AI Face Generator From Photo Free

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Cyber attribution

In the area of computer security, cyber attribution is an attribution of cybercrime, i.e., finding who perpetrated a cyberattack. Uncovering a perpetrator may give insights into various security issues, such as infiltration methods, communication channels, etc., and may help in enacting specific countermeasures. Cyber attribution is a costly endeavor requiring considerable resources and expertise in cyber forensic analysis. For governments and other major players dealing with cybercrime would require not only technical solutions, but legal and political ones as well, and for the latter ones cyber attribution is crucial. Attributing a cyberattack is difficult, and of limited interest to companies that are targeted by cyberattacks. In contrast, secret services often have a compelling interest in finding out whether a state is behind the attack. A further challenge in attribution of cyberattacks is the possibility of a false flag attack, where the actual perpetrator makes it appear that someone else caused the attack. Every stage of the attack may leave artifacts, such as entries in log files, that can be used to help determine the attacker's goals and identity. In the aftermath of an attack, investigators often begin by saving as many artifacts as they can find, and then try to determine the attacker.
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EuroMatrixPlus

The EuroMatrixPlus is a project that ran from March 2009 to February 2012. EuroMatrixPlus succeeded a project called EuroMatrix (September 2006 to February 2009) and continued in further development and improvement of machine translation (MT) systems for languages of the European Union (EU). == Project objectives == EuroMatrixPlus focused on achieving several goals: To continue advance of MT technology (create MT systems for all official EU languages and provide other MT researchers with existing data and infrastructure). To continually expand and investigate different MT approaches and techniques; to stay open to novel combinations of methods of MT. To bring MT to the users. Users post-edit output of statistical models and the system learns from the feedback and improves itself. Two groups of users were aimed at: Professional translators and translation agencies Users who voluntarily translate texts into their native language To contribute to MT research in Europe. To produce sample application for automatic translation of news and web pages and make that application freely accessible. == Outcome == EuroMatrixPlus contributed to MT field in several ways. It continued in development of an open source statistical MT engine Moses. The project worked on research in hybrid approaches to MT (combination of rule-based and statistical techniques). Several “MT Marathons” and annual evaluation campaigns were organized by the project. The project also resulted in releasing of 196 scientific publications. The results of the work were arranged into ten work packages: WP1: Rich Tree-Based Statistical Translation WP2: Hybrid Machine Translation WP3: Advanced Learning Methods for MT WP4: Open Source Tools and Data WP5: "WikiTrans" Translation Environments WP6: Integrated Localisation Workflow WP7: Evaluation Campaign WP8: Project Management and Dissemination WP9: Integrating Slovak Language Resources WP10: HPSG-based Statistical Translation === Software and data === Here is a list of software and data that were released by the project: Appraise – an open source tool for manual evaluation of MT output BURGER – Bulgarian Resource BulTreeBank – Treebank of Bulgarian CSLM toolkit – free tool for training continuous space language models (CSLM) to large tasks Caitra – tool for post-editing MT results Europarl – European Parliament parallel corpus IRSTLM toolkit – tool for training language models Joshua – an open-source statistical machine translation decoder for hierarchical and syntax-based MT MT Server Land – an open-source architecture for MT Moses – statistical MT MultiUN Corpora – parallel corpus extracted from the United Nations Website PCEDT 2.0 – Prague Czech-English Dependency Treebank PEDT 2.0 – English part of the Prague Czech-English Dependency Treebank Slovak corpora – English-Slovak and Czech-Slovak as well as a Slovak-English and a Slovak-Czech parallel corpus Slovak treebank – A dependency treebank TermEx – RBMT-Suited Statistical Terminology Extraction Tool Treex, TectoMT == Funding == The EuroMatrixPlus project was sponsored by EU Information Society Technology program. Total cost of the project was 5 942 121 €, from which the European Union contributed 4 266 896 €. == Project members == To ensure advance in MT, several organizations that are experts in various disciplines (linguistics, computer science, mathematics, translation) were brought together to cooperate on EuroMatrixPlus. The consortium consisted of academic as well as commercial partners. Academic partners were the University of Edinburgh (United Kingdom), DFKI – German Research Centre for Artificial Intelligence (Germany), Charles University (Czech Republic), Johns Hopkins University (United States), University of Le Mans (France), Fondazione Bruno Kessler (Italy), Dublin City University (Ireland). Two institutions joined about one year into the project. These were the L'udovít Štúr Institute of Linguistics (Slovak Republic) and IICT – Institute of Information and Communication Technologies at the Bulgarian Academy of Sciences (Bulgaria). Commercial partners included Lucy Software and Services GmbH (Germany) and CEET s.r.o. (Czech Republic). Coordination of the project was in hands of DFKI with its Language Technology Lab in Saarbrücken. The principal investigator and scientific coordinator was Hans Uszkoreit, a professor of Computational Linguistics at Saarland University.
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Machine-readable medium and data

