AI Essay Detector Grammarly

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  Pray.com

  Pray.com is a Christian social networking service and mobile application designed to facilitate religious communities. Launched in 2016, it was founded by Steve Gatena, Michael Lynn, Ryan Beck and Matthew Potter. The platform offers features for social networking, daily prayers, sermons, biblical content, and podcasts. The COVID-19 pandemic significantly increased Pray.com's user base, with downloads surging by 955%. During this period, the platform collaborated with churches to support virtual ministry services as in-person gatherings were restricted. The Federal Election Commission issued an opinion in 2021 that allows the platform to feature members of the United States Congress. Pray.com serves as a specialized social media platform for religious groups. Congregations can establish their own groups where members and leaders can participate in discussions, livestream services, and manage donations. Additionally, users can join "prayer communities" to post and respond to prayer requests. For those who subscribe to premium services, the platform provides access to biblically-inspired meditations and bedtime stories, and Bible stories for children. Pray.com also produces Radio drama-style productions with notable actors such as Kristen Bell and Blair Underwood narrating biblical stories. == History == === Funding and development === Pray.com has secured significant funding to support its development and growth. In 2017, the platform raised $2 million in seed funding from Science Inc., Greylock Partners, and Spark Capital. This was followed by a Series A funding round in March 2018, in which the company secured an additional $14 million from TPG Growth, Science Inc., and Greylock Partners. Founder Steve Gatena has highlighted difficulties in securing funding, noting some venture capitalists' negative attitudes towards faith-based technology. === Clinical studies === There have been clinical studies on Pray.com. In one study, the app was found to be acceptable and easy to use among racial and ethnic minority groups, with participants reporting improved mental health and well-being. Greater app use was associated with better outcomes, though low and variable usage suggests the need for further research to fully understand its impact. Another study examined Pray.com's impact on mental health by assigning 192 participants to use the app freely, use its meditative prayer function, or not use it at all. Over two months, participants reported overall improvements in mental health and well-being. Although no significant differences were found between groups, greater app usage correlated with better mental health outcomes. This suggests that religiously based mobile apps may help improve mental health and well-being. Another study of pray.com had similar findings. === National Day of Prayer === Pray first hosted a National Day of Prayer event in 2020 when it streamed to nearly one million viewers on Facebook. In 2021, Pray hosted a virtual event for the National Day of Prayer in the United States. The event featured remarks from public figures including United States President Joe Biden and former Vice President Mike Pence. President Biden spoke of his faith and prayed for an end to the COVID-19 pandemic. Biden remarked: "It means the world to me to know that there are people across the country who include Jill and me in their prayers. And I hope you know that you and your families are in our prayers as well. Today I am praying for the end of this great COVID crisis." The event featured musical performances from Gary Valenciano, Brooke Ligertwood from the Christian band Hillsong Worship, Lecrae, Heather Headley and Michael Neale. Other notable speakers included Ronnie Floyd, Ed Young, Mark Driscoll, and Samuel Rodriguez. Pray.com partnered with Sirius XM, DirecTV and Facebook to stream the event across multiple platforms. Pray.com was featured as a pop-up channel on Sirius XM, channel 154, to host the prayer event and celebrate people of all faith. === Partnerships and sponsorships === In 2024, Pray.com partnered with Sting Ray Robb as the primary sponsor for his No. 41 Chevrolet in the 2024 NTT IndyCar Series. The partnership, highlighting Robb's Christian faith, aims to engage younger audiences with faith-based content. The car, featuring Pray.com's branding, was set to debut at the Firestone Grand Prix of St. Petersburg. A partnership with Palantir Technologies for use of its AI systems was also announced in 2024. === Censorship in China === The app was removed from Apple's App Store in China as part of the country's broader efforts to restrict access to religious content. The app was targeted due to China's stringent regulations on religious material, particularly content distributed through digital platforms. The removal aligns with China's ongoing campaign to control online religious expression and maintain state-approved religious activities.

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  Anna Korhonen

  Anna-Leena Korhonen is a Finnish computer scientist who works in England as professor of natural language processing at the University of Cambridge, where she is co-director of the Language Technology Lab and the Institute for Technology and Humanity, fellow of the Alan Turing Institute, director of the Centre for Human Inspired Artificial Intelligence, fellow of the European Laboratory for Learning and Intelligent Systems, and a senior research fellow of Churchill College, Cambridge. Her research interests include natural language processing, the applications of natural language processing in health, and the social consequences of AI-based language tools. == Education and career == Korhonen studied linguistics as an undergraduate at the University of Helsinki. After a master's degree in linguistics at the University of Reading, she completed a Ph.D. in computer science at the University of Cambridge. Her 2002 doctoral dissertation, Subcategorization acquisition, was supervised by Ted Briscoe. After postdoctoral research at the University of Pennsylvania and at the National Institute of Informatics in Japan, she returned to Cambridge in 2005 as a senior research associate and Royal Society University Research Fellow. She became a reader in computational linguistics in 2014, professor of natural language processing in 2017, director of the Centre for Human Inspired Artificial Intelligence in 2022, and co-director of the Institute for Technology and Humanity in 2024. == Recognition == Korhonen was named as a Fellow of the Association for Computational Linguistics in 2023, "for significant contributions to lexical acquisition, multilingual and low resource NLP, socially beneficial language applications, and services to the ACL community". She was elected to the Academia Europaea in 2025.

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  Eric Brill

  Eric Brill is a computer scientist specializing in natural language processing. He created the Brill tagger, a supervised part of speech tagger. Another research paper of Brill introduced a machine learning technique now known as transformation-based learning. == Biography == Brill earned a BA in mathematics from the University of Chicago in 1987 and a MS in Computer Science from UT Austin in 1989. In 1994, he completed his PhD at the University of Pennsylvania. He was an assistant professor at Johns Hopkins University from 1994 to 1999. In 1999, he left JHU for Microsoft Research, he developed a system called "Ask MSR" that answered search engine queries written as questions in English, and was quoted in 2004 as predicting the shift of Google's web-page based search to information based search. In 2009 he moved to eBay to head their research laboratories.

