A public computer (or public access computer) is any of various computers available in public areas. Some places where public computers may be available are libraries, schools, or dedicated facilities run by government. Public computers share similar hardware and software components to personal computers, however, the role and function of a public access computer is entirely different. A public access computer is used by many different untrusted individuals throughout the course of the day. The computer must be locked down and secure against both intentional and unintentional abuse. Users typically do not have authority to install software or change settings. A personal computer, in contrast, is typically used by a single responsible user, who can customize the machine's behavior to their preferences. Public access computers are often provided with tools such as a PC reservation system to regulate access. The world's first public access computer center was the Marin Computer Center in California, co-founded by David and Annie Fox in 1977. == Kiosks == A kiosk is a special type of public computer using software and hardware modifications to provide services only about the place the kiosk is in. For example, a movie ticket kiosk can be found at a movie theater. These kiosks are usually in a secure browser with zero access to the desktop. Many of these kiosks may run Linux, however, ATMs, a kiosk designed for depositing money, often run Windows XP. == Public computers in the United States == === Library computers === In the United States and Canada, almost all public libraries have computers available for the use of patrons, though some libraries will impose a time limit on users to ensure others will get a turn and keep the library less busy. Users are often allowed to print documents that they have created using these computers, though sometimes for a small fee. ==== Privacy ==== Privacy is an important part of the public library institution, since the libraries entitle the public to intellectual freedom. Use of any computer or network may create records of users' activities that can jeopardize their privacy. It is possible for a patron to jeopardize their privacy if they do not delete cache, clear cookies, or documents from the public computer. In order for a member of the public to remain private on a computer, the American Library Association (ALA) has guidelines. These give patrons an idea of the right way to keep using public library computers. In their provision of services to library users, librarians have an ethical responsibility, expressed in the ALA Code of Ethics, to preserve users' right to privacy. A librarian is also responsible for giving users an understanding of private patron use and access. Libraries must ensure that users have the following rights when browsing on public computers: the computer automatically will clear a users history; libraries should display privacy screens so users do not see another patron's screen; updating software for effective safety measures; restoration data software to clear documents that users may have left on their computers and to combat possible malware; security practices; and making users aware of any possible monitoring of their browsing activities. Users can also view the Library Privacy Checklist for Public Access Computers and Networks to better understand what libraries strive for when protecting privacy. === School computers === The U.S. government has given money to many school boards to purchase computers for educational applications. Schools may have multiple computer labs, which contain these computers for students to use. There is usually Internet access on these machines, but some schools will put up a blocking service to limit the websites that students are able to access to only include educational resources, such as Google. In addition to controlling the content students are viewing, putting up these blocks can also help to keep the computers safe by preventing students from downloading malware and other threats. However, the effectiveness of such content filtering systems is questionable since it can easily be circumvented by using proxy websites, Virtual Private Networks, and for some weak security systems, merely knowing the IP address of the intended website is enough to bypass the filter. School computers often have advanced operating system security to prevent tech-savvy students from inflicting damage (i.e. the Windows Registry Editor and Task Manager, etc.) are disabled on Microsoft Windows machines. Schools with very advanced tech services may also install a locked down BIOS/firmware or make kernel-level changes to the operating system, precluding the possibility of unauthorized activity.