In communications and computing, a machine-readable medium (or computer-readable medium) is a medium capable of storing data in a format easily readable by a digital computer or a sensor. It contrasts with human-readable medium and data. The result is called machine-readable data or computer-readable data, and the data itself can be described as having machine-readability. == Data == Machine-readable data must be structured data. Attempts to create machine-readable data occurred as early as the 1960s. At the same time that seminal developments in machine-reading and natural-language processing were releasing (like Weizenbaum's ELIZA), people were anticipating the success of machine-readable functionality and attempting to create machine-readable documents. One such example was musicologist Nancy B. Reich's creation of a machine-readable catalog of composer William Jay Sydeman's works in 1966. In the United States, the OPEN Government Data Act of 14 January 2019 defines machine-readable data as "data in a format that can be easily processed by a computer without human intervention while ensuring no semantic meaning is lost." The law directs U.S. federal agencies to publish public data in such a manner, ensuring that "any public data asset of the agency is machine-readable". Machine-readable data may be classified into two groups: human-readable data that is marked up so that it can also be read by machines (e.g. microformats, RDFa, HTML), and data file formats intended principally for processing by machines (CSV, RDF, XML, JSON). These formats are only machine readable if the data contained within them is formally structured; exporting a CSV file from a badly structured spreadsheet does not meet the definition. Machine readable is not synonymous with digitally accessible. A digitally accessible document may be online, making it easier for humans to access via computers, but its content is much harder to extract, transform, and process via computer programming logic if it is not machine-readable. Extensible Markup Language (XML) is designed to be both human- and machine-readable, and Extensible Stylesheet Language Transformations (XSLT) is used to improve the presentation of the data for human readability. For example, XSLT can be used to automatically render XML in Portable Document Format (PDF). Machine-readable data can be automatically transformed for human-readability but, generally speaking, the reverse is not true. For purposes of implementation of the Government Performance and Results Act (GPRA) Modernization Act, the Office of Management and Budget (OMB) defines "machine readable format" as follows: "Format in a standard computer language (not English text) that can be read automatically by a web browser or computer system. (e.g.; xml). Traditional word processing documents and portable document format (PDF) files are easily read by humans but typically are difficult for machines to interpret. Other formats such as extensible markup language (XML), (JSON), or spreadsheets with header columns that can be exported as comma separated values (CSV) are machine readable formats. As HTML is a structural markup language, discreetly labeling parts of the document, computers are able to gather document components to assemble tables of contents, outlines, literature search bibliographies, etc. It is possible to make traditional word processing documents and other formats machine readable but the documents must include enhanced structural elements." == Media == Examples of machine-readable media include magnetic media such as magnetic disks, cards, tapes, and drums, punched cards and paper tapes, optical discs, barcodes and magnetic ink characters. Common machine-readable technologies include magnetic recording, processing waveforms, and barcodes. Optical character recognition (OCR) can be used to enable machines to read information available to humans. Any information retrievable by any form of energy can be machine-readable. Examples include: Acoustics Chemical Photochemical Electrical Semiconductor used in volatile RAM microchips Floating-gate transistor used in non-volatile memory cards Radio transmission Magnetic storage Mechanical Tins And Swins Punched card Paper tape Music roll Music box cylinder or disk Grooves (See also: Audio Data) Phonograph cylinder Gramophone record DictaBelt (groove on plastic belt) Capacitance Electronic Disc Optics Optical storage Thermodynamic == Applications == === Documents === === Catalogs === === Dictionaries === === Passports ===
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Deborah Raji