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  Peter Gerstoft

  Peter Gerstoft is a Danish-American scientist and engineer specializing in ocean acoustics, seismology, and signal processing. He is currently a professor in the Department of Electrical and Photonics Engineering at the Technical University of Denmark. He was previously a Distinguished Data Scientist at the Scripps Institution of Oceanography at the University of California, San Diego and an adjunct professor in the Department of Electrical and Computer Engineering at UC San Diego. == Education == Gerstoft received his MSc in engineering from the Technical University of Denmark in 1983 and another MSc from the University of Western Ontario in 1984. He completed his PhD in engineering at the Technical University of Denmark in 1986. == Career == Gerstoft began his career in acoustics and vibrations at Odegaard & Danneskiold-Samsøe (1987–1992). He then served as a Senior Scientist at the NATO SACLANT Undersea Research Centre in La Spezia, Italy, from 1992 to 1997. Between 1999 and 2000, Gerstoft worked as a Senior Seismic Acoustic Officer with the Comprehensive Nuclear-Test-Ban Treaty Organization. He has been a Data Scientist at the Scripps Institution of Oceanography since 1997. From 2013, he held an adjunct faculty position in Electrical and Computer Engineering at UC San Diego, where he taught courses on seismology, data assimilation, and machine learning for physical systems. Gerstoft retired from UC San Diego in 2025 and accepted an appointment as Professor of Electrical and Photonics Engineering at the Technical University of Denmark in 2026 . == Research and contributions == Gerstoft's research focuses on environmental signal processing, with a particular emphasis on inversion methods, including their theoretical development, algorithmic implementation, and practical applications. In the 1990s, he investigated the use of nonlinear optimization and Bayesian approaches in acoustic inverse problems related to source localization and environmental parameter estimation. His work integrated physical propagation models with Bayesian sampling methods and a range of likelihood functions. These techniques have been applied to various data types, including vertical sensor arrays, single-sensor broadband data, and transmission loss measurements, and contributed to a general framework for inversion based on Gaussian assumptions. He has also conducted research in machine learning and sparse signal processing, particularly in the context of sensor array data. This includes applications such as direction of arrival estimation and source localization, including for seismic events such as the 2011 Tōhoku earthquake and for ship tracking in ocean environments. His work on sparse Bayesian sequential methods and techniques for estimating Lagrange multipliers in constrained optimization problems has contributed to the development of adaptive and high-resolution signal processing techniques. Gerstoft has applied supervised learning and deep neural networks to problems in physical acoustics, including source localization in ocean waveguides. He has also co-authored several review articles on the use of machine learning in acoustics and seismology. == Honors == Fulbright Scholar, Massachusetts Institute of Technology (1989–1990) Fellow, Acoustical Society of America (2003) Member, American Geophysical Union (since 2004) Senior Member, Institute of Electrical and Electronics Engineers (2018) Fellow, Institute of Electrical and Electronics Engineers (2023) == Selected publications == === Book === Diachok, O., Caiti, A., Gerstoft, P., & Schmidt, H. (Eds.). Full Field Inversion Methods in Ocean and Seismo-Acoustics. Kluwer Academic Publishers, 1995. === Selected articles === Gerstoft, P. (1994). "Inversion of seismo-acoustic data using genetic algorithms and a posteriori probability distributions". Journal of the Acoustical Society of America. 95 (2): 770–782. doi:10.1121/1.408467. Gerstoft, P., & Mecklenbrauker, C. F. (1998). "Ocean acoustic inversion with estimation of a posteriori probability distributions". Journal of the Acoustical Society of America. 104 (2): 808–819. doi:10.1121/1.423287. Sabra, K. G., Gerstoft, P., Roux, P., Kuperman, W. A., & Fehler, M. (2005). "Extracting time-domain Green's function estimates from ambient seismic noise". Geophysical Research Letters. 32, L03310. Xenaki, A., Gerstoft, P., & Mosegaard, K. (2014). "Compressive beamforming". Journal of the Acoustical Society of America. 136, 260–271. Niu, H., Reeves, D., & Gerstoft, P. (2017). "Source localization in an ocean waveguide using supervised machine learning". Journal of the Acoustical Society of America. 142, 1176–1188.

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  Spike (application)

  Spike is a cross-platform email client and AI-powered communication app, available on Windows, MacOS, iOS, Android and the web. It has a chat-like, conversational view for emails with AI-powered inbox management and integrated collaboration features. Depending on the selected plan, it can be used solely as an email application or as a full suite of business communication tools. == History == Founded in 2013 by Erez Pilosof and Dvir Ben-Aroya, Spike is a software application that puts existing e-mails into a multimedia messaging, chat-like interface enhanced with video and voice calls. The application was initially named Hop. In 2019, the developers completed a $5 million funding round including investment from Wix.com and NFX Capital. In 2020, Spike raised $8m in a Series A funding round led by Insight Partners with the participation from previous rounds' investors. In 2021 Spike announced a collaboration with Meta to launch on the Oculus Store and would become one of the first productivity apps to launch in Meta's new virtual world, known as the Metaverse. In June 2023, the company introduced its corporate offering — Teamspace, a corporate communication platform for teams with features such as company-wide channels for broad conversations, private groups for specific topics or projects, direct one-on-one conversations, video meetings, file collaboration, AI-powered email messaging, and custom email domain. It supports file management, search capabilities, and project management. Built on open-protocol technology, Spike Teamspace enables users to send and receive messages from all email providers. Regardless of whether the other party is using Spike. == Company operations == Spike is developed and operated by SpikeNow LTD. Dvir Ben Aroya serves as Spike’s CEO and Erez Pilosof is the CTO. The company is headquartered in Tel Aviv, Israel. == Mode of use == The app enables users to organize email into three types of "conversations,"a traditional inbox/sent format, by subject, or by people. Spike users can also make audio and video calls to each other, and other features include a calendar, contact list, and Groups. Spike is available for Microsoft Windows, MacOS, iOS and Android, and as a web version, and works with Gmail, Outlook, Exchange, iCloud, Yahoo! Mail and IMAP email providers. == Features == Since 2023, the platform features an AI-driven assistant, Magic AI, for customized email creation, document summarization, research, content generation, advanced note-taking, project management, and real-time translation. Since 2023, Spike offers custom email domain management. It supports team collaboration through Channels, uniting members globally with access to historical messages, and combines email with real-time messaging via Conversational Email. The Shared Inbox allows team collaboration on emails, while Groups support private conversations and invitations. It also features integrated video meetings, real-time collaboration on documents and notes, and email hosting with custom domains. Super Search enables retrieval of various content, and the Priority Inbox organizes emails by priority. Collaborative Tasks offer real-time updates and tracking. The platform allows voice message sending from mobile devices and integrates multiple calendar platforms into a unified schedule. File Management optimizes attachment handling, and the Unified Inbox consolidates emails from multiple accounts. Spike ensures data security with AES-256 encryption and private keys. The platform features AI-powered inbox management and communication tools. In May 2025, Spike launched its AI Feed feature, which automatically summarizes unread messages in a unified stream and enables bulk email actions. Additional AI capabilities include email composition assistance, document summarization, content generation, note-taking enhancement, and real-time translation.

