Royal Society Travel Fellowship (1983) Commonwealth of Australia Visiting Professor (1981) Visiting Sloan Fellow, University of California, Berkeley (1980) Invited Participant in the Nobel Symposium on Language, Stockholm (1979) NATO Senior Scientist Fellowship (1979) Visiting Sloan Fellow, Yale University (1975) SRC Senior Visiting Fellowship, University of Edinburgh == Membership == Wilks was an active member of the following associations: Association for Computational Linguistics Society for the Study of AI and Simulation of Behaviour Association for Computing Machinery Cognitive Science Society British Society for the Philosophy of Science American Association for Artificial Intelligence Aristotelian Society == Selected works == === Books === Wilks, Y. (2019) Artificial Intelligence: Modern Magic or Dangerous Future?.Icon Books. New illustrated edition, 2023, MIT Press. Wilks, Y. (2015) Machine Translation: its scope and limits. Springer Wilks, Y (ed.) (2010) Close Engagements with Artificial Companions: Key Social, Psychological and Design issues. John Benjamins; Amsterdam Wilks, Y., Brewster, C. (2009) Natural Language Processing as a Foundation of the Semantic Web. Now Press: London. Wilks, Y. (2007) Words and Intelligence I, Selected papers by Yorick Wilks. In K. Ahmad, C. Brewster & M. Stevenson (eds.), Springer: Dordrecht. Wilks, Y. (ed. and with introduction and commentaries). (2006) Language, cohesion and form: selected papers of Margaret Masterman. Cambridge: Cambridge University Press. Wilks, Y., Nirenburg, S., Somers, H. (eds.) (2003) Readings in Machine Translation. Cambridge, MA: MIT Press. Wilks, Y.(ed.). (1999) Machine Conversations. Kluwer: New York. Wilks, Y., Slator, B., Guthrie, L. (1996) Electric Words: dictionaries, computers and meanings. Cambridge, MA: MIT Press. Ballim, A., Wilks, Y. (1991) Artificial Believers. Norwood, NJ: Erlbaum. Wilks, Y.(ed.). (1990) Theoretical Issues in Natural Language Processing. Norwood, NJ: Erlbaum. Wilks, Y., Partridge, D. (eds. plus three YW chapters and an introduction). (1990) The Foundations of Artificial Intelligence: a sourcebook. Cambridge: Cambridge University Press. Wilks, Y., Sparck-Jones, K.(eds.). (1984) Automatic Natural Language Processing, paperback edition. New York: Wiley. Originally published by Ellis Horwood. Wilks, Y., Charniak, E. (eds and principal authors). (1976) Computational Semantics—an Introduction to Artificial Intelligence and

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  • AI Writing Assistants: Free vs Paid (2026)

    AI Writing Assistants: Free vs Paid (2026)

    Curious about the best AI writing assistant? An AI writing assistant is software that uses machine learning to help you get more done — it combines speed, accuracy, and an interface that just works. Hands-on testing shows real-world results vary, so a short free trial is the smartest way to decide. Whether you are a beginner or a pro, the right AI writing assistant slots into your workflow and pays for itself fast. This guide breaks down the top picks, their pros and cons, and who each one is best for.

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  • Floyd–Steinberg dithering

    Floyd–Steinberg dithering

    Floyd–Steinberg dithering is an image dithering algorithm first published in 1976 by Robert W. Floyd and Louis Steinberg. It is commonly used by image manipulation software, for example, when converting an image from a Truecolor 24-bit PNG format into a GIF format, which is restricted to a maximum of 256 colors. == Implementation == The algorithm achieves dithering using error diffusion, meaning it pushes (adds) the residual quantization error of a pixel onto its neighboring pixels, to be quantized after. It spreads the debt out according to the distribution (shown as a map of the neighboring pixels): [ ∗ 7 16 … … 3 16 5 16 1 16 … ] {\displaystyle {\begin{bmatrix}&&&{\frac {\displaystyle 7}{\displaystyle 16}}&\ldots \\\ldots &{\frac {\displaystyle 3}{\displaystyle 16}}&{\frac {\displaystyle 5}{\displaystyle 16}}&{\frac {\displaystyle 1}{\displaystyle 16}}&\ldots \\\end{bmatrix}}} The pixel indicated with a star () indicates the pixel currently being scanned, and the blank pixels are the previously scanned pixels. The specific values (7/16, 3/16, 5/16, 1/16) were originally found by trial-and-error, "guided by the desire to have a region of desired density 0.5 come out as a checkerboard pattern". The algorithm scans the image from left to right, top to bottom, quantizing pixel values one by one. Each time, the quantization error is transferred to the neighboring pixels, while not affecting the pixels that already have been quantized. Hence, if a number of pixels have been rounded downwards, it becomes more likely that the next pixel is rounded upwards, such that on average, the quantization error is close to zero. The diffusion coefficients have the property that if the original pixel values are exactly halfway in between the nearest available colors, the dithered result is a checkerboard pattern. For example, 50% grey data could be dithered as a black-and-white checkerboard pattern. For optimal dithering, the counting of quantization errors should be in sufficient accuracy to prevent rounding errors from affecting the result. For correct results, all values should be linearized first, rather than operating directly on sRGB values as is common for images stored on computers. In some implementations, the horizontal direction of scan alternates between lines; this is called "serpentine scanning" or boustrophedon transform dithering. The algorithm described above is in the following pseudocode. This works for any approximately linear encoding of pixel values, such as 8-bit integers, 16-bit integers or real numbers in the range [0, 1]. for each y from top to bottom do for each x from left to right do oldpixel := pixels[x][y] newpixel := find_closest_palette_color(oldpixel) pixels[x][y] := newpixel quant_error := oldpixel - newpixel pixels[x + 1][y ] := pixels[x + 1][y ] + quant_error × 7 / 16 pixels[x - 1][y + 1] := pixels[x - 1][y + 1] + quant_error × 3 / 16 pixels[x ][y + 1] := pixels[x ][y + 1] + quant_error × 5 / 16 pixels[x + 1][y + 1] := pixels[x + 1][y + 1] + quant_error × 1 / 16 When converting grayscale pixel values from a high to a low bit depth (e.g. 8-bit grayscale to 1-bit black-and-white), find_closest_palette_color() may perform just a simple rounding, for example: find_closest_palette_color(oldpixel) = round(oldpixel / 255) The pseudocode can result in pixel values exceeding the valid values (such as greater than 255 in 8-bit grayscale images). Such values should ideally be handled by the find_closest_palette_color() function, rather than clipping the intermediate values, since a subsequent error may bring the value back into range. However, if fixed-width integers are used, wrapping of intermediate values would cause inversion of black and white, and so should be avoided. The find_closest_palette_color() implementation is nontrivial for a palette that is not evenly distributed, however small inaccuracies in selecting the correct palette color have minimal visual impact due to error being propagated to future pixels. A nearest neighbor search in 3D is frequently used.

