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Cowrie (honeypot)

Cowrie is a medium interaction SSH and Telnet honeypot designed to log brute force attacks and shell interaction performed by an attacker. Cowrie also functions as an SSH and telnet proxy to observe attacker behavior to another system. Cowrie was developed from Kippo. == Reception == Cowrie has been referenced in published papers. The Book "Hands-On Ethical Hacking and Network Defense" includes Cowrie in a list of 5 commercial honeypots. === Prior uses === Discussing a honeypot effort called the Project Heisenberg Cloud by Rapid7, Bob Rudis, the company's chief data scientist, told eWEEK, "There are custom Rapid7-developed low- and medium-interaction honeypots used within the framework, along with open-source ones, such as Cowrie." Doug Rickert has experimented with the open-source Cowrie SSH honeypot and wrote about it on Medium. Putting up a simple honeypot isn't difficult, and there are many open-source products besides Cowrie, including the original Honeyd to MongoDB and NoSQL honeypots, to ones that emulate web servers. Some appear to be SCADA or other more advanced applications. === Best practices === Researchers at the SysAdmin, Audit, Network and Security (SANS) institute urged administrators and security researchers to run the latest version of Cowrie on a honeypot to monitor shifts in the type of passwords being scanned for and pattern of attacks on IoT devices. === Discussion and further resources === Attack Detection and Forensics Using Honeypot in an IoT Environment calls Cowrie a "medium interaction honeypot" and describes results from using it for 40 days to capture "all communicated sessions in log files." The book Advances on Data Science also devotes chapter two to "Cowrie Honeypot Dataset and Logging." ICCWS 2018 13th International Conference on Cyber Warfare and Security describes using Cowrie. On the Move to Meaningful Internet Systems: OTM 2019 Conferences includes details of using Cowrie. Splunk, a security tool that can receive information from honeypots, outlines how to set up a honeypot using the open-source Cowrie package.
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Mark Keane (cognitive scientist)

Mark Thomas Gerard Keane (Irish: Marcus Ó Cathain, born 3 July 1961, Dublin, Ireland) is a cognitive scientist and author of several books on human cognition and artificial intelligence, including Cognitive Psychology: A Student's Handbook (8 editions, with Michael Eysenck), Advances in the Psychology of Thinking (1992, with Ken Gilhooly), Novice Programming Environments (1992/2018, with Marc Eisenstadt and Tim Rajan), Advances in Case-Based Reasoning (1995, with J-P Haton and Michel Manago)., Case-Based Reasoning: Research & Development (2022, with N Wiratunga). == Education == Keane received a B.A. in Psychology from University College Dublin in 1982. He then received a Ph.D. from Trinity College Dublin in 1987. He then moved to postdoctoral positions in Queen Mary University of London and the Open University. == Academic career == He was a Lecturer in Psychology at Cardiff University. He became a lecturer in Computer Science at Trinity College Dublin in 1990, and became a fellow in 1994. Keane moved to become Chair of Computer Science at University College Dublin in 1998. In 2006, he was seconded to Science Foundation Ireland as Director of ICT, overseeing on a $700m research investment. He advised the Irish Government on its 3.7B euro Strategy for Science, Technology & Innovation (SSTI). From 2006 to 2007, he was Director General of Science Foundation Ireland before returning to University College Dublin where he was appointed VP of Innovation & Partnerships (2007-2009). Keane's research has been split between cognitive science and computer science. His cognitive science research has been in analogy, metaphor, conceptual combination and similarity. His computer science research has been in natural language processing, machine learning, case-based reasoning, text analytics and explainable artificial intelligence. He has been a PI in the Science Foundation Ireland funded Insight Centre for Data Analytics working on digital journalism and digital humanities. More recently, he was deputy director of the VistaMilk SFI Research Centre that is exploring precision agriculture in the dairy sector.
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Jaime Carbonell