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  Cobham's theorem

  Cobham's theorem is a theorem in combinatorics on words that has important connections with number theory, notably transcendental numbers, and automata theory. Informally, the theorem gives the condition for the members of a set S of natural numbers written in bases b1 and base b2 to be recognised by finite automata. Specifically, consider bases b1 and b2 such that they are not powers of the same integer. Cobham's theorem states that S written in bases b1 and b2 is recognised by finite automata if and only if S differs by a finite set from a finite union of arithmetic progressions. The theorem was proved by Alan Cobham in 1969 and has since given rise to many extensions and generalisations. == Definitions == Let n > 0 {\displaystyle n>0} be an integer. The representation of a natural number n {\textstyle n} in base b {\textstyle b} is the sequence of digits n 0 n 1 ⋯ n h {\displaystyle n_{0}n_{1}\cdots n_{h}} such that n = n 0 + n 1 b + ⋯ + n h b h {\displaystyle n=n_{0}+n_{1}b+\cdots +n_{h}b^{h}} where 0 ≤ n 0 , n 1 , … , n h < b {\displaystyle 0\leq n_{0},n_{1},\ldots ,n_{h} 0 {\displaystyle n_{h}>0} . The word n 0 n 1 ⋯ n h {\displaystyle n_{0}n_{1}\cdots n_{h}} is often denoted ⟨ n ⟩ b {\displaystyle \langle n\rangle _{b}} , or more simply, n b {\displaystyle n_{b}} . A set of natural numbers S is recognisable in base b {\textstyle b} or more simply b {\textstyle b} -recognisable or b {\textstyle b} -automatic if the set { n b ∣ n ∈ S } {\displaystyle \{n_{b}\mid n\in S\}} of the representations of its elements in base b {\displaystyle b} is a language recognisable by a finite automaton on the alphabet { 0 , 1 , … , b − 1 } {\displaystyle \{0,1,\ldots ,b-1\}} . Two positive integers k {\displaystyle k} and ℓ {\displaystyle \ell } are multiplicatively independent if there are no non-negative integers p {\displaystyle p} and q {\displaystyle q} such that k p = ℓ q {\displaystyle k^{p}=\ell ^{q}} . For example, 2 and 3 are multiplicatively independent, but 8 and 16 are not since 8 4 = 16 3 {\displaystyle 8^{4}=16^{3}} . Two integers are multiplicatively dependent if and only if they are powers of a same third integer. == Problem statements == === Original problem statement === More equivalent statements of the theorem have been given. The original version by Cobham is the following: Another way to state the theorem is by using automatic sequences. Cobham himself calls them "uniform tag sequences." The following form is found in Allouche and Shallit's book:We can show that the characteristic sequence of a set of natural numbers S recognisable by finite automata in base k is a k-automatic sequence and that conversely, for all k-automatic sequences u {\displaystyle u} and all integers 0 ≤ i < k {\displaystyle 0\leq i 1 {\displaystyle \alpha >1} is the dominant eigenvalue of the matrix of morphism f {\displaystyle f} , namely, the matrix M ( f ) = ( m x , y ) x ∈ B , y ∈ A {\displaystyle M(f)=(m_{x,y})_{x\in B,y\in A}} , where m x , y {\displaystyle m_{x,y}} is the number of occurrences of the letter x {\displaystyle x} in the word f ( y ) {\displaystyle f(y)} . A set S of natural numbers is α {\displaystyle \alpha } -recognisable if its characteristic sequence s {\displaystyle s} is α {\displaystyle \alpha } -substitutive. A last definition: a Perron number is an algebraic number z > 1 {\displaystyle z>1} such that all its conjugates belong to the disc { z ′ ∈ C , | z ′ | < z } {\displaystyle \{z'\in \mathbb {C} ,|z'| Read more →

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  Edward Stabler

  Edward Stabler is a Professor of Linguistics at the University of California, Los Angeles. His primary areas of research are (1) Natural Language Processing (NLP), (2) Parsing and formal language theory, and (3) Philosophy of Logic and Language. He was a member of the faculty at UCLA from 1984 to 2016. His work involves the production of software for minimalist grammars (MGs) and related systems. == Early life and education == Stabler received his Ph.D. from the Department of Linguistics and Philosophy at MIT in 1981. == Recent publications == Edward Stabler (2011) Computational perspectives on minimalism. Revised version in C. Boeckx, ed, Oxford Handbook of Linguistic Minimalism, pp. 617–642. Edward Stabler (2010) A defense of this perspective against the Evans&Levinson critique appears here, with revised version in Lingua 120(12): 2680-2685. Edward Stabler (2010) After GB. Revised version in J. van Benthem & A. ter Meulen, eds, Handbook of Logic and Language, pp. 395–414. Edward Stabler (2010) Recursion in grammar and performance. Presented at the 2009 UMass recursion conference. Edward Stabler (2009) Computational models of language universals. Revised version appears in M. H. Christiansen, C. Collins, and S. Edelman, eds., Language Universals, Oxford: Oxford University Press, pages 200-223. Edward Stabler (2008) Tupled pregroup grammars. Revised version appears in P. Casadio and J. Lambek, eds., Computational Algebraic Approaches to Natural Language, Milan: Polimetrica, pages 23–52. Edward Stabler (2006) Sidewards without copying. Proceedings of the 11th Conference on Formal Grammar, edited by P. Monachesi, G. Penn, G. Satta, and S. Wintner. Stanford: CSLI Publications, 2006, pages 133-146.