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  • Top 10 AI Resume Builders Compared (2026)

    Top 10 AI Resume Builders Compared (2026)

    In search of the best AI resume builder? An AI resume builder is software that uses machine learning to help you get more done — it turns a rough idea into a polished result in seconds. When choosing one, weigh output quality, pricing, export formats, and how well it fits the tools you already use. Whether you are a beginner or a pro, the right AI resume builder slots into your workflow and pays for itself fast. We tested the leading options and ranked them by quality, value, and ease of use.

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  • Wolfgang Ketter

    Wolfgang Ketter

    Wolfgang Ketter (born Traben-Trarbach, Germany, 1972) is Chaired Professor of Information Systems for a Sustainable Society at the University of Cologne. and a prominent scientist in the application of artificial intelligence, machine learning and intelligent agents in the design of smart markets, including demand response mechanisms and in particular automated auctions. He is a co-founder of the open energy system platform Power TAC, an automated retail electricity trading platform that simulates the performance of retail markets in an increasingly prosumer- and renewable-energy-influenced electricity landscape. == Career == === Advisory roles === Ketter is an advisor on the energy transition to the German government, in particular, the energy-intensive German state of North Rhine-Westphalia. He is also a fellow of the World Economic Forum and member of the WEF Global Council on Future Mobility and the Global New Mobility Coalition, contributing on the use of AI and machine learning to address issues arising from growth in electrification of energy such as the use of batteries as virtual power plants, the management of electric vehicle charging to prevent grid congestion, or the potential for peer-to-peer electricity trading. Ketter has also been an advisor for over a decade to the Port of Rotterdam on the design of energy cooperatives and energy trading platforms as well as one of the largest auction companies in the world, Royal FloraHolland, where his initial research led to a redesign of auction mechanisms and decision support systems. The cumulative research project team received the Association for Information Systems Impact Award in 2020 === Research === Ketter’s research is multidisciplinary, addressing the overlap of AI and ML in the economics of retail energy and mobility markets. The industry and policy applications of his research interconnect in large-scale projects such as the EU Smart city development project Ruggedised, for which the Erasmus University-based team's publication on the optimization of the City of Rotterdam's electric transit bus network was recognized with the Institute for Operations Research and the Management Sciences Daniel H. Wagner runner-up award. His research focuses on the use of competitive benchmarking and intelligent agents in virtual world simulations of retail energy markets as part of a smart grid. A small-scale version of the Power TAC project led to a publication on demand side management, 'A simulation of household behavior under variable prices' that has several hundred citations in publications representing a variety of scientific disciplines. Two of his publications in the Management Information Systems Quarterly journal and one in Energy Economics form the foundation for the current Power TAC platform. In 2016 and 2019 he was Chair of the Workshop on Information Technologies and Systems. Ketter is Coordinator of the Key Research Initiative Sustainable Smart Energy & Mobility at the University of Cologne, where he is a chaired Professor of Information Systems for a Sustainable Society. At the Rotterdam School of Management, Erasmus University, he is Professor of Next Generation Information Systems as well as Director of the Erasmus Centre for Future Energy Business and Academic Director of Smart Cities and Smart Energy at the Erasmus Centre of Data Analytics. He has been a visiting professor at the Haas School of Business and Berkeley Institute of Data Science, University of California at Berkeley in 2016 to 2017.