Digital supply chain security
Digital supply chain security refers to efforts to enhance cyber security within the supply chain. It is a subset of supply chain security and is focused on the management of cyber security requirements for information technology systems, software and networks, which are driven by threats such as cyber-terrorism, malware, data theft and the advanced persistent threat (APT). Typical supply chain cyber security activities for minimizing risks include buying only from trusted vendors, disconnecting critical machines from outside networks, and educating users on the threats and protective measures they can take. The acting deputy undersecretary for the National Protection and Programs Directorate for the United States Department of Homeland Security, Greg Schaffer, stated at a hearing that he is aware that there are instances where malware has been found on imported electronic and computer devices sold within the United States. == Examples of supply chain cyber security threats == Network or computer hardware that is delivered with malware installed on it already. Malware that is inserted into software or hardware (by various means) Vulnerabilities in software applications and networks within the supply chain that are discovered by malicious hackers Counterfeit computer hardware == Related U.S. government efforts == Comprehensive National Cyber Initiative Defense Procurement Regulations: Noted in section 806 of the National Defense Authorization Act International Strategy for Cyberspace: White House lays out for the first time the U.S.’s vision for a secure and open Internet. The strategy outlines three main themes: diplomacy, development and defense. Diplomacy: The strategy sets out to “promote an open, interoperable, secure and reliable information and communication infrastructure” by establishing norms of acceptable state behavior built through consensus among nations. Development: Through this strategy the government seeks to “facilitate cybersecurity capacity-building abroad, bilaterally and through multilateral organizations.” The objective is to protect the global IT infrastructure and to build closer international partnerships to sustain open and secure networks. Defense: The strategy calls out that the government “will ensure that the risks associated with attacking or exploiting our networks vastly outweigh the potential benefits” and calls for all nations to investigate, apprehend and prosecute criminals and non-state actors who intrude and disrupt network systems. == Related government efforts around the world == Common Criteria offers with Evaluation Assurance Level(EAL) 4 an opportunity to evaluate all relevant aspects of the digital supply chain security like the product, the development environment, IT systems security, the processes in human resource, physical security and with the module ALC_FLR.3 (Systematic Flaw Remediation) also security update processes and methods even by physical site visits. EAL 4 is mutually recognized in countries that signed the SOGIS-MRA and up to ELA 2 in countries the signed the CCRA but including ALC_FRL.3. Russia: Russia has had non-disclosed functionality certification requirements for several years and has recently initiated the National Software Platform effort based on open-source software. This reflects the apparent desire for national autonomy, reducing dependence on foreign suppliers. India: Recognition of supply chain risk in its draft National Cybersecurity Strategy. Rather than targeting specific products for exclusion, it is considering Indigenous Innovation policies, giving preferences to domestic ITC suppliers in order to create a robust, globally competitive national presence in the sector. China: Deriving from goals in the 11th Five Year Plan (2006–2010), China introduced and pursued a mix of security-focused and aggressive Indigenous Innovation policies. China is requiring an indigenous innovation product catalog be used for its government procurement and implementing a Multi-level Protection Scheme (MLPS) which requires (among other things) product developers and manufacturers to be Chinese citizens or legal persons, and product core technology and key components must have independent Chinese or indigenous intellectual property rights. == Private sector efforts == SLSA (Supply-chain Levels for Software Artifacts) is an end-to-end framework for ensuring the integrity of software artifacts throughout the software supply chain. The requirements are inspired by Google’s internal "Binary Authorization for Borg" that has been in use for the past 8+ years and that is mandatory for all of Google's production workloads. The goal of SLSA is to improve the state of the industry, particularly open source, to defend against the most pressing integrity threats. With SLSA, consumers can make informed choices about the security posture of the software they consume. == Other references == Financial Sector Information Sharing and Analysis Center International Strategy for Cyberspace (from the White House) NSTIC SafeCode Whitepaper Archived 2013-10-21 at the Wayback Machine Trusted Technology Forum and the Open Trusted Technology Provider Standard (O-TTPS) Archived 2012-01-03 at the Wayback Machine Cyber Supply Chain Security Solution Malware Implants in Firmware Supply Chain in the Software Era INFORMATION AND COMMUNICATIONS TECHNOLOGY SUPPLY CHAIN RISK MANAGEMENT TASK FORCE: INTERIM REPORT
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".
Jun'ichi Tsujii