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  Ocrad

  Ocrad is an optical character recognition program and part of the GNU Project. It is free software licensed under the GNU GPL. Based on a feature extraction method, it reads images in portable pixmap formats known as Portable anymap and produces text in byte (8-bit) or UTF-8 formats. Also included is a layout analyser, able to separate the columns or blocks of text normally found on printed pages. == User interface == Ocrad can be used as a stand-alone command-line application or as a back-end to other programs. Kooka, which was the KDE environment's default scanning application until KDE 4, can use Ocrad as its OCR engine. Since conversion to newer Qt versions, current versions of KDE no longer contain Kooka; development continues in the KDE git repository. Ocrad can be also used as an OCR engine in OCRFeeder. == History == Ocrad has been developed by Antonio Diaz Diaz since 2003. Version 0.7 was released in February 2004, 0.14 in February 2006 and 0.18 in May 2009. It is written in C++. Archives of the bug-ocrad mailing list go back to October 2003.

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  Macromedia FreeHand

  Macromedia FreeHand (formerly Aldus FreeHand) is a discontinued computer application for creating two-dimensional vector graphics oriented primarily to professional illustration, desktop publishing and content creation for the Web. FreeHand was similar in scope, intended market, and functionality to Adobe Illustrator, CorelDRAW and Xara Designer Pro. Because of FreeHand's dedicated page layout and text control features, it also compares to Adobe InDesign and QuarkXPress. Professions using FreeHand include graphic design, illustration, cartography, fashion and textile design, product design, architects, scientific research, and multimedia production. FreeHand was created by Altsys Corporation in 1988 and licensed to Aldus Corporation, which released versions 1 through 4. In 1994, Aldus merged with Adobe Systems and because of the overlapping market with Adobe Illustrator, FreeHand was returned to Altsys by order of the Federal Trade Commission. Altsys was later bought by Macromedia, which released FreeHand versions 5 through 11 (FreeHand MX). In 2005, Adobe Systems acquired Macromedia and its product line which included FreeHand MX, under whose ownership it presently resides. Since 2003, FreeHand development has been discontinued; in the Adobe Systems catalog, FreeHand has been replaced by Adobe Illustrator. FreeHand MX continues to run under Windows 11 and under Mac OS X 10.6 (Snow Leopard) within Rosetta, a PowerPC code emulator, and requires a registration patch supplied by Adobe. FreeHand 10 runs without problems on Mac OS X Snow Leopard with Rosetta enabled, and does not require a registration patch. Later versions of macOS can use a Mac OS X Snow Leopard Server virtual machine to emulate the required PowerPC support. == History == === Altsys and Aldus FreeHand === In 1984, James R. Von Ehr founded Altsys Corporation to develop graphics applications for personal computers. Based in Plano, Texas, the company initially produced font editing and conversion software; Fontastic Plus, Metamorphosis, and the Art Importer. Their premier PostScript font-design package, Fontographer, was released in 1986 and was the first such program on the market. With the PostScript background having been established by Fontographer, Altsys also developed FreeHand (originally called Masterpiece) as a Macintosh Postscript-based illustration program that used Bézier curves for drawing and was similar to Adobe Illustrator. FreeHand was announced as "... a Macintosh graphics program described as having all the features of Adobe's Illustrator plus drawing tools such as those in Mac Paint and Mac Draft and special effects similar to those in Cricket Draw." Seattle's Aldus Corporation acquired a licensing agreement with Altsys Corporation to release FreeHand along with their flagship product, Pagemaker, and Aldus FreeHand 1.0 was released in 1988. FreeHand's product name used intercaps; the F and H were capitalized. The partnership between the two companies continued with Altsys developing FreeHand and with Aldus controlling marketing and sales. After 1988, a competitive exchange between Aldus FreeHand and Adobe Illustrator ensued on the Macintosh platform with each software advancing new tools, achieving better speed, and matching significant features. Windows PC development also allowed Illustrator 2 (aka, Illustrator 88 on the Mac) and FreeHand 3 to release Windows versions to the graphics market. FreeHand 1.0 sold for $495 in 1988. It included the standard drawing tools and features as other draw programs including special effects in fills and screens, text manipulation tools, and full support for CMYK color printing. It was also possible to create and insert PostScript routines anywhere within the program. FreeHand performed in preview mode instead of keyline mode but performance was slower. FreeHand 2.0 sold for $495 in 1989. Besides improving on the features of FreeHand 1.0, FreeHand 2 added faster operation, Pantone colors, stroked text, flexible fill patterns and automatically import graphic assets from other programs. It added accurate control over a color monitor screen display, limited only by its resolution. FreeHand 3.0 sold for $595 in 1991. New features included resizable color, style, and layer panels including an Attributes menu. Also tighter precision of both the existing tools and aligning of objects. FH3 created compound Paths. Text could be converted to paths, applied to an ellipse, or made vertical. Carried over from version 1.0, FreeHand 3 suffered by having text entered into a dialog box instead of directly to the page. In October 1991, a 3.1 upgrade made FreeHand work with System 7 but additionally, it supported pressure-sensitive drawing which offered varying line widths with a users stroke. It improved element manipulation and added more import/export options. FreeHand 4.0 sold for $595 in 1994. Altsys ported FreeHand 3.0 to the NeXT system creating a new program named Virtuoso. Virtuoso continued its development at Altsys and version 2.0 of Virtuoso was feature-equivalent to FreeHand 4 (with the addition of NeXT-specific features such as Services and Display PostScript) and file compatible, with Virtuoso 2 able to open FreeHand 4 files and vice versa. A prominent feature of this version was the ability to type directly into the page and wrap inside or outside any shape. It also included drag-and-drop color imaging, a larger pasteboard, and a user interface that featured floating, rollup panels. The colors palette included a color mixer for adding new colors to the swatch list. Speed increases were made. In the same year of FreeHand 4 release, Adobe Systems announced merger plans with Aldus Corporation for $525 million. Fear about the end of competition between these two leading applications was reported in the media and expressed by customers (Illustrator versus FreeHand and Adobe Photoshop versus Aldus PhotoStyler.) Because of this overlapping of the market, Altsys stepped in by suing Aldus, saying that the merger deal was "a prima facie violation of a non-compete clause within the FreeHand licensing agreement." Altsys CEO Jim Von Ehr explained, "No one loves FreeHand more than we do. We will do whatever it takes to see it survive." The Federal Trade Commission issued a complaint against Adobe Systems on October 18, 1994, ordering a divestiture of FreeHand to "remedy the lessening of competition resulting from the acquisition as alleged in the Commission's complaint," and further, the FTC ordering, "That for a period of ten (10) years from the date on which this order becomes final, respondents shall not, without the prior approval of the Commission, directly or indirectly, through subsidiaries, partnerships, or otherwise .. Acquire any Professional Illustration Software or acquire or enter into any exclusive license to Professional Illustration Software;" (referring to FreeHand.) FreeHand was returned to Altsys with all licensing and marketing rights as well as Aldus FreeHand's customer list. === Macromedia Freehand === By late 1994, Altsys still retained all rights to FreeHand. Despite brief plans to keep it in-house to sell it along with Fontographer and Virtuoso, Altsys reached an agreement with the multimedia software company, Macromedia, to be acquired. This mutual agreement provided FreeHand and Fontographer a new home with ample resources for marketing, sales, and competition against the newly merged Adobe-Aldus company. Altsys would remain in Richardson, Texas, but would be renamed as the Digital Arts Group of Macromedia and was responsible for the continued development of FreeHand. Macromedia received FreeHand's 200,000 customers and expanded its traditional product line of multimedia graphics software to illustration and design graphics software. CEO James Von Ehr became a Macromedia vice-president until 1997 when he left to start another venture. FreeHand 5.0 sold for $595 in 1995. This version featured a more customizable and expanded workspace, multiple views, stronger design and editing tools, a report generator, spell check, paragraph styles, multicolor gradient fills up to 64 colors, speed improvements, and it accepted Illustrator plugins. In September 1995, a 5.5 upgrade added Photoshop plug-in support, PDF import capabilities, the Extract feature, inline graphics to text, improved auto-expanding text containers, the Crop feature, and the Create PICT Image feature. A FreeHand 5.5 upgrade was part of the FreeHand Graphics Studio (a suite that included Fontographer, Macromedia xRes image editing application, and Extreme 3D animation and modeling application). FreeHand 6.0 in 1996. This version only existed in beta. Some Freehand 7 prerelease versions were released under the Freehand 6 tag. FreeHand 7.0 sold for $399 in 1996, or $449 as part of the FreeHand Graphics Studio (see above.) Features included a redesigned user interface that allowed recombining Inspectors, Panel Tabs, Dockable Panels, Smart Cursors,