Inioluwa Deborah Raji (born 1995/1996) is a Nigerian-Canadian computer scientist and socio-tech leader who works on algorithmic bias, AI accountability, and algorithmic auditing. A current Mozilla fellow, she has been recognized by MIT Technology Review and Forbes as one of the world's top young innovators. Raji started her work with racial bias in technology during her internship with Clarifai when she recognized that people of color were more often tagged for NSFW compared to white people. Raji has previously worked with Joy Buolamwini, Timnit Gebru, and the Algorithmic Justice League on researching gender and racial bias in facial recognition technology. Her work on racial bias in facial recognition has forced companies to ultimately change their practices. She has also worked with Google’s Ethical AI team and been a research fellow at the Partnership on AI and AI Now Institute at New York University working on how to operationalize ethical considerations in machine learning engineering practice. She was working on a computer vision model that would help clients flag inappropriate images as NSFW. == Early life and education == Raji was born in Port Harcourt, Nigeria, and moved to Mississauga, Ontario, Canada, when she was four years old. Eventually her family moved to Ottawa. She attended Colonel By Secondary School and completed the International Baccalaureate programme. She studied Engineering Science at the University of Toronto, graduating in 2019. In 2015, she founded Project Include, a nonprofit providing increased student access to engineering education, mentorship, and resources in low income and immigrant communities in the Greater Toronto Area. She started a Doctor of Philosophy - PhD, in Computer Science from the University of California, Berkeley in Aug 2021. == Career and research == Raji worked with Joy Buolamwini at the MIT Media Lab and Algorithmic Justice League, where she audited commercial facial recognition technologies from Microsoft, Amazon, IBM, Face++, and Kairos. They found that these technologies were significantly less accurate for darker-skinned women than for white men. With support from other top AI researchers and increased public pressure and campaigning, their work led IBM and Amazon to agree to support facial recognition regulation and later halt the sale of their product to police for at least a year. Raji also interned at machine learning startup Clarifai, where she worked on a computer vision model for flagging images. She participated in a research mentorship program at Google and worked with their Ethical AI team on creating model cards, a documentation framework for more transparent machine learning model reporting. She also co-led the development of internal auditing practices at Google. Her contributions at Google were separately presented and published at the AAAI conference and ACM Conference on Fairness, Accountability, and Transparency. In 2019, Raji was a summer research fellow at The Partnership on AI working on setting industry machine learning transparency standards and benchmarking norms. Raji was a Tech Fellow at the AI Now Institute worked on algorithmic and AI auditing. Currently, she is a fellow at the Mozilla Foundation researching algorithmic auditing and evaluation. Raji's work on bias in facial recognition systems has been highlighted in the 2020 documentary Coded Bias directed by Shalini Kantayya. She also took part in the 2026 documentary The AI Doc: Or How I Became an Apocaloptimist directed by Daniel Roher. == Awards == 2019 Venture Beat AI Innovations Award in category AI for Good (received with Joy Buolamwini and Timnit Gebru) 2020 MIT Technology Review 35 Under 35 Innovator Award 2020 EFF Pioneer Award (received with Buolamwini and Gebru) 2021 Forbes 30 Under 30 Award in Enterprise Technology 2021 100 Brilliant Women in AI Ethics Hall of Fame Honoree 2023 Time magazine 100 Most Influential People in AI
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BevQ

BevQ is a queue management mobile application developed by Faircode Technologies of Kochi, Kerala. It is provided by the Kerala State Beverages Corporation under Government of Kerala. == History == This app was released together by the Government of Kerala and the Kerala State Beverages Corporation in order to implement social distancing in the liquor stores Kerala in the case of the COVID-19 pandemic in Kerala and to reduce the congestion of people. The BevQ App was released by Faircode Technologies on 27 May 2020 on the Google Play Store. In January 2021, the app was withdrawn as bars had opened. In June 2021, there was a commitment from the Kerala CM that the App will be relaunched again. It has been reported that over 132,000 new users downloaded the app in the 48 hours after the announcement. == Achievements == The BEVQ app, which works only in the state of Kerala, beat all other Indian food and drink apps in 2020 to see the highest growth in year-on-year sessions, according to the State of Mobile 2021 report by App Annie. The app even beat the likes of Domino’s, which is used all across India. Around 300 government Liquor shops and 900 private liquor shops were enlisted in the platform. More than 200 million unique users registered in the platform. About 250,000 tokens were given out a day.
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How to Choose an AI Subtitle Generator

Shopping for the best AI subtitle generator? An AI subtitle generator is software that uses machine learning to help you get more done — it keeps getting smarter as the underlying models improve. Pricing, accuracy, and the size of the model behind the tool are the three factors that most affect daily usefulness. Whether you are a beginner or a pro, the right AI subtitle generator 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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Top 10 Conversational AI Platforms Compared (2026)