Jun'ichi Tsujii (辻井 潤一, Tsujii Jun'ichi; born 7 February 1949) is a Japanese computer scientist specializing in natural language processing and text mining, particularly in the field of biology and bioinformatics. == Education == Tsujii received his Bachelor of Engineering, Master of Engineering and PhD degrees in electrical engineering from Kyoto University in 1971, 1973, and 1978 respectively. He was Assistant Professor and Associate Professor at Kyoto University, before accepting a position as Professor of Computational Linguistics at the University of Manchester Institute of Science and Technology (UMIST) in 1988. He was President of the Association for Computational Linguistics (ACL) in 2006, and has been a permanent member of the International Committee on Computational Linguistics (ICCL) since 1992, and the chair of the committee since 2014. == Research == Since May 2015, Tsujii has been the director of the Artificial Intelligence Research Center at the National Institute of Advanced Industrial Science and Technology, Japan. Tsujii was previously a Principal Researcher at Microsoft Research Asia (MSRA). Before joining MSRA, he was a professor at the University of Tokyo, where he belonged to both the School of Inter-faculty Initiative on Informatics and the Graduate School of Information Science and Technology. Tsujii is also a Visiting Professor and Scientific Advisor at the National Centre for Text Mining (NaCTeM) at the University of Manchester in the United Kingdom. == Awards == On 14 May 2010, Tsujii was awarded the Medals of Honor with Purple Ribbon, one of Japan's highest awards, presented to influential contributors in the fields of art, academics or sports. In September 2014, Tsujii was awarded the FUNAI Achievement Award at the Forum on Information Technology (FIT), which took place at the University of Tsukuba. The award is presented to distinguished individuals engaged in research or related business activities in the field of Information Technology who have produced excellent achievements in the field, are still active in leading positions and have strong impact on young students and researchers. In December 2014, Tsujii was named as an ACL Fellow, in recognition of his significant contributions to MT, parsing by unification-based grammar and text mining for biology. In March 2016, Tsujii was awarded Okawa Prize for his contribution to the field of Natural Language Processing, Machine Translation and Text Mining, together with Professor Jaime Carbonnel of CMU. In August 2021, Tsujii received ACL Lifetime Achievement Award, which is considered the most prestigious award in the field of Computational Linguistics and Natural Language Processing. In May 2022, Tsujii received the Order of the Sacred Treasure, Gold Rays and Neck Ribbon, from the Japanese government. In October 2024, Tsujii was designated a Person of Cultural Merit. == Selected publications == Oiwa, Hidekazu; Tsujii, Jun'ichi (2014). Common Space Embedding of Primal-Dual Relation Semantic Spaces. COLING 2014. Dublin. pp. 1579–1590. Taura, K.; Matsuzaki, T.; Miwa, M.; Kamoshida, Y.; Yokoyama, D.; Dun, N.; Shibata, T.; Jun, C. S.; Tsujii, J. (2013). "Design and implementation of GXP make – A workflow system based on make". Future Generation Computer Systems. 29 (2): 662–672. doi:10.1016/j.future.2011.05.026. S2CID 31627886. Sun, X.; Zhang, Y.; Matsuzaki, T.; Tsuruoka, Y.; Tsujii, J. (2013). "Probabilistic Chinese word segmentation with non-local information and stochastic training". Information Processing & Management. 49 (3): 626–636. doi:10.1016/j.ipm.2012.12.003. Mu, T.; Goulermas, J. Y.; Tsujii, J.; Ananiadou, S. (2012). "Proximity-Based Frameworks for Generating Embeddings from Multi-Output Data". IEEE Transactions on Pattern Analysis and Machine Intelligence. 34 (11): 2216–2232. Bibcode:2012ITPAM..34.2216M. doi:10.1109/TPAMI.2012.20. PMID 23289130. S2CID 711467. Miwa, M.; Sætre, R.; Kim, J. D.; Tsujii, J. (2010). "Event Extraction with Complex Event Classification Using Rich Features". Journal of Bioinformatics and Computational Biology. 08 (1): 131–146. doi:10.1142/S0219720010004586. PMID 20183879. Kim, J. D.; Ohta, T.; Tsujii, J. (2008). "Corpus annotation for mining biomedical events from literature". BMC Bioinformatics. 9 10. doi:10.1186/1471-2105-9-10. PMC 2267702. PMID 18182099. Miyao, Y.; Tsujii, J. (2008). "Feature Forest Models for Probabilistic HPSG Parsing". Computational Linguistics. 34: 35–80. doi:10.1162/coli.2008.34.1.35. S2CID 885002. Sagae, Kenji; Tsujii, Jun'ichi (2007). Dependency Parsing and Domain Adaptation with LR Models and Parser Ensembles. EMNLP-CoNLL. pp. 1044–1050. Ananiadou, S; Pyysalo, S; Tsujii, J; Kell, D. B. (2010). "Event extraction for systems biology by text mining the literature". Trends in Biotechnology. 28 (7): 381–90. doi:10.1016/j.tibtech.2010.04.005. PMID 20570001. Tsuruoka, Y.; Tateishi, Y.; Kim, J. D.; Ohta, T.; McNaught, J.; Ananiadou, S.; Tsujii, J. (2005). "Developing a Robust Part-of-Speech Tagger for Biomedical Text". Advances in Informatics. Lecture Notes in Computer Science. Vol. 3746. p. 382. doi:10.1007/11573036_36. ISBN 978-3-540-29673-7. S2CID 206592413. Tsuruoka, Y.; Tsujii, J. (2005). Bidirectional inference with the easiest-first strategy for tagging sequence data. Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing - HLT '05. pp. 467–474. doi:10.3115/1220575.1220634. Tsujii, J.; Ananiadou, S. (2005). "Thesaurus or Logical Ontology, Which One Do We Need for Text Mining?". Language Resources and Evaluation. 39: 77–90. doi:10.1007/s10579-005-2697-0. S2CID 3204827. Kazama, J. I.; Tsujii, J. I. (2005). "Maximum Entropy Models with Inequality Constraints: A Case Study on Text Categorization". Machine Learning. 60 (1–3): 159–194. doi:10.1007/s10994-005-0911-3. hdl:10119/3305. Matsuzaki, T.; Miyao, Y.; Tsujii, J. I. (2005). Probabilistic CFG with latent annotations. 43rd Annual Meeting on Association for Computational Linguistics - ACL '05. p. 75. doi:10.3115/1219840.1219850. Kim, J. -D.; Ohta, T.; Tateisi, Y.; Tsujii, J. (2003). "GENIA corpus--a semantically annotated corpus for bio-textmining". Bioinformatics. 19: i180–i182. doi:10.1093/bioinformatics/btg1023. PMID 12855455. Hirschman, L.; Park, J. C.; Tsujii, J.; Wong, L.; Wu, C. H. (2002). "Accomplishments and challenges in literature data mining for biology". Bioinformatics. 18 (12): 1553–1561. doi:10.1093/bioinformatics/18.12.1553. PMID 12490438. Torisawa, K.; Tsujii, J. I. (1996). Computing phrasal-signs in HPSG prior to parsing. 16th conference on Computational linguistics -. Vol. 2. p. 949. doi:10.3115/993268.993332.