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  Corpus-assisted discourse studies

  Corpus-assisted discourse studies (abbr.: CADS) is related historically and methodologically to the discipline of corpus linguistics. The principal endeavor of corpus-assisted discourse studies is the investigation, and comparison of features of particular discourse types, integrating into the analysis the techniques and tools developed within corpus linguistics. These include the compilation of specialised corpora and analyses of word and word-cluster frequency lists, comparative keyword lists and, above all, concordances. A broader conceptualisation of corpus-assisted discourse studies would include any study that aims to bring together corpus linguistics and discourse analysis. Such research is often labelled as corpus-based or corpus-assisted discourse analysis, with the term CADS coined by a research group in Italy (Partington 2004) for a specific type of corpus-assisted discourse analysis (see the section 'in different countries' below). == Aims == Corpus-assisted discourse studies aim to uncover non-obvious meaning, that is, meaning which might not be readily available to naked-eye perusal. Much of what carries meaning in texts is not open to direct observation: “you cannot understand the world just by looking at it” (Stubbs [after Gellner 1959] 1996: 92). We use language “semi-automatically”, in the sense that speakers and writers make semi-conscious choices within the various complex overlapping systems of which language is composed, including those of transitivity, modality (Michael Halliday 1994), lexical sets (e.g. freedom, liberty, deliverance), modification, and so on. Authors themselves are, famously, generally unaware of all the meanings their texts convey. By combining the quantitative research approach, that is, statistical analysis of large amounts of the discourse in question - more precisely, large numbers of tokens of the discourse type under study contained in a corpus - with the more qualitative research approach typical of discourse analysis, that is, the close, detailed examination of particular stretches of discourse it may be possible to better understand the processes at play in the discourse type and to gain access to non-obvious meanings. Aims can differ in other types of corpus-based or corpus-assisted discourse analysis; but in general such studies combine quantitative and qualitative research and aim to shed light on discourses, registers, discourse patterns, etc., with the help of a corpus linguistic approach. Specific aims and techniques depend on the relevant project. == In different countries == In German-speaking countries: Pioneering work in corpus-based discourse analysis was conducted in Europe, in particular by Hardt-Mautner/Mautner (1995, 2000) and Stubbs (1996, 2001). CADS and other types of corpus-based discourse analysis are inspired by this important early work. In Italy: A considerable body of research has been conducted in Italy either by individual researchers or under the aegis of combined inter-university projects such as Newspool (Partington et al. 2004) and CorDis (Morley and Bayley eds, 2009). It has concentrated on political and media language, mainly because a nucleus of linguists in Italian universities work in Political Science faculties and are increasingly interested in the use of corpus techniques to conduct a particular type of sociopolitical discourse analysis, including the unearthing of noteworthy ideological metaphors and motifs in the language of political figures and institutions. Italian researchers also developed Modern diachronic corpus-assisted discourse studies (MD-CADS). This approach contrasts the language contained in comparable corpora from different but recent points in time in order to track changes in modern language usage but also social, cultural and political changes over modern times, as reflected - and shared among people - in language. It is this Italian body of research that makes most use of the label CADS. In the UK: Linguists in the UK tend to undertake corpus-based critical discourse analysis (CDA). CDA generally adopts a leftist political stance, focusing on the ways that social and political domination is reproduced by text and talk. This type of corpus-based research was originally associated with Lancaster University (Baker et al. 2008), but has spread more widely since. Such work typically studies the discourses around particular groups of people (e.g. Muslims, people with disabilities) or concepts/events (e.g. feminism, same-sex marriage). In Australia: Corpus-based discourse analysis is undertaken by a growing number of Australian researchers, most often on media texts. Some of this work aims to elucidate specific features of discourse types (news, social media, television series, etc.), while other work is rooted in the tradition of corpus-based critical discourse analysis. == Comparison with traditional corpus linguistics == Traditional corpus linguistics has, quite naturally, tended to privilege the quantitative approach. In the drive to produce more authentic dictionaries and grammars of a language, it has been characterised by the compilation of some very large corpora of heterogeneric discourse types in the desire to obtain an overview of the greatest quantity and variety of discourse types possible, in other words, of the chimerical but useful fiction called the “general language” (“general English”, “general Italian”, and so on). This has led to the construction of immensely valuable research tools such as the Bank of English and the British National Corpus. Some branches of corpus linguistics have also promoted an approach that is "corpus-driven", in which we need, grammatically speaking, a mental tabula rasa to free ourselves of the baleful prejudice exerted by traditional models and allow the data to speak entirely for itself. The aim of corpus-assisted discourse studies and related approaches is radically different. Here the aim of the exercise is to acquaint oneself as much as possible with the discourse type(s) in hand. Researchers typically engage with their corpus in a variety of ways. As well as via wordlists and concordancing, intuitions for further research can also arise from reading or watching or listening to parts of the data-set, a process which can help provide a feel for how things are done linguistically in the discourse-type being studied. Corpus-assisted discourse analysis is also typically characterised by the compilation of ad hoc specialised corpora, since very frequently there exists no previously available collection of the discourse type in question. Often, other corpora are utilized in the course of a study for purposes of comparison. These may include pre-existing corpora or may themselves need to be compiled by the researcher. In some sense, all work with corpora – just as all work with discourse - is properly comparative. Even when a single corpus is employed, it is used to test the data it contains against another body of data. This may consist of the researcher's intuitions, or the data found in reference works such as dictionaries and grammars, or it may be statements made by previous authors in the field. == CADS as a specific type of corpus-based discourse analysis == Researchers in Italy have developed CADS as a specific type of corpus-based discourse analysis, creating a standard set of methods: 'A basic, standard methodology in CADS may resemble the following:' Step 1: Decide upon the research question; Step 2: Choose, compile or edit an appropriate corpus; Step 3: Choose, compile or edit an appropriate reference corpus / corpora; Step 4: Make frequency lists and run a keywords comparison of the corpora; Step 5: Determine the existence of sets of key items; Step 6: Concordance interesting key items (with differing quantities of co-text); Step 7: (Possibly) refine the research question and return to Step 2. This basic procedure can of course vary according to individual research circumstances and requirements. A particular way of conceptualising research questions has also been proposed in such CADS projects: Given that P is a discourse participant (or possibly an institution) and G is a goal, often a political goal: How does P achieve G with language? What does this tell us about P? Comparative studies: how do P1 and P2 differ in their use of language? Does this tell us anything about their different principles and objectives? A second general type of CADS research question, which might be asked of interactive discourse data, has been conceptualised as follows: Given that P(x) is a particular participant or set of participants, DT is the discourse type, and R is an observed relationship between or among participants: How do {P(a), P(b)...P(n)} achieve / maintain R in DT [using language]? Another common type of research question has been conceptualised thus: Given that A is an author, Ph(x) is a phenomenon or practice or behaviour, and DT(x) is a particular discourse type. A has said P

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  Best AI Text-to-video Tools in 2026

  In search of the best AI text-to-video tool? An AI text-to-video tool 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 text-to-video tool 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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  Constrained conditional model