In search of the best conversational AI platform? An conversational AI platform 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 conversational AI platform slots into your workflow and pays for itself fast. We tested the leading options and ranked them by quality, value, and ease of use.
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Ranking SVM

In machine learning, a ranking SVM is a variant of the support vector machine algorithm, which is used to solve certain ranking problems (via learning to rank). The ranking SVM algorithm was published by Thorsten Joachims in 2002. The original purpose of the algorithm was to improve the performance of an internet search engine. However, it was found that ranking SVM also can be used to solve other problems such as Rank SIFT. == Description == The ranking SVM algorithm is a learning retrieval function that employs pairwise ranking methods to adaptively sort results based on how 'relevant' they are for a specific query. The ranking SVM function uses a mapping function to describe the match between a search query and the features of each of the possible results. This mapping function projects each data pair (such as a search query and clicked web-page, for example) onto a feature space. These features are combined with the corresponding click-through data (which can act as a proxy for how relevant a page is for a specific query) and can then be used as the training data for the ranking SVM algorithm. Generally, ranking SVM includes three steps in the training period: It maps the similarities between queries and the clicked pages onto a certain feature space. It calculates the distances between any two of the vectors obtained in step 1. It forms an optimization problem which is similar to a standard SVM classification and solves this problem with the regular SVM solver. == Background == === Ranking method === Suppose C {\displaystyle \mathbb {C} } is a data set containing N {\displaystyle N} elements c i {\displaystyle c_{i}} . r {\displaystyle r} is a ranking method applied to C {\displaystyle \mathbb {C} } . Then the r {\displaystyle r} in C {\displaystyle \mathbb {C} } can be represented as a N × N {\displaystyle N\times N} binary matrix. If the rank of c i {\displaystyle c_{i}} is higher than the rank of c j {\displaystyle c_{j}} , i.e. r c i < r c j {\displaystyle r\ c_{i} Read more →
Automated machine learning

Automated machine learning (AutoML) is the process of automating the tasks of applying machine learning to real-world problems. It is the combination of automation and ML. AutoML potentially includes every stage from beginning with a raw dataset to building a machine learning model ready for deployment. AutoML was proposed as an artificial intelligence-based solution to the growing challenge of applying machine learning. The high degree of automation in AutoML aims to allow non-experts to make use of machine learning models and techniques without requiring them to become experts in machine learning. Automating the process of applying machine learning end-to-end additionally offers the advantages of producing simpler solutions, faster creation of those solutions, and models that often outperform hand-designed models. Common techniques used in AutoML include hyperparameter optimization, meta-learning and neural architecture search. == Comparison to the standard approach == In a typical machine learning application, practitioners have a set of input data points to be used for training. The raw data may not be in a form that all algorithms can be applied to. To make the data amenable for machine learning, an expert may have to apply appropriate data pre-processing, feature engineering, feature extraction, and feature selection methods. After these steps, practitioners must then perform algorithm selection and hyperparameter optimization to maximize the predictive performance of their model. If deep learning is used, the architecture of the neural network must also be chosen manually by the machine learning expert. Each of these steps may be challenging, resulting in significant hurdles to using machine learning. AutoML aims to simplify these steps for non-experts, and to make it easier for them to use machine learning techniques correctly and effectively. AutoML plays an important role within the broader approach of automating data science, which also includes challenging tasks such as data engineering, data exploration and model interpretation and prediction. == Targets of automation == Automated machine learning can target various stages of the machine learning process. Steps to automate are: Data preparation and ingestion (from raw data and miscellaneous formats) Column type detection; e.g., Boolean, discrete numerical, continuous numerical, or text Column intent detection; e.g., target/label, stratification field, numerical feature, categorical text feature, or free text feature Task detection; e.g., binary classification, regression, clustering, or ranking Feature engineering Feature selection Feature extraction Meta-learning and transfer learning Detection and handling of skewed data and/or missing values Model selection - choosing which machine learning algorithm to use, often including multiple competing software implementations Ensembling - a form of consensus where using multiple models often gives better results than any single model Hyperparameter optimization of the learning algorithm and featurization Neural architecture search Pipeline selection under time, memory, and complexity constraints Selection of evaluation metrics and validation procedures Problem checking Leakage detection Misconfiguration detection Analysis of obtained results Creating user interfaces and visualizations == Challenges and Limitations == There are a number of key challenges being tackled around automated machine learning. A big issue surrounding the field is referred to as "development as a cottage industry". This phrase refers to the issue in machine learning where development relies on manual decisions and biases of experts. This is contrasted to the goal of machine learning which is to create systems that can learn and improve from their own usage and analysis of the data. Basically, it's the struggle between how much experts should get involved in the learning of the systems versus how much freedom they should be giving the machines. However, experts and developers must help create and guide these machines to prepare them for their own learning. To create this system, it requires labor intensive work with knowledge of machine learning algorithms and system design. Additionally, other challenges include meta-learning and computational resource allocation.
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Chris Callison-Burch