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  • Language and Computers

    Language and Computers

    Language and Computers: Studies in Practical Linguistics (ISSN 0921-5034) is a book series on corpus linguistics and related areas. As studies in linguistics, volumes in the series have, by definition, their foundations in linguistic theory; however, they are not concerned with theory for theory's sake, but always with a definite direct or indirect interest in the possibilities of practical application in the dynamic area where language and computers meet. The book series was founded in 1988, and is published by Brill|Rodopi. == Editors == Christian Mair Charles F. Meyer == Volumes == Volumes include: # 77. English Corpus Linguistics: Variation in Time, Space and Genre. Selected papers from ICAME 32., Edited by Gisle Andersen and Kristin Bech. ISBN 978-90-420-3679-6 E-ISBN 978-94-012-0940-3 # 76. English Corpus Linguistics: Crossing Paths., Edited by Merja Kytö. ISBN 978-90-420-3518-8 E-ISBN 978-94-012-0793-5 # 75. Corpus Linguistics and Variation in English.Theory and Description., Edited by Joybrato Mukherjee and Magnus Huber. ISBN 978-90-420-3495-2 E-ISBN 978-94-012-0771-3 # 74. English Corpus Linguistics: Looking back, Moving forward. Papers from the 30th International Conference on English Language Research on Computerized Corpora (ICAME 30), Lancaster, UK, 27–31 May 2009., Edited by Sebastian Hoffmann, Paul Rayson and Geoffrey Leech. ISBN 978-90-420-3466-2 E-ISBN 978-94-012-0747-8 #73. Corpus-based Studies in Language Use, Language Learning, and Language Documentation., Edited by John Newman, Harald Baayen and Sally Rice. ISBN 978-90-420-3401-3 E-ISBN 978-94-012-0688-4 #72. The Progressive in Modern English. A Corpus-Based Study of Grammaticalization and Related Changes., by Svenja Kranich. ISBN 978-90-420-3143-2 E-ISBN 978-90-420-3144-9 #71. Corpus-linguistic applications. Current studies, new directions, Edited by Stefan Th. Gries, Stefanie Wulff, and Mark Davies.. ISBN 978-90-420-2800-5 #70. A resource-light approach to morpho-syntactic tagging., by Anna Feldman and Jirka Hana. ISBN 978-90-420-2768-8 #69. Corpus Linguistics. Refinements and Reassessments., Edited by Antoinette Renouf and Andrew Kehoe. ISBN 978-90-420-2597-4 #68. Corpora: Pragmatics and Discourse. Papers from the 29th International Conference on English Language Research on Computerized Corpora (ICAME 29). Ascona, Switzerland, 14–18 May 2008., Edited by Andreas H. Jucker, Daniel Schreier and Marianne Hundt. ISBN 978-90-420-2592-9 #67. Modals and Quasi-modals in English., by Peter Collins. ISBN 978-90-420-2532-5 #66. Linking up contrastive and learner corpus research., Edited by Gaëtanelle Gilquin, Szilvia Papp and María Belén Díez-Bedmar. ISBN 978-90-420-2446-5 #64. Language, People, Numbers. Corpus Linguistics and Society., Edited by Andrea Gerbig and Oliver Mason. ISBN 978-90-420-2350-5 #63. Variation and change in the lexicon. A corpus-based analysis of adjectives in English ending in –ic and –ical. , by Mark Kaunisto. ISBN 978-90-420-2233-1 #62. Corpus Linguistics 25 Years on., Edited by Roberta Facchinetti. ISBN 978-90-420-2195-2 #61. Corpora in the Foreign Language Classroom. Selected papers from the Sixth International Conference on Teaching and Language Corpora (TaLC 6), Edited by Encarnación Hidalgo, Luis Quereda and Juan Santana. ISBN 978-90-420-2142-6 #60. Corpus Linguistics Beyond the Word. Corpus Research from Phrase to Discourse, Edited by Eileen Fitzpatrick. ISBN 978-90-420-2135-8 #59. Corpus Linguistics and the Web., Edited by Marianne Hundt, Nadja Nesselhauf and Carolin Biewer. ISBN 978-90-420-2128-0 #58. English mediopassive constructions. A cognitive, corpus-based study of their origin, spread, and current status, by Marianne Hundt. ISBN 978-90-420-2127-3 / ISBN 90-420-2127-6

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  • Amira (software)

    Amira (software)