  A constrained conditional model (CCM) is a machine learning and inference framework that augments the learning of conditional (probabilistic or discriminative) models with declarative constraints. The constraint can be used as a way to incorporate expressive prior knowledge into the model and bias the assignments made by the learned model to satisfy these constraints. The framework can be used to support decisions in an expressive output space while maintaining modularity and tractability of training and inference. Models of this kind have recently attracted much attention within the natural language processing (NLP) community. Formulating problems as constrained optimization problems over the output of learned models has several advantages. It allows one to focus on the modeling of problems by providing the opportunity to incorporate domain-specific knowledge as global constraints using a first order language. Using this declarative framework frees the developer from low level feature engineering while capturing the problem's domain-specific properties and guarantying exact inference. From a machine learning perspective it allows decoupling the stage of model generation (learning) from that of the constrained inference stage, thus helping to simplify the learning stage while improving the quality of the solutions. For example, in the case of generating compressed sentences, rather than simply relying on a language model to retain the most commonly used n-grams in the sentence, constraints can be used to ensure that if a modifier is kept in the compressed sentence, its subject will also be kept. == Motivation == Making decisions in many domains (such as natural language processing and computer vision problems) often involves assigning values to sets of interdependent variables where the expressive dependency structure can influence, or even dictate, what assignments are possible. These settings are applicable not only to Structured Learning problems such as semantic role labeling, but also for cases that require making use of multiple pre-learned components, such as summarization, textual entailment and question answering. In all these cases, it is natural to formulate the decision problem as a constrained optimization problem, with an objective function that is composed of learned models, subject to domain- or problem-specific constraints. Constrained conditional models form a learning and inference framework that augments the learning of conditional (probabilistic or discriminative) models with declarative constraints (written, for example, using a first-order representation) as a way to support decisions in an expressive output space while maintaining modularity and tractability of training and inference. These constraints can express either hard restrictions, completely prohibiting some assignments, or soft restrictions, penalizing unlikely assignments. In most applications of this framework in NLP, following, Integer Linear Programming (ILP) was used as the inference framework, although other algorithms can be used for that purpose. == Formal Definition == Given a set of feature functions { ϕ i ( x , y ) } {\displaystyle \{\phi _{i}(x,y)\}} and a set of constraints { C i ( x , y ) } {\displaystyle \{C_{i}(x,y)\}} , defined over an input structure x ∈ X {\displaystyle x\in X} and an output structure y ∈ Y {\displaystyle y\in Y} , a constraint conditional model is characterized by two weight vectors, w and ρ {\displaystyle \rho } , and is defined as the solution to the following optimization problem: a r g m a x y ∑ i w i ϕ i ( x , y ) − ∑ ρ i C i ( x , y ) {\displaystyle argmax_{y}\sum _{i}w_{i}\phi _{i}(x,y)-\sum \rho _{i}C_{i}(x,y)} . Each constraint C i ∈ C {\displaystyle C_{i}\in C} is a boolean mapping indicating if the joint assignment ( x , y ) {\displaystyle (x,y)} violates a constraint, and ρ {\displaystyle \rho } is the penalty incurred for violating the constraints. Constraints assigned an infinite penalty are known as hard constraints, and represent unfeasible assignments to the optimization problem. == Training paradigms == === Learning local vs. global models === The objective function used by CCMs can be decomposed and learned in several ways, ranging from a complete joint training of the model along with the constraints to completely decoupling the learning and the inference stage. In the latter case, several local models are learned independently and the dependency between these models is considered only at decision time via a global decision process. The advantages of each approach are discussed in which studies the two training paradigms: (1) local models: L+I (learning + inference) and (2) global model: IBT (Inference based training), and shows both theoretically and experimentally that while IBT (joint training) is best in the limit, under some conditions (basically, ”good” components) L+I can generalize better. The ability of CCM to combine local models is especially beneficial in cases where joint learning is computationally intractable or when training data are not available for joint learning. This flexibility distinguishes CCM from the other learning frameworks that also combine statistical information with declarative constraints, such as Markov logic network, that emphasize joint training. === Minimally supervised CCM === CCM can help reduce supervision by using domain knowledge (expressed as constraints) to drive learning. These settings were studied in and. These works introduce semi-supervised Constraints Driven Learning (CODL) and show that by incorporating domain knowledge the performance of the learned model improves significantly. === Learning over latent representations === CCMs have also been applied to latent learning frameworks, where the learning problem is defined over a latent representation layer. Since the notion of a correct representation is inherently ill-defined, no gold-standard labeled data regarding the representation decision is available to the learner. Identifying the correct (or optimal) learning representation is viewed as a structured prediction process and therefore modeled as a CCM. This problem was covered in several papers, in both supervised and unsupervised settings. In all cases research showed that explicitly modeling the interdependencies between representation decisions via constraints results in an improved performance. == Integer linear programming for natural language processing applications == The advantages of the CCM declarative formulation and the availability of off-the-shelf solvers have led to a large variety of natural language processing tasks being formulated within the framework, including semantic role labeling, syntactic parsing, coreference resolution, summarization, transliteration, natural language generation and joint information extraction. Most of these works use an integer linear programming (ILP) solver to solve the decision problem. Although theoretically solving an Integer Linear Program is exponential in the size of the decision problem, in practice using state-of-the-art solvers and approximate inference techniques large scale problems can be solved efficiently. The key advantage of using an ILP solver for solving the optimization problem defined by a constrained conditional model is the declarative formulation used as input for the ILP solver, consisting of a linear objective function and a set of linear constraints. == Resources == CCM Tutorial Predicting Structures in NLP: Constrained Conditional Models and Integer Linear Programming in NLP