Jaime Guillermo Carbonell (July 29, 1953 – February 28, 2020) was a computer scientist who made seminal contributions to the development of natural language processing tools and technologies. His research in machine translation resulted in the development of several state-of-the-art language translation and artificial intelligence systems. He earned his B.S. degrees in Physics and in Mathematics from MIT in 1975 and did his Ph.D. under Dr. Roger Schank at Yale University in 1979. He joined Carnegie Mellon University as an assistant professor of computer science in 1979 and moved to Pittsburgh. He was affiliated with the Language Technologies Institute, Computer Science Department, Machine Learning Department, and Computational Biology Department at Carnegie Mellon. His interests spanned several areas of artificial intelligence, language technologies and machine learning. In particular, his research focused on areas such as text mining (extraction, categorization, novelty detection) and in new theoretical frameworks such as a unified utility-based theory bridging information retrieval, summarization, free-text question-answering and related tasks. He also worked on machine translation, both high-accuracy knowledge-based MT and machine learning for corpus-based MT (such as generalized example-based MT). == Career == Carbonell was the Allen Newell Professor of Computer Science and head of the Language Technologies Institute at Carnegie Mellon University. He joined Carnegie Mellon in 1979, and became a key faculty member in the artificial intelligence area. He was appointed full professor in 1987, Newell Chair in 1995, and University Professor in 2012. He completed his undergraduate studies at MIT. He received dual degrees in Mathematics and Physics. He received his Ph.D. in computer science from Yale University in 1979. At the time of his appointment, Carbonell was the youngest chaired professor in the School of Computer Science at CMU. His research spanned several areas of computer science, mostly in artificial intelligence, including: machine learning, data and text mining, natural language processing, very-large-scale knowledge bases, translingual information retrieval and automated summarization. He wrote more than 300 technical papers and gave over 500 invited or refereed-paper presentations (colloquia, seminars, panels, conferences, keynotes, etc.). He died following a long illness on February 28, 2020. Mona Talat Diab became the director of CMU's Language Technologies Institute in 2023. == Research == Carbonell created MMR (maximal marginal relevance) technology for text summarization and informational novelty detection in search engines, invention of transformational analogy, a generalized method for case-based reasoning (CBR) to re-use, modify and compose past successful plans for increasingly complex problems and knowledge-based interlingual machine translation. He was instrumental in setting up the Computational Biolinguistics Program, a joint venture between Carnegie Mellon and the University of Pittsburgh, which combines Language Technologies and Machine Learning to model and predict genomic, proteomic and glycomic 3D structures. Carbonell also did work in machine learning. He organized the first four machine learning conferences, starting with CMU in 1981. The Language Technologies Institute (LTI), founded and directed by Carbonell, achieved top honors in multiple areas. These areas include machine translation, search engines (including founding of Lycos by Michael Mauldin, one of Carbonell’s PhD students), speech synthesis, and education. LTI remains the original, largest and best-known institute for language technologies, with over $12M in annual funding and 200 researchers (faculty, staff, PhD students, MS students, visiting scholars etc.). Carbonell made major technical contributions in several fields, including (1) Creation of MMR (maximal marginal relevance) technology for text summarization and informational novelty detection in search engines,(2) Proactive machine learning for multi-source cost-sensitive active learning, (3) Linked conditional random fields for predicting tertiary and quaternary protein folds, (4) Symmetric optimal phrasal alignment method for trainable example-based and statistical machine translation, (5) Series- anomaly modeling for financial fraud detection and syndromic surveillance, (6) Knowledge-based interlingual machine translation, (7) Robust case-frame parsing, (8) Seeded version-space learning and (9) Invention of transformational and derivational analogy, generalized methods for case-based reasoning (CBR) to re-use, modify and compose past successful plans for increasingly complex problems. The teams led by Carbonell achieved top honors in many areas such as first scalable high-accuracy interlingual machine translation (1991), first speech-to-speech machine translation (1992), first large-scale spider and search engine (1994), and first trainable, large-scale protein-structure topology predictor (2005). Modern machine learning, co-founded by Carbonell, Michalski and Mitchell, is a fundamental enabling technology in search engines, data mining and social networking. Starting in 1980, he co-edited the first three books on ML, launched the ML conferences and was a co-founder and editor-in-chief of ML Journal. Carbonell’s innovations have led to several successful start-ups: Carnegie Group (AI expertsystems), Lycos (web search), Wisdom (financial optimization & ML), Carnegie Speech (spoken-language tutoring), Dynamix (data mining and pattern discovery), and Meaningful Machines (context-based machine translation). Carbonell was the founding director of The Language Technology Institute, the preeminent global institution in language studies, unparalleled in size and scope and has since been adopted/imitated in Germany (DFKI), Japan (Tokyo Univ.), and the US (Johns Hopkins). == Awards and honors == Okawa Prize, 2015 Best paper award, “Translingual Search” w/Yang, International Joint Conference on AI, 1997 Allen Newell endowed chair, Carnegie Mellon University, 1995 Elected fellow of AAAI, 1991 Computer Science teaching award, Carnegie Mellon University, 1987 Sperry Fellowship for excellence in AI research, 1986 Herbert Simon teaching award, 1986 "Recognition of Service" award from the ACM for the SIGART presidency, 1983–1985 Provided congressional testimony on machine translation, 1990 == Selected works == === Books === 1983. (with Ryszard S. Michalski & Tom M. Mitchell, Eds.) Machine learning: An artificial intelligence approach. Los Altos, CA: Morgan Kaufmann. 1986. (with Ryszard S. Michalski & Tom Mitchell, Eds.) Machine learning: An artificial intelligence approach. Vol. II. Los Altos, CA: Morgan-Kaufmann. 1986. (with Ryszard S. Michalski & Tom Mitchell, Eds.) Machine Learning: A Guide to Current Research. Kluwer Academic Publishers. == Contributions == “Protein Quaternary Fold Recognition Using Conditional Graphical Models” IJCAI 2007 (w/Liu et al.) “Context-Based Machine Translation” AMTA 2006 (w/Klein et al.) “SCRFs: A New Approach for Protein Fold Recognition,’’ Journal of Computational Biology, 13,2, 2006 (w/Liu et al) “MT for Resource-Poor Languages Using Elicitation-Based Learning” Machine Translation, 2004 ‘‘Learning Approaches for Detecting and Tracking News Events,’’ IEEE Trans I.S., 14, 4, 2000 (w/Yang)
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Margaret Mitchell (scientist)