    Amira (ah-MEER-ah) is a software platform for visualization, processing, and analysis of 3D and 4D data. It is being actively developed by Thermo Fisher Scientific in collaboration with the Zuse Institute Berlin (ZIB), and commercially distributed by Thermo Fisher Scientific — together with its sister software Avizo. == Overview == Amira is an extendable software system for scientific visualization, data analysis, and presentation of 3D and 4D data. It is used by researchers and engineers in academia and industry. It is a tool for processing, analysis and visualization of data from various modalities; e.g. micro-CT, PET, Ultrasound. It is used in many fields, such as microscopy in biology and materials science, molecular biology, quantum physics, astrophysics, computational fluid dynamics (CFD), finite element modeling (FEM), non-destructive testing (NDT), and many more. One of the key features, besides data visualization, is Amira's set of tools for image segmentation and geometry reconstruction. This allows the user to mark (or segment) structures and regions of interest in 3D image volumes using automatic, semi-automatic, and manual tools. The segmentation can then be used for a variety of subsequent tasks, such as volumetric analysis, density analysis, shape analysis, or the generation of 3D computer models for visualization, numerical simulations, or rapid prototyping or 3D printing. Other key Amira features are multi-planar and volume visualization, image registration, filament tracing, cell separation and analysis, tetrahedral mesh generation, fiber-tracking from diffusion tensor imaging (DTI) data, skeletonization, spatial graph analysis, and stereoscopic rendering of 3D data over multiple displays and immersive virtual reality environments, including CAVEs. As a commercial product Amira requires the purchase of a license or an academic subscription. A time-limited, but full-featured evaluation version is available for download free of charge. == History == === 1993–1998: Research software === Amira's roots go back to 1993 and the Department for Scientific Visualization, headed by Hans-Christian Hege at the Zuse Institute Berlin (ZIB). The ZIB is a research institute for mathematics and informatics. The Scientific Visualization department's mission is to help solve computationally and scientifically challenging tasks in medicine, biology, engineering and materials science. For this purpose, it develops algorithms and software for 2D, 3D, and 4D data visualization and visually supported exploration and analysis. At that time, the young visualization group at the ZIB had experience with the extendable, data flow-oriented visualization environments apE, IRIS Explorer, and Advanced Visualization Studio (AVS), but was not satisfied with these products' interactivity, flexibility, and ease-of-use for non-computer scientists. Therefore, the development of a new software system was started in a research project within a medically oriented, multi-disciplinary collaborative research center. Based on experiences that Tobias Höllerer had gained in late 1993 with the new graphics library IRIS Inventor, it was decided to utilize that library. The development of the medical planning system was performed by Detlev Stalling, who later became the chief software architect of Amira. The new software was called "HyperPlan", highlighting its initial target application – a planning system for hyperthermia cancer treatment. The system was being developed on Silicon Graphics (SGI) computers, which at the time were the standard workstations used for high-end graphics computing. The software was based on libraries such as OpenGL (originally IRIS GL), Open Inventor (originally IRIS Inventor), and the graphical user interface libraries X11, Motif (software), and ViewKit. In 1998, X11/Motif/Viewkit were replaced by the Qt toolkit. The HyperPlan framework served as the base for more and more projects at the ZIB and was used by a growing number of researchers in collaborating institutions. The projects included applications in medical image computing, medical visualization, neurobiology, confocal microscopy, flow visualization, molecular analytics and computational astrophysics. === 1998–today: Commercially supported product === The growing number of users of the system started to exceed the capacities that ZIB could spare for software distribution and support, as ZIB's primary mission was algorithmic research. Therefore, the spin-off company Indeed – Visual Concepts GmbH was founded by Hans-Christian Hege, Detlev Stalling, and Malte Westerhoff. In Feb 1998 the HyperPlan software was given the new, application-neutral name "Amira". This name is not an acronym, but was chosen for being pronounceable in different languages and providing a suitable connotation, namely "to look at" or "to wonder at", from the Latin verb "admirare" (to admire), which reflects a basic situation in data visualization. A major re-design of the software was undertaken by Detlev Stalling and Malte Westerhoff in order to make it a commercially supportable product and to make it available on non-SGI computers as well. In March 1999, the first version of the commercial Amira was exhibited at the CeBIT tradeshow in Hannover, Germany on SGI IRIX and Hewlett-Packard UniX (HP-UX) booths. Versions for Linux and Microsoft Windows followed within the following twelve months. Later Mac OS X support was added. Indeed – Visual Concepts GmbH selected the Bordeaux, France and San Diego, United States based company TGS, Inc. as the worldwide distributor for Amira and completed five major releases (up to version 3.1) in the subsequent four years. In 2003 both Indeed – Visual Concepts GmbH, as well as TGS, Inc. were acquired by Massachusetts-based Mercury Computer Systems, Inc. (NASDAQ:MRCY) and became part of Mercury's newly formed life sciences business unit, later branded Visage Imaging. In 2009, Mercury Computer Systems, Inc. spun off Visage Imaging again and sold it to Melbourne, Australia based Promedicus Ltd (ASX:PME), a leading provider of radiology information systems and medical IT solutions. During this time, Amira continued to be developed in Berlin, Germany and in close collaboration with the ZIB, still headed by the original creators of Amira. TGS, located in Bordeaux, France was sold by Mercury Computer systems to a French investor and renamed to Visualization Sciences Group (VSG). VSG continued the work on a complementary product named Avizo, based on the same source code but customized for material sciences. In August 2012, FEI, to that date the largest OEM reseller of Amira, purchased VSG and the Amira business from Promedicus. This brought the two software sisters Amira and Avizo back into one hand. In August 2013, Visualization Sciences Group (VSG) became a business unit of FEI. In 2016 FEI has been bought by Thermo Fisher Scientific and became part of its Materials & Structural Analysis division in early 2017. Amira and Avizo are still being marketed as two different products; Amira for life sciences and Avizo for materials science, but the development efforts are now joined once again. In the meantime, the number of scientific articles using the Amira / Avizo software, is in the order of 10 thousands. == Amira options == === Microscopy option === Specific readers for microscopy data Image deconvolution Exploration of 3D imagery obtained from virtually any microscope Extraction and editing of filament networks from microscopy images === DICOM reader === Import of clinical and preclinical data in DICOM format === Mesh option === Generation of 3D finite element (FE) meshes from segmented image data Support for many state-of-the-art FE solver formats High-quality visualization of simulation mesh-based results, using scalar, vector, and tensor field display modules === Skeletonization option === Reconstruction and analysis of neural and vascular networks Visualization of skeletonized networks Length and diameter quantification of network segments Ordering of segments in a tree graph Skeletonization of very large image stacks === Molecular option === Advanced tools for the visualization of molecule models Hardware-accelerated volume rendering Powerful molecule editor Specific tools for complex molecular visualization === Developer option === Creation of new custom components for visualizing or data processing Implementation of new file readers or writers C++ programming language Development wizard for getting started quickly === Neuro option === Medical image analysis for DTI and brain perfusion Fiber tracking supporting several stream-line based algorithms Fiber separation into fiber bundles based on user defined source and destination regions Computation of tensor fields, diffusion weighted maps Eigenvalue decomposition of tensor fields Computation of mean transit time, cerebral blood flow, and cerebral blood volume === VR option === Visualization of data on large tiled displays