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  ByLock

  ByLock was a smartphone application that allowed users to communicate via a private, encrypted connection. It was launched in March 2014 on Google Play, Apple App Store The app was downloaded over 600,000 times from its launch in April 2014 until March 2016, when it was permanently shut down. The Turkish National Intelligence Organization (Turkish: Millî İstihbarat Teşkilatı, MİT) stated that the app was downloaded mainly in Turkey and the users were “Fetullahist Terror Organisation (FETÖ) which was formerly known as “Gülen movement” members. == Gülen Movement controversy == In Turkey, possession of the app is deemed evidence of membership in the Gülen Movement, which was allegedly connected to the failed Turkish coup d'état attempt in July 2016. Users of ByLock were deemed terrorists in Turkish courts. According to Deutsche Welle, of the 215,000 former ByLock users, an estimated 23,000 have been detained by Turkish authorities. Some believe that the MİT and other Turkish authorities manipulated the ByLock database in order to arrest suspected members of the Gülen Movement. Tuncay Beşikçi, a computer forensic expert in Turkey, emphasized that "the demands to investigate and analyze ByLock data from independent institutions are refused by the Turkish courts. But it is not normal". Tuncay Beşikçi believes that this application is precisely one of the channels for Gülen molecules to communicate and can also monitor the activities of other members of the organization. He also stated that the developers behind the Mor Beyin app, deliberately set a plan in motion that would put thousands of innocent people in prison as a cover for the Gülen movement. In December 2017, Turkish authorities revealed that almost half the people who had been prosecuted for having ByLock on their smartphones would have their legal cases reviewed, as they could have been redirected to the app without their knowledge. Following the failed coup attempt on 15 July 2016, the use of the ByLock messaging application by members of the Gülen Movement was the sole evidence in investigations and prosecutions to justify arrests and convictions for "membership in an armed terrorist organization." However, these decisions have been considered human rights violations by the European Court of Human Rights (ECHR), the United Nations Human Rights Committee, and the UN Working Group on Arbitrary Detention. Some of the relevant decisions include the following: === Decisions of the European Court of Human Rights === On 20 July 2021, in the case of Tekin Akgün v. Turkey, the European Court of Human Rights (ECHR) ruled that the use of the ByLock messaging application, unless supported by other evidence, does not create a reasonable suspicion of a crime. Based on this reasoning, the court found that the detention order violated Article 5 of the European Convention on Human Rights, which protects the right to liberty and security. In the Yüksel Yalçınkaya v. Turkey decision on 26 September 2023, the European Court of Human Rights (ECHR) examined an appeal against a conviction based on the use of ByLock. The Court ruled that the failure to provide an opportunity to challenge the authenticity of the ByLock data violated the right to a fair trial (Article 6 of the ECHR). The Court also stated that the mere use of ByLock could not be considered sufficient evidence for membership in an armed terrorist organization. It further noted that local courts had established an automatic presumption of guilt based solely on ByLock use, creating a broad and unpredictable interpretation of the law, making it nearly impossible for the accused to exonerate themselves. Therefore, the Court concluded that the conviction based on the use of ByLock violated the principle of no punishment without law (Article 7 of the ECHR). On 22 July 2025, in the Demirhan and 238 Others case, the European Court of Human Rights (ECHR) consolidated the applications of 239 individuals who had been convicted of "membership in an armed terrorist organization" based on their use of ByLock, as determined by 239 separate courts in Turkey. The Court ruled that the convictions violated the right to a fair trial under Article 6 and the principle of no punishment without law under Article 7 of the European Convention on Human Rights (ECHR). The ruling stated that the Turkish courts' "categorical approach" to the use of ByLock lacked legal foundation. In this context, it was emphasized that anyone who had used ByLock could not be convicted of membership in an armed terrorist organization based solely on this reasoning. The ruling also stated that, due to the large number of similar applications, the issue was "systemic in nature" and it called for a national solution to be developed by Turkey. While the Court did not order compensation for the 239 applicants, it emphasized that reopening the trial to ensure the enforcement of the violation ruling was the most appropriate remedy. This ruling, which confirms the violation finding in the Yüksel Yalçınkaya case of 26 September 2023, is considered a continuation of the ECHR's case law concerning trials based on ByLock evidence. === Decisions of the United Nations Human Rights Committee and Working Group === In the İsmet Özçelik and Turgay Karaman v. Turkey decision, dated 28 May 2019 (Application No. 2980/2017), the UN Human Rights Committee ruled that the use of ByLock and allegations of depositing money into Bank Asya could not justify the applicants' arrests. In the Mestan Yayman v. Turkey decision (Opinion No. 42/2018 – 21 August 2018) by the UN Human Rights Council Working Group on Arbitrary Detention, it was stated that using a public messaging application like ByLock cannot be considered criminal evidence, and that the use of such an application falls under the scope of freedom of thought and expression. The dozens of decisions later issued by the UN Human Rights Council Working Group are of the same nature.