Margaret Mitchell is a computer scientist who works on algorithmic bias and fairness in machine learning. She is most well known for her work on automatically removing undesired biases concerning demographic groups from machine learning models, as well as more transparent reporting of their intended use. == Education == Mitchell obtained a bachelor's degree in linguistics from Reed College, Portland, Oregon, in 2005. After having worked as a research assistant at the OGI School of Science and Engineering for two years, she subsequently obtained a Master's in Computational Linguistics from the University of Washington in 2009. She enrolled in a PhD program at the University of Aberdeen, where she wrote a doctoral thesis on the topic of Generating Reference to Visible Objects, graduating in 2013. == Career and research == Mitchell is best known for her work on fairness in machine learning and methods for mitigating algorithmic bias. This includes her work on introducing the concept of 'Model Cards' for more transparent model reporting, and methods for debiasing machine learning models using adversarial learning. Margaret Mitchell created the framework for recognizing and avoiding biases by testing with a variable for the group of interest, predictor and an adversary. In 2012, Mitchell joined the Human Language Technology Center of Excellence at Johns Hopkins University as a postdoctoral researcher, before taking up a position at Microsoft Research in 2013. At Microsoft, Mitchell was the research lead of the Seeing AI project, an app that offers support for the visually impaired by narrating texts and images. In November 2016, she became a senior research scientist at Google Research and Machine intelligence. While at Google, she founded and co-led the Ethical Artificial Intelligence team together with Timnit Gebru. In May 2018, she represented Google in the Partnership on AI. In February 2018, she gave a TED talk on "How we can build AI to help humans, not hurt us". In January 2021, after Timnit Gebru's termination from Google, Mitchell reportedly used a script to search through her corporate account and download emails that allegedly documented discriminatory incidents involving Gebru. An automated system locked Mitchell's account in response. In response to media attention Google claimed that she "exfiltrated thousands of files and shared them with multiple external accounts". After a five-week investigation, Mitchell was fired. Prior to her dismissal, Mitchell had been a vocal advocate for diversity at Google, and had voiced concerns about research censorship at the company. In late 2021, she joined AI start-up Hugging Face. Mitchell is a co-founder of Widening NLP, a special interest group within the Association for Computational Linguistics (ACL) seeking to increase the proportion of women and minorities working in natural language processing; and Computational Linguistics and Clinical Psychology, an annual workshop within the ACL that brings together clinicians and computational linguists to advance the state of the art in clinical psychology.
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ObjectVision

ObjectVision was a forms-based programming language and environment for Windows 3.x developed by Borland. The latest version, 2.1, was released in 1992. An ObjectVision application is composed by forms designed in a graphic way that contains objects and events to provide interactivity. Forms are connected together with logic in the form of decision trees. ObjectVision applications also can interact with databases using multiple engines, like Paradox and dBase. A finished project is saved as an OVD file, that is executed by an interpreted runtime that can be freely distributed. ObjectVision was not used broadly except in some niche segments, but the visual programming ideas were the basis for Borland Delphi.
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Michael Collins (computational linguist)

Michael J. Collins (born 4 March 1970) is a researcher in the field of computational linguistics. He is the Vikram S. Pandit Professor of Computer Science at Columbia University. His research interests are in natural language processing as well as machine learning and he has made important contributions in statistical parsing and in statistical machine learning. In his studies Collins covers a wide range of topics such as parse re-ranking, tree kernels, semi-supervised learning, machine translation and exponentiated gradient algorithms with a general focus on discriminative models and structured prediction. One notable contribution is a state-of-the-art parser for the Penn Wall Street Journal corpus. As of 11 November 2015, his works have been cited 16,020 times, and he has an h-index of 47. Collins worked as a researcher at AT&T Labs between January 1999 and November 2002, and later held the positions of assistant and associate professor at M.I.T. Since January 2011, he has been a professor at Columbia University. In 2011, he was named a fellow of the Association for Computational Linguistics.
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Timo Honkela

Timo Untamo Honkela (August 4, 1962 – May 9, 2020) was a computer scientist at the University of Helsinki, Aalto University School of Science and Aalto University School of Art, Design and Architecture. He holds a PhD from Helsinki University of Technology. From 2014 until 2018 he held a fixed-term professorship at the University of Helsinki. Before joining the University of Helsinki he worked as a non-tenured professor in two Schools of the Aalto University, The School of Art, Design and Architecture and the School of Science. He has presented his thoughts on his studies and work in the joint blog 375 Humanists. Timo Honkela conducted research on several areas related to knowledge engineering, cognitive modeling and natural language processing. Honkela was born in Kalajoki. From 1998 to 2000 he worked as a professor in the Aalto Media Lab. To the media Lab Honkela brought his expertise in Kohonen self-organising map (SOM) and worked closely with artist and designers around the topic. In 2001 Honkela collaborated with George Legrady to produce an interactive museum installation, Pockets Full of Memories to the Centre Georges Pompidou, National Museum of Modern Art in Paris. The concept, created by Legrady, provided for visitors a possibility to scan their own objects to a database and then organise them by Kohonen Self-Organizing Map algorithm. In 2017 Honkela published a book in Finnish. The book Rauhankone (English: Peace Machine) presents his idea of designing artificial intelligence and machine learning to serve humanity, in practice to help people to live in peace with each other. He died in Helsinki. == Publications == Timo Honkela, Wlodzislaw Duch, Mark Girolami and Samuel Kaski (editors): Artificial Neural Networks and Machine Learning, Springer, 2011. Jorma Laaksonen and Timo Honkela (editors): Advances in Self-Organizing Maps, Springer, 2011. Timo Honkela: Rauhankone. Gaudeamus, 2017.
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Steve Omohundro