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  • Aravind Joshi

    Aravind Joshi

    Aravind Krishna Joshi (August 5, 1929 – December 31, 2017) was the Henry Salvatori Professor of Computer and Cognitive Science in the computer science department of the University of Pennsylvania. Joshi defined the tree-adjoining grammar formalism which is often used in computational linguistics and natural language processing. Joshi studied at Pune University and the Indian Institute of Science, where he was awarded a BE in electrical engineering and a DIISc in communication engineering respectively. Joshi's graduate work was done in the electrical engineering department at the University of Pennsylvania, and he was awarded his PhD in 1960. He became a professor at Penn and was the co-founder and co-director of the Institute for Research in Cognitive Science. == Awards and recognitions == Guggenheim fellow, 1971–72 Fellow of the Institute of Electrical and Electronics Engineers (IEEE), 1976 Best Paper Award at the National Conference on Artificial Intelligence, 1987 Founding Fellow of the American Association for Artificial Intelligence (AAAI), 1990 IJCAI Award for Research Excellence, 1997 Fellow of the Association for Computing Machinery, 1998 Elected to the National Academy of Engineering, 1999 First to be awarded the Association for Computational Linguistics Lifetime Achievement Award at the 40th anniversary meeting of the ACL, 2002 Awarded the Rumelhart Prize, 2003 Benjamin Franklin Medal in Computer and Cognitive Science, 2005 Doctor honoris causa of mathematical and physical sciences, Charles University in Prague, October 30, 2013 S.-Y. Kuroda Prize of the SIG Mathematics of Language of the ACL, 2013 === Awarded history === On April 21, 2005, Joshi was awarded the Franklin Institute's Benjamin Franklin Medal in Computer and Cognitive Science. The Franklin Institute citation states that he was awarded the medal "for his fundamental contributions to our understanding of how language is represented in the mind, and for developing techniques that enable computers to process efficiently the wide range of human languages. These advances have led to new methods for computer translation."

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  • Additive smoothing

    Additive smoothing

    In statistics, additive smoothing, also called Laplace smoothing or Lidstone smoothing, is a technique used to smooth count data, eliminating issues caused by certain values having 0 occurrences. Given a set of observation counts x = ⟨ x 1 , x 2 , … , x d ⟩ {\displaystyle \mathbf {x} =\langle x_{1},x_{2},\ldots ,x_{d}\rangle } from a d {\displaystyle d} -dimensional multinomial distribution with N {\displaystyle N} trials, a "smoothed" version of the counts gives the estimator θ ^ i = x i + α N + α d ( i = 1 , … , d ) , {\displaystyle {\hat {\theta }}_{i}={\frac {x_{i}+\alpha }{N+\alpha d}}\qquad (i=1,\ldots ,d),} where the smoothed count x ^ i = N θ ^ i {\displaystyle {\hat {x}}_{i}=N{\hat {\theta }}_{i}} , and the "pseudocount" α > 0 is a smoothing parameter, with α = 0 corresponding to no smoothing (this parameter is explained in § Pseudocount below). Additive smoothing is a type of shrinkage estimator, as the resulting estimate will be between the empirical probability (relative frequency) x i / N {\displaystyle x_{i}/N} and the uniform probability 1 / d . {\displaystyle 1/d.} Common choices for α are 0 (no smoothing), +1⁄2 (the Jeffreys prior), or 1 (Laplace's rule of succession), but the parameter may also be set empirically based on the observed data. From a Bayesian point of view, this corresponds to the expected value of the posterior distribution, using a symmetric Dirichlet distribution with parameter α as a prior distribution. In the special case where the number of categories is 2, this is equivalent to using a beta distribution as the conjugate prior for the parameters of the binomial distribution. == History == Laplace came up with this smoothing technique when he tried to estimate the chance that the sun will rise tomorrow. His rationale was that even given a large sample of days with the rising sun, we still can not be completely sure that the sun will still rise tomorrow (known as the sunrise problem). == Pseudocount == A pseudocount is an amount (not generally an integer, despite its name) added to the number of observed cases in order to change the expected probability in a model of those data, when not known to be zero. It is so named because, roughly speaking, a pseudo-count of value α {\displaystyle \alpha } weighs into the posterior distribution similarly to each category having an additional count of α {\displaystyle \alpha } . If the number of occurrences of each item i {\displaystyle i} is x i {\displaystyle x_{i}} out of N {\displaystyle N} samples, the empirical probability of event i {\displaystyle i} is p i , empirical = x i N , {\displaystyle p_{i,{\text{empirical}}}={\frac {x_{i}}{N}},} but the posterior probability when additively smoothed is p i , α -smoothed = x i + α N + α d , {\displaystyle p_{i,\alpha {\text{-smoothed}}}={\frac {x_{i}+\alpha }{N+\alpha d}},} as if to increase each count x i {\displaystyle x_{i}} by α {\displaystyle \alpha } a priori. Depending on the prior knowledge, which is sometimes a subjective value, a pseudocount may have any non-negative finite value. It may only be zero (or the possibility ignored) if impossible by definition, such as the possibility of a decimal digit of π being a letter, or a physical possibility that would be rejected and so not counted, such as a computer printing a letter when a valid program for π is run, or excluded and not counted because of no interest, such as if only interested in the zeros and ones. Generally, there is also a possibility that no value may be computable or observable in a finite time (see the halting problem). But at least one possibility must have a non-zero pseudocount, otherwise no prediction could be computed before the first observation. The relative values of pseudocounts represent the relative prior expected probabilities of their possibilities. The sum of the pseudocounts, which may be very large, represents the estimated weight of the prior knowledge compared with all the actual observations (one for each) when determining the expected probability. In any observed data set or sample there is the possibility, especially with low-probability events and with small data sets, of a possible event not occurring. Its observed frequency is therefore zero, apparently implying a probability of zero. This oversimplification is inaccurate and often unhelpful, particularly in probability-based machine learning techniques such as artificial neural networks and hidden Markov models. By artificially adjusting the probability of rare (but not impossible) events so those probabilities are not exactly zero, zero-frequency problems are avoided. Also see Cromwell's rule. === Choice of pseudocount === ==== Weakly informative prior ==== One common approach is to add 1 to each observed number of events, including the zero-count possibilities. This is sometimes called Laplace's rule of succession. This approach is equivalent to assuming a uniform prior distribution over the probabilities for each possible event (spanning the simplex where each probability is between 0 and 1, and they all sum to 1). Using the Jeffreys prior approach, a pseudocount of one half should be added to each possible outcome. Pseudocounts should be set to one or one-half only when there is no prior knowledge at all – see the principle of indifference. However, given appropriate prior knowledge, the sum should be adjusted in proportion to the expectation that the prior probabilities should be considered correct, despite evidence to the contrary – see further analysis. Higher values are appropriate inasmuch as there is prior knowledge of the true values (for a mint-condition coin, say); lower values inasmuch as there is prior knowledge that there is probable bias, but of unknown degree (for a bent coin, say). ==== Frequentist interval ==== One way to motivate pseudocounts, particularly for binomial data, is via a formula for the midpoint of an interval estimate, particularly a binomial proportion confidence interval. The best-known is due to Edwin Bidwell Wilson, in Wilson (1927): the midpoint of the Wilson score interval corresponding to ⁠ z {\displaystyle z} ⁠ standard deviations on either side is n S + z n + 2 z {\displaystyle {\frac {n_{S}+z}{n+2z}}} Taking z = 2 {\displaystyle z=2} standard deviations to approximate a 95% confidence interval (⁠ z ≈ 1.96 {\displaystyle z\approx 1.96} ⁠) yields pseudocount of 2 for each outcome, so 4 in total, colloquially known as the "plus four rule": n S + 2 n + 4 {\displaystyle {\frac {n_{S}+2}{n+4}}} This is also the midpoint of the Agresti–Coull interval (Agresti & Coull 1998). ==== Known incidence rates ==== Often the bias of an unknown trial population is tested against a control population with known parameters (incidence rates) μ = ⟨ μ 1 , μ 2 , … , μ d ⟩ . {\displaystyle {\boldsymbol {\mu }}=\langle \mu _{1},\mu _{2},\ldots ,\mu _{d}\rangle .} In this case the uniform probability 1 / d {\displaystyle 1/d} should be replaced by the known incidence rate of the control population μ i {\displaystyle \mu _{i}} to calculate the smoothed estimator: θ ^ i = x i + μ i α d N + α d ( i = 1 , … , d ) . {\displaystyle {\hat {\theta }}_{i}={\frac {x_{i}+\mu _{i}\alpha d}{N+\alpha d}}\qquad (i=1,\ldots ,d).} As a consistency check, if the empirical estimator happens to equal the incidence rate, i.e. μ i = x i / N , {\displaystyle \mu _{i}=x_{i}/N,} the smoothed estimator is independent of α {\displaystyle \alpha } and also equals the incidence rate. == Applications == === Classification === Additive smoothing is commonly a component of naive Bayes classifiers. === Statistical language modelling === In a bag of words model of natural language processing and information retrieval, the data consists of the number of occurrences of each word in a document. Additive smoothing allows the assignment of non-zero probabilities to words which do not occur in the sample. Studies have shown that additive smoothing is more effective than other probability smoothing methods in several retrieval tasks such as language-model-based pseudo-relevance feedback and recommender systems.