Chris Callison-Burch is an American computer scientist and professor of computer and information science at the University of Pennsylvania (Penn), specializing in natural language processing (NLP), artificial intelligence (AI), and crowdsourcing. He is recognised for his contributions to machine translation, paraphrase generation, and the application of large language models (LLMs) to AI challenges, with over 200 publications cited more than 33,000 times. Callison-Burch has influenced public policy on AI and copyright, testifying before the U.S. Congress in 2023 on generative AI’s implications. He serves as the faculty director for Penn’s Online Master of Science in Engineering in AI program. == Education == Callison-Burch earned his PhD in Computer Science from the University of Edinburgh in 2008, focusing on machine translation and paraphrasing techniques. His doctoral research developed statistical methods for generating paraphrases in machine translation systems, laying the foundation for his later NLP work. Prior to his PhD, he studied at Stanford University, where he developed an interest in computational linguistics. == Career == After his PhD, Callison-Burch joined the Centre for Language and Speech Processing at Johns Hopkins University as a research faculty member from 2008 to 2013, working on NLP projects, including machine translation and crowdsourcing for creating training data. In 2013, he joined the University of Pennsylvania as an assistant professor in the Department of Computer and Information Science and was promoted to associate professor in 2017, and to full professor in 2024. At Penn, Callison-Burch teaches courses on AI and NLP, including CIS 5300 (Natural Language Processing) and CIS 5210 (Artificial Intelligence), which attract over 500 students annually. He directs Penn’s Online Master of Science in Engineering in AI program, launched in 2025. He teaches AI and NLP courses on Coursera, reaching thousands of global learners. Callison-Burch was a part-time visiting researcher at Google in 2019 and 2020, where he collaborated on applying Google's LLM to Dungeons & Dragons dialogues. In 2023, he took a sabbatical at the Allen Institute for AI (AI2), where he contributed to vision-language models. == Research == Callison-Burch’s research focuses on NLP, AI, and crowdsourcing, with significant contributions to machine translation, paraphrase generation, and LLMs for tasks like text simplification and bias detection. His early work developed crowdsourcing methods for machine translation, leveraging non-expert annotators for paraphrase-based evaluation, influencing platforms like Amazon Mechanical Turk. Recent projects have included several notable works. Molmo and PixMo (2025) are open-weight vision-language models developed with AI2, achieving state-of-the-art multimodal performance and earning a Best Paper Honourable Mention at CVPR 2025. Also in 2025, his work on Calibrating Large Language Models with Sample Consistency improves LLM reliability via sample-based calibration, presented at NAACL 2025. The Media Bias Detector (2025) is a real-time tool analysing selection and framing bias in news, using LLMs to detect persuasive language differences (e.g., Russian vs. English Wikipedia). Holodeck (2024) is a language-guided system for generating 3D embodied AI environments, presented at CVPR 2024. BORDIRLINES (2024) is a dataset for cross-lingual retrieval-augmented generation, focusing on culturally sensitive tasks. He has co-authored over 200 publications, featured at conferences like ACL, EMNLP, and CVPR. == Awards and recognition == Callison-Burch has received numerous awards: Best Paper Honourable Mention at CVPR 2025 for "Molmo and PixMo". Best Paper Award at the Workshop on Cognitive Modelling and Computational Linguistics (CMCL) 2024 for "Evaluating Vision-Language Models on Bistable Images". Best Paper Award at STARSEM 2016 for "So-Called Non-Subsective Adjectives". Best Paper Award at the Workshop on Sense, Concept and Entity Representations 2017 for "Word Sense Filtering Improves Embedding-Based Lexical Substitution". Honourable Mention Award at CHI 2018 for "A Data-Driven Analysis of Workers’ Earnings on Amazon Mechanical Turk". Google Faculty Research Award (2013) for crowdsourcing in NLP. Sloan Research Fellowship (2014). He has received research funding from Google, Microsoft, Amazon, Facebook, Roblox, DARPA, IARPA, and NSF. His h-index is 72, with over 33,000 citations. He served as General Chair of ACL 2017 and as the Program Co-Chair EMNLP 2015. == Public policy and testimony == On May 17, 2023, Callison-Burch testified before the U.S. House Subcommittee on Courts, Intellectual Property, and the Internet on AI and copyright law. His testimony emphasised generative AI’s role in creative industries and the need for balanced copyright frameworks. He has appeared on Fox News to discuss AI’s societal impact, and discussed its impact with other print news sources. He contributes to AI ethics discussions, including workshops on AI’s effects on writing and creative professions.
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The Best Free AI Text-to-image Tool for Beginners