Stephen Malvern Omohundro (born 1959) is an American computer scientist whose areas of research include Hamiltonian physics, dynamical systems, programming languages, machine learning, machine vision, and the social implications of artificial intelligence. His current work uses rational economics to develop safe and beneficial intelligent technologies for better collaborative modeling, understanding, innovation, and decision making. == Education == Omohundro has degrees in physics and mathematics from Stanford University (Phi Beta Kappa) and a Ph.D. in physics from the University of California, Berkeley. == Learning algorithms == Omohundro started the "Vision and Learning Group" at the University of Illinois, which produced 4 Masters and 2 Ph.D. theses. His work in learning algorithms included a number of efficient geometric algorithms, the manifold learning task and various algorithms for accomplishing this task, other related visual learning and modelling tasks, the best-first model merging approach to machine learning (including the learning of Hidden Markov Models and Stochastic Context-free Grammars), and the Family Discovery Learning Algorithm, which discovers the dimension and structure of a parameterized family of stochastic models. == Self-improving artificial intelligence and AI safety == Omohundro started Self-Aware Systems in Palo Alto, California to research the technology and social implications of self-improving artificial intelligence. He is an advisor to the Machine Intelligence Research Institute on artificial intelligence. He argues that rational systems exhibit problematic natural "drives" that will need to be countered in order to build intelligent systems safely. His papers, talks, and videos on AI safety have generated extensive interest. He has given many talks on self-improving artificial intelligence, cooperative technology, AI safety, and connections with biological intelligence. == Programming languages == At Thinking Machines Corporation, Cliff Lasser and Steve Omohundro developed Star Lisp, the first programming language for the Connection Machine. Omohundro joined the International Computer Science Institute (ICSI) in Berkeley, California, where he led the development of the open source programming language Sather. Sather is featured in O'Reilly's History of Programming Languages poster. == Physics and dynamical systems theory == Omohundro's book Geometric Perturbation Theory in Physics describes natural Hamiltonian symplectic structures for a wide range of physical models that arise from perturbation theory analyses. He showed that there exist smooth partial differential equations which stably perform universal computation by simulating arbitrary cellular automata. The asymptotic behavior of these PDEs is therefore logically undecidable. With John David Crawford he showed that the orbits of three-dimensional period doubling systems can form an infinite number of topologically distinct torus knots and described the structure of their stable and unstable manifolds. == Mathematica and Apple tablet contest == From 1986 to 1988, he was an Assistant Professor of Computer science at the University of Illinois at Urbana-Champaign and cofounded the Center for Complex Systems Research with Stephen Wolfram and Norman Packard. While at the University of Illinois, he worked with Stephen Wolfram and five others to create the symbolic mathematics program Mathematica. He and Wolfram led a team of students that won an Apple Computer contest to design "The Computer of the Year 2000." Their design entry "Tablet" was a touchscreen tablet with GPS and other features that finally appeared when the Apple iPad was introduced 22 years later. == Other contributions == Subutai Ahmad and Steve Omohundro developed biologically realistic neural models of selective attention. As a research scientist at the NEC Research Institute, Omohundro worked on machine learning and computer vision, and was a co-inventor of U.S. Patent 5,696,964, "Multimedia Database Retrieval System Which Maintains a Posterior Probability Distribution that Each Item in the Database is a Target of a Search." === Pirate puzzle === Omohundro developed an extension to the game theoretic pirate puzzle featured in Scientific American. == Outreach == Omohundro has sat on the Machine Intelligence Research Institute board of advisors. He has written extensively on artificial intelligence, and has warned that "an autonomous weapons arms race is already taking place" because "military and economic pressures are driving the rapid development of autonomous systems".
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C3D Toolkit