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  • AI Video Editors: Free vs Paid (2026)

    AI Video Editors: Free vs Paid (2026)

    Trying to pick the best AI video editor? An AI video editor is software that uses machine learning to help you get more done — it scales effortlessly from a single task to thousands. The best picks balance beginner-friendly simplicity with the depth power users need, and they ship updates often. Whether you are a beginner or a pro, the right AI video editor slots into your workflow and pays for itself fast. This guide breaks down the top picks, their pros and cons, and who each one is best for.

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  • Abiquo Enterprise Edition

    Abiquo Enterprise Edition

    Abiquo Hybrid Cloud Management Platform is a web-based cloud computing software platform developed by Abiquo. Written entirely in Java, it is used to build, integrate and manage public and private clouds in homogeneous environments. Users can deploy and manage servers, storage system and network and virtual devices. It also supports LDAP integration. == Hypervisors == Abiquo supports five hypervisor systems. VMware ESXi Microsoft Hyper-V Citrix XenServer Oracle VM Server for x86 KVM From version 3.1, it also supports multiple public cloud providers: Amazon AWS Rackspace Google Compute Engine HP Cloud ElasticHosts DigitalOcean Abiquo version 3.2 added: Microsoft Azure Abiquo version 3.4 added: Support for Docker hosts, adding multi-tenant networking, storage management and private registry management for Docker SoftLayer CloudSigma Later versions continued to add features including autoscaling on any cloud, integration to VMware NSX and OpenStack Neutron for software defined networking, guest config with cloud-init and integrated monitoring driving guest automation. == Storage services == Abiquo supports any vendor for hypervisor storage, and also supports tiered storage pools, enabling storage-as-a-service from specific vendors and technologies including: NFS Generic iSCSI NetApp Nexenta == SAAS version == In April 2014 Abiquo launched Abiquo anyCloud, a SAAS version of the Abiquo Hybrid Cloud Platform software. This version lets users manage public cloud resources from: Amazon AWS Microsoft Azure IBM SoftLayer DigitalOcean Rackspace Open Cloud (an OpenStack cloud) HP Public Cloud (an OpenStack cloud) Google Compute Engine ElasticHosts Additional security and process features include workflow, to have an enterprise administrator electronically sign off on changes, an audit trail of activity and the ability to share cloud accounts among and enterprise team in a secure way. == Reviews and awards == Finalist for the 2015 Cloud Awards Finalist for the 2015 UK Cloud Awards in the category Cloud Management Product of the Year EMA Radar for Private Cloud platforms 2013 Global Telecoms Business Innovation Summit and Awards 2013 (with Interoute) EuroCloud UK Awards