Looking for the best AI text-to-image tool? An AI text-to-image tool is software that uses machine learning to help you get more done — it can save you hours every week by automating repetitive work. Most options offer a generous free tier, with paid plans unlocking higher limits, faster processing, and team features. Whether you are a beginner or a pro, the right AI text-to-image tool 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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Hidden Markov model

A hidden Markov model (HMM) is a Markov model in which the observations are dependent on a latent (or hidden) Markov process (referred to as X {\displaystyle X} ). An HMM requires that there be an observable process Y {\displaystyle Y} whose outcomes depend on the outcomes of X {\displaystyle X} in a known way. Since X {\displaystyle X} cannot be observed directly, the goal is to learn about state of X {\displaystyle X} by observing Y {\displaystyle Y} . By definition of being a Markov model, an HMM has an additional requirement that the outcome of Y {\displaystyle Y} at time t = t 0 {\displaystyle t=t_{0}} must be "influenced" exclusively by the outcome of X {\displaystyle X} at t = t 0 {\displaystyle t=t_{0}} and that the outcomes of X {\displaystyle X} and Y {\displaystyle Y} at t < t 0 {\displaystyle t
Arabic Ontology

Arabic Ontology is a website offering linguistic ontology services for the Arabic language which can be used like the online site WordNet. Users can use Arabic Ontology to classify or clarify the concepts and meanings of Arabic terms. == Ontology Structure == The ontology structure (i.e., data model) is similar to WordNet's structure. Each concept in the database is given a unique concept identifier (URI), informally described by a gloss, and lexicalized by one or more synonymous lemma terms. Each term-concept pair is called a sense, and is given a SenseID. A set of senses is called synset. Concepts and senses are described by further attributes such as era and area — to specify example usage and ontological analysis. Semantic relations are defined between concepts. Some important entities are included in the ontology, such as individual countries and bodies of water. These individuals are given separate IndividualIDs and linked with their concepts through the InstanceOf relation. == Mappings to other resources == Concepts in the Arabic Ontology are mapped to synsets in WordNet, as well as to BFO and DOLCE. Terms used in the Arabic Ontology are mapped to lemmas in the LDC's SAMA database. == Applications == Arabic Ontology can be used in many application domains, such as: Information retrieval, to enrich queries (e.g., in search engines) and improve the quality of the results, i.e. meaningful search rather than string-matching search; Machine translation and word-sense disambiguation, by finding the exact mapping of concepts across languages, especially that the Arabic ontology is also mapped to the WordNet; Data Integration and interoperability in which the Arabic ontology can be used as a semantic reference to link databases and information systems; Semantic Web and Web 3.0, by using the Arabic ontology as a semantic reference to disambiguate the meanings used in websites; among many other applications. == URLs Design == The URLs in the Arabic Ontology are designed according to the W3C's Best Practices for Publishing Linked Data, as described in the following URL schemes. This allows one to also explore the whole database like exploring a graph: Ontology Concept: Each concept in the Arabic Ontology has a ConceptID and can be accessed using: https://{domain}/concept/{ConceptID | Term}. In case of a term, the set of concepts that this term lexicalizes are all retrieved. In case of a ConceptID, the concept and its direct subtypes are retrieved, e.g. https://ontology.birzeit.edu/concept/293198 Semantic relations: Relationships between concepts can be accessed using these schemes: (i) the URL: https:// {domain}/concept/{RelationName}/{ConceptID} allows retrieval of relationships among ontology concepts. (ii) the URL: https://{domain}/lexicalconcept/{RelationName}/{lexicalConceptID} allows retrieval of relations between lexical concepts. For example, https://ontology.birzeit.edu/concept/instances/293121 retrieves the instances of the concept 293121. The relations that are currently used in our database are: {subtypes, type, instances, parts, related, similar, equivalent}.
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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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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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