C3D Toolkit is a proprietary cross-platform geometric modeling kit software developed by Russian C3D Labs (previously part of ASCON Group). It's written in C++ . It can be licensed by other companies for use in their 3D computer graphics software products. The most widely known software in which C3D Toolkit is typically used are computer aided design (CAD), computer-aided manufacturing (CAM), and computer-aided engineering (CAE) systems. C3D Toolkit provides routines for 3D modeling, 3D constraint solving, polygonal mesh-to-B-rep conversion, 3D visualization, and 3D file conversions etc. == History == Nikolai Golovanov is a graduate of the Mechanical Engineering department of Bauman Moscow State Technical University as a designer of space launch vehicles. Upon his graduation, he began with the Kolomna Engineering Design bureau, which at the time employed the future founders of ASCON, Alexander Golikov and Tatiana Yankina. While at the bureau, Dr Golovanov developed software for analyzing the strength and stability of shell structures. In 1989, Alexander Golikov and Tatiana Yankina left Kolomna to start up ASCON as a private company. Although they began with just an electronic drawing board, even then they were already conceiving the idea of three-dimensional parametric modeling. This radical concept eventually changed flat drawings into three-dimensional models. The ASCON founders shared their ideas with Nikolai Golovanov, and in 1996 he moved to take up his current position with ASCON. As of 2012 he was involved in developing algorithms for C3D Toolkit. In 2012 the earliest version of the C3D Modeller kernel was extracted from KOMPAS-3D CAD. It was later adopted to a range of different platforms and advertised as a separate product. == Overview == It incorporates five modules: C3D Modeler constructs geometric models, generates flat projections of models, performs triangulations, calculates the inertial characteristics of models, and determines whether collisions occur between the elements of models; C3D Modeler for ODA enables advanced 3D modeling operations through the ODA's standard "OdDb3DSolid" API from the Open Design Alliance; C3D Solver makes connections between the elements of geometric models, and considers the geometric constraints of models being edited; C3D B-Shaper converts polygonal models to boundary representation (B-rep) bodies; C3D Vision controls the quality of rendering for 3D models using mathematical apparatus and software, and the workstation hardware; C3D Converter reads and writes geometric models in a variety of standard exchange formats. == Features == == Development == == Applications == Since 2013 - the date the company started issuing a license for the toolkit -, several companies have adopted C3D software components for their products, users include: Recently, C3D Modeler has been adapted to ODA Platform. In April 2017, C3D Viewer was launched for end users. The application allows to read 3D models in common formats and write it to the C3D file format. Free version is available.
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Sophia Ananiadou

Sophia Ananiadou is a Greek-British computer scientist and computational linguist. She led the development of and directs the National Centre for Text Mining (NaCTeM) in the United Kingdom. She is also Professor in Computer Science in the Department of Computer Science at the University of Manchester. Her research focusses on biomedical text mining and natural language processing and has fed into the development of numerous applications that, for example, facilitate the discovery of new knowledge, enable exploration of historical archives, allow semantic search of biomedical literature, reduce human effort in screening search hits for production of systematic reviews, enable enrichment of metabolic pathway models with evidence from the literature, allow discovery of risk in the construction industry from health and safety incident reports and enable interoperability of components in text mining workflows. == Education == Ananiadou was educated at the Lycée français St Joseph in Athens, Greece (1969–1975). She received a Bachelor of Arts (Ptychion) from the University of Athens (1979), a Master of Advanced Studies (DEA) in Linguistics from Paris VII, Jussieu, France (1980), a DEA in Literature from Paris IV, Sorbonne, France (1984) and a PhD in Computational linguistics from the University of Manchester Institute of Science and Technology (UMIST), in 1988. == Career and research == Ananiadou was a research assistant at Dalle Molle Institute for Semantic and Cognitive Studies (ISSCO, 1983–1984), a research assistant (1985–1988) then research associate (1988–1993) in the department of language engineering at UMIST, senior lecturer at Manchester Metropolitan University (1993–1999), senior lecturer then reader in the School of Computing Science and Engineering, University of Salford (2000–2005), then reader in the School of Computer Science, University of Manchester (2005–2009). Since 2009, she has served as professor in computer science in the Department of Computer Science at the University of Manchester. In July 2025, she became deputy director of the Christabel Pankhurst Institute for health technology research and innovation, University of Manchester. From 2018–2026, she served as the deputy director of the Institute for Data Science and Artificial Intelligence, University of Manchester. She is a senior lead researcher of the ARCHIMEDES research unit of the Athena Research Centre, Greece. ARCHIMEDES is a research and innovation hub fostering international collaboration and knowledge exchange on Artificial Intelligence and Data Science. On February 7, 2025, she was appointed a member of the Artificial Intelligence Sectoral Scientific Council of the Greek Ministry of Development (announcement of appointment in Greek). She is also a Visiting Distinguished Research Fellow in the Knowledge and Information Research Team at the Artificial Intelligence Research Center (AIRC), Japan, which is a research unit of the Japanese National Institute of Advanced Industrial Science and Technology (AIST). In addition, she was appointed to the honorary position of Adjunct Professor of Wuhan University, People's Republic of China, for the period October 2025 to October 2028, collaborating with the School of Artificial Intelligence. Ananiadou has published since 1986, has an h-index of 81 and a Research.com United Kingdom ranking in Computer Science of 104. She is also ranked number 1 internationally in text mining by ScholarGPS. In addition, she is included in the Stanford/Elsevier Top 2% Scientist Rankings for 2025. Ananiadou received a Diplôme de traducteur (Diploma of Translator) from the Institut français d'Athènes, Greece (1979) and a Certificate in Counselling from the University of Salford, UK (2004). === Awards and honours === In 2019, in recognition of her contributions in Artificial Intelligence and text mining for Biomedicine, Ananiadou received an honorary doctorate from the University of the Aegean, on the 20th anniversary of its Department of Mediterranean Studies, Rhodes. Ananiadou received the Unstructured Information Management Architecture (UIMA) innovation award from IBM three years running (2006, 2007 & 2008). She was awarded the Daiwa Adrian Prize in 2004 and also received a Japan Trust award from the Ministry of Education, Japan in 1997. Ananiadou was a Turing Fellow of the Alan Turing Institute in London from 2018 to 2023. Since 2021, she is a member and, since 2024, a Fellow, of the ELLIS Society, the professional society of the cross-national European Laboratory for Learning and Intelligent Systems. Ananiadou served as vice president (VP) of the European Association for Terminology from 1997 to 1999. At the 28th International Conference on Computational Linguistics (COLING 2020), she received, with M. Li and H. Takamura, an Outstanding Paper designation for the paper "A Neural Model for Aggregating Coreference Annotation in Crowdsourcing".
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Linguistic Systems