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  • David Horn (Israeli physicist)

    David Horn (Israeli physicist)

    David Horn (Hebrew: דוד הורן; born 10 September 1937) is a Professor (Emeritus) of Physics in the School of Physics and Astronomy at Tel Aviv University (TAU), Israel. He has served as Vice-Rector of TAU, Chairman of the School of Physics and Astronomy and as Dean of the Faculty of Exact Sciences in TAU. He is a fellow of the American Physical Society, nominated for "contributions to theoretical particle physics, including the seminal work on finite energy sum rules, research of the phenomenology of hadronic processes, and investigation of Hamiltonian lattice theories". == Early life and education == David Horn was born and educated in Haifa. He graduated from the Reali School in 1955. He began his academic studies in Physics at the Technion in Haifa in 1957, and received his B.Sc. (Summa Cum Laude) in 1961, and M.Sc. in 1962. He continued his Ph.D. studies at the Hebrew University of Jerusalem until 1965. His thesis on "Some Aspects of the Structure of Weak Interactions" was supervised by Prof. Yuval Ne'eman. == Career == Horn joined the newly founded Tel Aviv University as an assistant in 1962. He became a lecturer in 1965, a senior lecturer in 1967 and an associate professor in 1968. He was promoted to full professor of Physics in 1972. In 1974 he became the incumbent of the Edouard and Francoise Jaupart Chair of Theoretical Physics of Particles and Fields, a position he held until 2007. Horn has supervised 43 graduate students at TAU and authored over 240 scientific publications. He retired as a professor emeritus in 2005, and continues to be an active researcher. Horn spent a significant part of his career holding visiting academic positions at other universities and research institutes, including: Postdoctoral Fellow at Argonne National Lab, ILL, Research Fellow and three times Visiting Associate at California Institute of Technology, Pasadena, CA, Visitor at CERN in Geneva, Visiting Professor at Cornell University, NY, Member of the Institute for Advanced Study, Princeton, NJ, Visiting Professor at SLAC in Stanford University, CA, and Visiting Professor at Kyoto University, Japan. Beginning from 1980, Horn held official positions at Tel Aviv University, starting with tenure as Vice-Rector (1980-1983), a position he left for research at SLAC. After returning he was nominated Chairman of the Department of High Energy Physics (1984-1986), followed by tenures as Chairman of the School of Physics and Astronomy (1986-9), Dean of the Raymond and Beverly Sackler Faculty of Exact Sciences (1990-1995), and first Director of the Adams Super Center for Brain Studies (1993-2000). Horn has also held national and international professional positions. He was Chairman of the Israel Commission for High Energy Physics (1983-2003), and, in this capacity, served as an Israeli observer of the council of CERN (1991-2003). He served as member of the Israel Council for Higher Education (1987-1991), member of the Executive Committee of the European Physical Society (1989-1992) and member of the European Strategy Forum on Research Infrastructures (2005-2017). He chaired the Israeli Committee of Research Infrastructures (2012-2016), issuing roadmaps for scientific RI in 2013 and 2016. == Research == Horn's research work focused on theory and phenomenology of High Energy Physics until 1990. He then shifted his interests to Neural Computation and Machine Learning and, since 2005, he has also published in Bioinformatics. Together with Richard Dolen and Christoph Schmid he discovered the Finite Energy Sum Rules in 1967. It was a realization of the bootstrap approach to hadronic structure, and became known as the Dolen-Horn-Schmid Duality. Together with Richard Silver he investigated a model of coherent production of pions at high energy hadron collisions in 1971, and together with Jeffrey Mandula he undertook the investigation of mesons with constituent gluons in 1978. Moving to lattice gauge theories in 1979, he discovered, together with Shimon Yankielowic and Marvin Weinstein, a non-confining phase in Z(N) theories for large N. In 1981 he demonstrated the existence of finite matrix models with link gauge fields, nowadays known as quantum link models. In 1984 Horn and Weinstein developed the t-expansion methodology. Horn's contributions to neural modeling include a novel mechanism for memory maintenance via neuronal regulation in 1998, developed with Nir Levy and Eytan Ruppin and unsupervised learning of natural languages in 2005, a joint work with Zach Solan, Eytan Ruppin and Shimon Edelman, introducing novel algorithms for motif and grammar extraction from text. Horn has contributed to algorithms of clustering, an important topic in Machine Learning, by developing Support Vector Clustering (SVC) in 2001, together with Asa Ben Hur, Hava Siegelmann and Vladimir Vapnik. This was followed shortly thereafter by a joint work with Assaf Gottlieb on Quantum Clustering (QC). His contributions to Bioinformatics include motif descriptions of function and structure of proteins, as well as motif studies of genomic structures. Together with Erez Persi he studied compositional order of proteomes, and repeat instability of genomes, as evolution markers of organisms and of cancer (a joint work with Persi and others). == Honors == Horn is a Fellow of the American Physical Society (1985) and a Fellow of the Israel Physical Society (2018). == Publications == === Selected articles === R. Dolen, D. Horn and C. Schmid; Prediction of Regge-parameters of rho poles from low-energy pi-N scattering data Phys. Rev. Lett. 19 (1967) 402–407. Finite-Energy Sum Rules and Their Application to pi-N Charge Exchange Phys. Rev. 166 (1968) 1768–1781. D. Horn and R. Silver: Coherent production of pions, Annals Phys. 66 (1971) 509-541 T. Banks, D. Horn and H. Neuberger: Bosonization of the SU(N) Thirring Models, Nucl. Phys. B108, 119 (1976). D. Horn and J. Mandula: Model of Mesons with Constituent Gluons, Phys. Rev. D17, 898 (1978). D. Horn, M. Weinstein and S. Yankielowicz: Hamiltonian Approach to Z(N) Lattice Gauge Theories, Phys. Rev. D19, 3715 (1979). D. Horn: Finite Matrix Models with Continuous Local Gauge Invariance, Phys. Lett. 100B, 149-151 (1981). T. Banks, Y. Dothan and D. Horn: Geometric Fermions, Phys. Lett. 117B, 413 (1982). D. Horn and M. Weinstein: The t expansion: A nonperturbative analytic tool for Hamiltonian systems. Phys. Rev. D 30, 1256-1270 (1984). Ury Naftaly, Nathan Intrator and David Horn: Optimal Ensemble Averaging of Neural Networks. Network, Computation in Neural Systems, 8, 283-296 (1997). David Horn, Nir Levy, Eytan Ruppin: Memory Maintenance via Neuronal Regulation, Neural Computation, 10, 1-18 (1998). Asa Ben-Hur, David Horn, Hava Siegelmann and Vladimir Vapnik: Support Vector Clustering. Journal of Machine Learning Research 2, 125-137 (2001). David Horn and Assaf Gottlieb: Algorithm for data clustering in pattern recognition problems based on quantum mechanics, Phys. Rev. Lett. 88 (2002) 18702 Zach Solan, David Horn, Eytan Ruppin and Shimon Edelman: Unsupervised learning of natural languages, Proc. Natl. Acad. Sc. 102 (2005) 11629–11634. Vered Kunik, Yasmine Meroz, Zach Solan, Ben Sandbank, Uri Weingart, Eytan Ruppin and David Horn: Functional representation of enzymes by specific peptides. PLOS Computational Biology 2007, 3(8):e167. Benny Chor, David Horn, Yaron Levy, Nick Goldman and Tim Massingham: Genomic DNA k-mer spectra: models and modalities. Genome Biology 2009, 10(10):R108 Erez Persi and David Horn. Systematic Analysis of Compositional Order of Proteins Reveals New Characteristics of Biological Functions and a Universal Correlate of Macroevolution. PLoS Comput Biol 9 (2013): e1003346. David Horn. Taxa counting using Specific Peptides of Aminoacyl tRNA Synthetases Encyclopedia of Metagenomics, Springer, 2013. Sagi Shporer, Benny Chor, Saharon Rosset, David Horn. Inversion symmetry of DNA k-mer counts: validity and deviations. BMC Genomics 2016, 17:696 Erez Persi, Davide Prandi, Yuri I. Wolf, Yair Pozniak, Christopher Barbieri, Paola Gasperini, Himisha Beltran, Bishoy M. Faltas, Mark A. Rubin, Tamar Geiger, Eugene V. Koonin, Francesca Demichelis, David Horn. Proteomic and Genomic Signatures of Repeat Instability in Cancer and Adjacent Normal Tissues. PNAS 116, 34, 2019 - 08790 === Book === David Horn and Fredrick Zachariasen: Hadron Physics at Very High Energies. Benjamin 1973. === Patents === Method and Apparatus for Quantum Clustering. USA Patent No. 7,653,646 B2. Method for discovering relationships in data by dynamic quantum clustering USA Patent No 8874412 and USA Patent No. 9,646,074. == Personal life == Horn was married to Nira Fuss since 1963 until her death in 2019. He is a father of three, Yuval, Tamar, and Oded, and grandfather of nine. He lives in Tel Aviv, Israel.