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  Gary B. Fogel

  Gary Bryce Fogel (born 1968) is an American biologist and computer scientist. He is the Chief Executive Officer of Natural Selection, Inc. He is most known for his applications of computational intelligence and machine learning to bioinformatics, computational biology, and industrial optimization. == Education and Research == Fogel was born and raised in La Jolla, California, graduating from La Jolla High School. He received a B.A. in biology with a minor in earth sciences from the University of California, Santa Cruz in 1991 and a Ph.D. in biology from the University of California, Los Angeles in 1998. Fogel has published over 150 peer-reviewed publications in conferences and journals, 2 edited books, and 11 patents. As CEO of Natural Selection, Inc., his research focuses on the application of computational intelligence, machine learning, and predictive analytics in areas not limited to: Viral evolution, cellular differentiation, drug discovery, RNA structure, cis-regulatory elements, cancer, and evolutionary game theory as well as the development of evolutionary algorithms and other approaches. == Service == Between 2008–2018 Gary Fogel was editor-in-chief of the Elsevier journal BioSystems. He has served previously as an associate editor for IEEE Transactions on Artificial Intelligence, IEEE Computational Intelligence Magazine (2005–2010), IEEE Transactions on Evolutionary Computation (2001–2013), IEEE Transactions on Emerging Topics in Computational Intelligence (2016–2018), IEEE/ACM Transactions on Computational Biology and Bioinformatics (2004–2008), International Journal of Bioinformatics Research and Applications (2004–2007), International Journal of Data Mining and Bioinformatics (2005–2007), as a consulting editor for the Journal of Computational Intelligence in Bioinformatics (2006–2007), and as an editorial board member of Ecological Informatics (2005–2009) and BMC Big Data Analytics (2015–2020). Within the IEEE Computational Intelligence Society, Fogel founded the Bioinformatics and Bioengineering Technical Committee and established the IEEE Computational Intelligence in Bioinformatics and Computational Biology conference series, chairing the first two meetings in 2004 and 2005 in San Diego. He co-founded the IEEE Conference on Artificial Intelligence in 2023. Fogel served on the IEEE Computational Intelligence Society Administrative Committee (2004–2009, 2014–2022) and served as IEEE CIS Vice President of Conferences (2010–2013, 2019). == Teaching == Gary Fogel also serves as adjunct faculty at San Diego State University in the department of aerospace engineering as well as in the Computational Science Research Center. He has authored four books and numerous articles on the history of early aviation focusing on motorless flight. He is an associate fellow of the American Institute of Aeronautics and Astronautics and serves on the AIAA History Committee. == Awards == 2023 – Outstanding Contribution to Aerospace Education Award, AIAA San Diego Section 2022 – Elected Fellow of the Asia-Pacific Artificial Intelligence Association 2019 – Top 100 AI Leaders in Drug Discovery and Advanced Healthcare by Deep Knowledge Analytics 2019 – Outstanding Contribution to Aerospace Education Award, AIAA San Diego Section 2016 – Meritorious Service Award, IEEE Computational Intelligence Society 2016 – Outstanding Contribution to the Community Award, AIAA San Diego Section 2015 – Outstanding Enhancement of the Image of the Aerospace Profession Award, AIAA San Diego Section 2012 – Medal for Significant Achievement, San Diego Chapter of Sigma Xi 2012 – Fellow of the Institute of Electrical and Electronics Engineers for contributions to computational intelligence and its application to biology, chemistry, and medicine. == Aeromodeling == Gary Fogel has established national and world records for model aircraft. He helped establish the National Model Aviation Heritage program for the Academy of Model Aeronautics. He is a leader member, contest director, and fellow of the Academy of Model Aeronautics, and was inducted into the Academy of Model Aeronautics Hall of Fame in 2025.

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  AI Virtual Assistants Reviews: What Actually Works in 2026

  Curious about the best AI virtual assistant? An AI virtual 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 virtual 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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