Linguistic Systems, Inc., also known as LSI, provides language translation services (conversion) for all media in over 115 languages. LSI focuses on the translation of legal, medical, business, institutional, academic, government and personal documents. LSI is headquartered in Cambridge, Massachusetts. == About LSI == Linguistic Systems, Inc. (LSI) was founded in 1967 by Martin Roberts. LSI's translates to/from 115 languages, DTP, audio-visual conversions, software localization, consecutive and simultaneous interpreting services, foreign brand name analysis, and machine translation with post-editing. LSI has provided translation services to over half of the Fortune 500 companies and most of the Fortune 100. Among its clients are AT&T, Boeing, Citigroup, Coca-Cola, DuPont, Exxon-Mobil, General Electric, General Motors, Hewlett-Packard, IBM, Johnson & Johnson, Pfizer, Procter & Gamble, Simon & Schuster, Time Warner, Verizon, and Walmart. As of 2013, LSI had a network of more than 7,000 translators who translate into their native languages; These include lawyers, scientists, engineers, and other bilingual professionals.
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Regular language

In theoretical computer science and formal language theory, a regular language (also called a rational language) is a formal language that can be defined by a regular expression, in the strict sense in theoretical computer science (as opposed to many modern regular expression engines, which are augmented with features that allow the recognition of non-regular languages). Alternatively, a regular language can be defined as a language recognised by a finite automaton. The equivalence of regular expressions and finite automata is known as Kleene's theorem (after American mathematician Stephen Cole Kleene). In the Chomsky hierarchy, regular languages are the languages generated by Type-3 grammars. == Formal definition == The collection of regular languages over an alphabet Σ is defined recursively as follows: The empty language ∅ is a regular language. For each a ∈ Σ (a belongs to Σ), the singleton language {a} is a regular language. If A is a regular language, A (Kleene star) is a regular language. Due to this, the empty string language {ε} is also regular. If A and B are regular languages, then A ∪ B (union) and A • B (concatenation) are regular languages. No other languages over Σ are regular. See Regular expression § Formal language theory for syntax and semantics of regular expressions. == Examples == All finite languages are regular; in particular the empty string language {ε} = ∅ is regular. Other typical examples include the language consisting of all strings over the alphabet {a, b} which contain an even number of as, or the language consisting of all strings of the form: several as followed by several bs. A simple example of a language that is not regular is the set of strings {anbn | n ≥ 0}. Intuitively, it cannot be recognized with a finite automaton, since a finite automaton has finite memory and it cannot remember the exact number of a's. Techniques to prove this fact rigorously are given below. == Equivalent formalisms == A regular language satisfies the following equivalent properties: it is the language of a regular expression (by the above definition) it is the language accepted by a nondeterministic finite automaton (NFA) it is the language accepted by a deterministic finite automaton (DFA) it can be generated by a regular grammar it is the language accepted by an alternating finite automaton it is the language accepted by a two-way finite automaton it can be generated by a prefix grammar it can be accepted by a read-only Turing machine it can be defined in monadic second-order logic (Büchi–Elgot–Trakhtenbrot theorem) it is recognized by some finite syntactic monoid M, meaning it is the preimage {w ∈ Σ | f(w) ∈ S} of a subset S of a finite monoid M under a monoid homomorphism f : Σ → M from the free monoid on its alphabet the number of equivalence classes of its syntactic congruence is finite. (This number equals the number of states of the minimal deterministic finite automaton accepting L.) Properties 10. and 11. are purely algebraic approaches to define regular languages; a similar set of statements can be formulated for a monoid M ⊆ Σ. In this case, equivalence over M leads to the concept of a recognizable language. Some authors use one of the above properties different from "1." as an alternative definition of regular languages. Some of the equivalences above, particularly those among the first four formalisms, are called Kleene's theorem in textbooks. Precisely which one (or which subset) is called such varies between authors. One textbook calls the equivalence of regular expressions and NFAs ("1." and "2." above) "Kleene's theorem". Another textbook calls the equivalence of regular expressions and DFAs ("1." and "3." above) "Kleene's theorem". Two other textbooks first prove the expressive equivalence of NFAs and DFAs ("2." and "3.") and then state "Kleene's theorem" as the equivalence between regular expressions and finite automata (the latter