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  • Optical braille recognition

    Optical braille recognition

    Optical braille recognition is technology to capture and process images of braille characters into natural language characters. It is used to convert braille documents for people who cannot read them into text, and for preservation and reproduction of the documents. == History == In 1984, a group of researchers at the Delft University of Technology designed a braille reading tablet, in which a reading head with photosensitive cells was moved along set of rulers to capture braille text line-by-line. In 1988, a group of French researchers at the Lille University of Science and Technology developed an algorithm, called Lectobraille, which converted braille documents into plain text. The system photographed the braille text with a low-resolution CCD camera, and used spatial filtering techniques, median filtering, erosion, and dilation to extract the braille. The braille characters were then converted to natural language using adaptive recognition. The Lectobraille technique had an error rate of 1%, and took an average processing time of seven seconds per line. In 1993, a group of researchers from the Katholieke Universiteit Leuven developed a system to recognize braille that had been scanned with a commercially available scanner. The system, however, was unable to handle deformities in the braille grid, so well-formed braille documents were required. In 1999, a group at the Hong Kong Polytechnic University implemented an optical braille recognition technique using edge detection to translate braille into English or Chinese text. In 2001, Murray and Dais created a handheld recognition system, that scanned small sections of a document at once. Because of the small area scanned at once, grid deformation was less of an issue, and a simpler, more efficient algorithm was employed. In 2003, Morgavi and Morando designed a system to recognize braille characters using artificial neural networks. This system was noted for its ability to handle image degradation more successfully than other approaches. == Challenges == Many of the challenges to successfully processing braille text arise from the nature of braille documents. Braille is generally printed on solid-color paper, with no ink to produce contrast between the raised characters and the background paper. However, imperfections in the page can appear in a scan or image of the page. Many documents are printed inter-point, meaning they are double-sided. As such, the depressions of the braille of one side appear interlaid with the protruding braille of the other side. == Techniques == Some optical braille recognition techniques attempt to use oblique lighting and a camera to reveal the shadows of the depressions and protrusions of the braille. Others make use of commercially available document scanners.

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