said to describe "recognizable languages"). A linguistically oriented text first equates regular grammars ("4." above) with DFAs and NFAs, calls the languages generated by (any of) these "regular", after which it introduces regular expressions which it terms to describe "rational languages", and finally states "Kleene's theorem" as the coincidence of regular and rational languages. Other authors simply define "rational expression" and "regular expressions" as synonymous and do the same with "rational languages" and "regular languages". Apparently, the term regular originates from a 1951 technical report where Kleene introduced regular events and explicitly welcomed "any suggestions as to a more descriptive term". Noam Chomsky, in his 1959 seminal article, used the term regular in a different meaning at first (referring to what is called Chomsky normal form today), but noticed that his finite state languages were equivalent to Kleene's regular events. == Closure properties == The regular languages are closed under various operations, that is, if the languages K and L are regular, so is the result of the following operations: the set-theoretic Boolean operations: union K ∪ L, intersection K ∩ L, and complement L, hence also relative complement K − L. the regular operations: K ∪ L, concatenation ⁠ K ∘ L {\displaystyle K\circ L} ⁠, and Kleene star L. the trio operations: string homomorphism, inverse string homomorphism, and intersection with regular languages. As a consequence they are closed under arbitrary finite state transductions, like quotient K / L with a regular language. Even more, regular languages are closed under quotients with arbitrary languages: If L is regular then L / K is regular for any K. the reverse (or mirror image) LR. Given a nondeterministic finite automaton to recognize L, an automaton for LR can be obtained by reversing all transitions and interchanging starting and finishing states. This may result in multiple starting states; ε-transitions can be used to join them. == Decidability properties == Given two deterministic finite automata A and B, it is decidable whether they accept the same language. As a consequence, using the above closure properties, the following problems are also decidable for arbitrarily given deterministic finite automata A and B, with accepted languages LA and LB, respectively: Containment: is LA ⊆ LB ? Disjointness: is LA ∩ LB = {} ? Emptiness: is LA = {} ? Universality: is LA = Σ ? Membership: given a ∈ Σ, is a ∈ LB ? For regular expressions, the universality problem is NP-complete already for a singleton alphabet. For larger alphabets, that problem is PSPACE-complete. If regular expressions are extended to allow also a squaring operator, with "A2" denoting the same as "AA", still just regular languages can be described, but the universality problem has an exponential space lower bound, and is in fact complete for exponential space with respect to polynomial-time reduction. For a fixed finite alphabet, the theory of the set of all languages – together with strings, membership of a string in a language, and for each character, a function to append the character to a string (and no other operations) – is decidable, and its minimal elementary substructure consists precisely of regular languages. For a binary alphabet, the theory is called S2S. == Complexity results == In computational complexity theory, the complexity class of all regular languages is sometimes referred to as REGULAR or REG and equals DSPACE(O(1)), the decision problems that can be solved in constant space (the space used is independent of the input size). REGULAR ≠ AC0, since it (trivially) contains the parity problem of determining whether the number of 1 bits in the input is even or odd and this problem is not in AC0. On the other hand, REGULAR does not contain AC0, because the nonregular language of palindromes, or the nonregular language { 0 n 1 n : n ∈ N } {\displaystyle \{0^{n}1^{n}:n\in \mathbb {N} \}} can both be recognized in AC0. If a language is not regular, it requires a machine with at least Ω(log log n) space to recognize (where n is the input size). In other words, DSPACE(o(log log n)) equals the class of regular languages. In practice, most nonregular problems are studied in a setting with at least logarithmic space, as this is the amount of space required to store a pointer into the input tape. == Location in the Chomsky hierarchy == To locate the regular languages in the Chomsky hierarchy, one notices that every regular language is context-free. The converse is not true: for example, the language consisting of all strings having the same number of as as bs is context-free but not regular. To prove that a language is not regular, one often uses the Myhill–Nerode theorem and the pumping lemma. Other approaches include using the closure properties of regular languages or quantifying Kolmogorov complexity. Important subclasses of regular languages include: Finite languages, those containing only a finite number of words. These are regular la
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Hierarchical control system

A hierarchical control system (HCS) is a form of control system in which a set of devices and governing software is arranged in a hierarchical tree. When the links in the tree are implemented by a computer network, then that hierarchical control system is also a form of networked control system. == Overview == A human-built system with complex behavior is often organized as a hierarchy. For example, a command hierarchy has among its notable features the organizational chart of superiors, subordinates, and lines of organizational communication. Hierarchical control systems are organized similarly to divide the decision making responsibility. Each element of the hierarchy is a linked node in the tree. Commands, tasks and goals to be achieved flow down the tree from superior nodes to subordinate nodes, whereas sensations and command results flow up the tree from subordinate to superior nodes. Nodes may also exchange messages with their siblings. The two distinguishing features of a hierarchical control system are related to its layers. Each higher layer of the tree operates with a longer interval of planning and execution time than its immediately lower layer. The lower layers have local tasks, goals, and sensations, and their activities are planned and coordinated by higher layers which do not generally override their decisions. The layers form a hybrid intelligent system in which the lowest, reactive layers are sub-symbolic. The higher layers, having relaxed time constraints, are capable of reasoning from an abstract world model and performing planning. A hierarchical task network is a good fit for planning in a hierarchical control system. Besides artificial systems, an animal's control systems are proposed to be organized as a hierarchy. In perceptual control theory, which postulates that an organism's behavior is a means of controlling its perceptions, the organism's control systems are suggested to be organized in a hierarchical pattern as their perceptions are constructed so. == Control system structure == The accompanying diagram is a general hierarchical model which shows functional manufacturing levels using computerised control of an industrial control system. Referring to the diagram; Level 0 contains the field devices such as flow and temperature sensors, and final control elements, such as control valves Level 1 contains the industrialised Input/Output (I/O) modules, and their associated distributed electronic processors. Level 2 contains the supervisory computers, which collate information from processor nodes on the system, and provide the operator control screens. Level 3 is the production control level, which does not directly control the process, but is concerned with monitoring production and monitoring targets Level 4 is the production scheduling level. == Applications == === Manufacturing, robotics and vehicles === Among the robotic paradigms is the hierarchical paradigm in which a robot operates in a top-down fashion, heavy on planning, especially motion planning. Computer-aided production engineering has been a research focus at NIST since the 1980s. Its Automated Manufacturing Research Facility was used to develop a five layer production control model. In the early 1990s DARPA sponsored research to develop distributed (i.e. networked) intelligent control systems for applications such as military command and control systems. NIST built on earlier research to develop its Real-Time Control System (RCS) and Real-time Control System Software which is a generic hierarchical control system that has been used to operate a manufacturing cell, a robot crane, and an automated vehicle. In November 2007, DARPA held the Urban Challenge. The winning entry, Tartan Racing employed a hierarchical control system, with layered mission planning, motion planning, behavior generation, perception, world modelling, and mechatronics. === Artificial intelligence === Subsumption architecture is a methodology for developing artificial intelligence that is heavily associated with behavior based robotics. This architecture is a way of decomposing complicated intelligent behavior into many "simple" behavior modules, which are in turn organized into layers. Each layer implements a particular goal of the software agent (i.e. system as a whole), and higher layers are increasingly more abstract. Each layer's goal subsumes that of the underlying layers, e.g. the decision to move forward by the eat-food layer takes into account the decision of the lowest obstacle-avoidance layer. Behavior need not be planned by a superior layer, rather behaviors may be triggered by sensory inputs and so are only active under circumstances where they might be appropriate. Reinforcement learning has been used to acquire behavior in a hierarchical control system in which each node can learn to improve its behavior with experience. James Albus, while at NIST, developed a theory for intelligent system design named the Reference Model Architecture (RMA), which is a hierarchical control system inspired by RCS. Albus defines each node to contain these components. Behavior generation is responsible for executing tasks received from the superior, parent node. It also plans for, and issues tasks to, the subordinate nodes. Sensory perception is responsible for receiving sensations from the subordinate nodes, then grouping, filtering, and otherwise processing them into higher level abstractions that update the local state and which form sensations that are sent to the superior node. Value judgment is responsible for evaluating the updated situation and evaluating alternative plans. World Model is the local state that provides a model for the controlled system, controlled process, or environment at the abstraction level of the subordinate nodes. At its lowest levels, the RMA can be implemented as a subsumption architecture, in which the world model is mapped directly to the controlled process or real world, avoiding the need for a mathematical abstraction, and in which time-constrained reactive planning can be implemented as a finite-state machine. Higher levels of the RMA however, may have sophisticated mathematical world models and behavior implemented by automated planning and scheduling. Planning is required when certain behaviors cannot be triggered by current sensations, but rather by predicted or anticipated sensations, especially those that come about as result of the node's actions.
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Best AI Website Builders in 2026

Comparing the best AI website builder? An AI website builder is software that uses machine learning to help you get more done — it lowers the barrier so anyone can produce professional output. Privacy matters too: check whether your data trains the model and whether a no-log or enterprise tier is available. Whether you are a beginner or a pro, the right AI website builder slots into your workflow and pays for itself fast. Below we compare features, pricing, and real output so you can choose with confidence.
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Lori Levin

Lorraine Susan (Lori) Levin is an American computer scientist and computational linguist specializing in natural language processing, particularly involving syntax, morphosyntax, and languages with small corpora. She is a research professor in the Language Technologies Institute of the Carnegie Mellon University School of Computer Science, and one of the founders of the North American Computational Linguistics Open Competition. == Education and career == Levin has a 1979 bachelor's degree in linguistics (summa cum laude) from the University of Pennsylvania, and a 1986 Ph.D. in linguistics from the Massachusetts Institute of Technology. Her dissertation, Operations on Lexical Forms: Unaccusative Rules in Germanic Languages, was jointly supervised by Joan Bresnan and Kenneth L. Hale. She worked as an assistant professor of linguistics at the University of Pittsburgh from 1983 until 1988, when she joined the Carnegie Mellon University Language Technologies Institute. == Recognition == Levin was named as a Fellow of the Association for Computational Linguistics in 2025, "for pioneering work on the use of phonetics, syntax, lexical semantics and dialogue modeling in machine translation and in the transfer of NLP technologies to low resource languages, as well as an enduring contribution to the North American Computational Linguistics Olympiad". Levin was awarded the Antonio Zampolli prize of the ELRA Language Resources Association at the LREC 2026 conference.
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