Ginger Software
Ginger Software is an American and Israeli start-up specialized in natural language processing and AI. The main products are tools aiming to improve written communications, develop English speaking skills and boost productivity. The company was founded in 2008 by Yael Karov and Avner Zangvil. Ginger Software uses the context of complete sentences to suggest corrections. In December 2011, Ginger Software was one of nine projects approved by the Board of Governors of the Israel-U.S. Binational Industrial Research and Development Foundation for a funding of $8.1 million. The company also raised $3 million from private Israeli and US investors in 2009. In May, 2014 Intel acquired one of Ginger's business units and the rights to use the company's patented technology. == Founders == Before founding Ginger Software, Yael Karov had worked with Rosetta Genomics as its Chief Technology Officer and Vice President of Research and Development from 2003 to 2006, and with ClickSoftware Technologies as a Director of Research and Development from 1990 to 1994. Karov also founded Agentics, a company specializing in free-text classification of e-commerce product information based on natural language processing, in 1996. Avner Zangvil is the co-founder of Ginger Software. Zangvil co-founded Menta Software in 1996 with his brother Arnon Zangvil to develop a product that transforms any Windows-based application into a Web-enabled application usable from any remote computer running a Web browser. Menta was acquired by GraphOn Corporation in 2001. == Technology == Ginger Software uses patented software algorithms in the field of natural language processing. The company claims that the algorithm allows it to correct the written sentences with relatively high accuracy (eliminating up to 95 percent of writing errors), compared to standard spell checkers. Its unique algorithm allows the software to understand the context of the sentence rather than correcting based solely on a word. According to its founder, Karov, the software operates on the logic of sentence context in addition to the memory of a database of words. The company is at the heart of a growing revolution in the world of assistive technology. Ginger claims that the benefits of the software have been leveraged by native English and non-native speakers alike, and have also found value in niche markets like dyslexia management. They further claim that ESL users derive great benefit from the use of the software, as it lets them write error-free English text. Its use also extends to native English speaking business professionals and students who use it as a 'safety net' for their email edits, as well as international students writing in English. More recently, the company has focused on implementing its technology in mobile devices as an integral component of its mobile keyboard products. == Products == Ginger Software products include Ginger Page, a cross-platform writing enhancement app, and Ginger Keyboard which is available for Android devices. Ginger Writer can be used as an online service or installed on your PC or Mac. It supports MS-Word, MS-Outlook, MS-PowerPoint, Microsoft Edge, Chrome, and functions as a writing enhancement app for Android and iOS mobile devices. Its main feature is English grammar and spelling checker that runs seamlessly with the different user interfaces. It also has an advanced paraphrasing tool, contextual synonyms and definitions, translation and a text-to-speech function that enables users to hear sentences before and after correction. Ginger Keyboard for Android replaces the stock keyboard and functions as a productivity boosting keyboard app. Featuring a full set of advanced keyboard features like Stream (swipe-like) typing, adaptive word prediction, a wide variety of customizable themes and emoji, Ginger Keyboard is the only 3rd party keyboard to offer proofreading and other writing tools via one tap access to Ginger Page. == Target segment == Ginger Software started off targeting people with dyslexia. The algorithm underlying the software studies a vast pool of proper sentences in English and builds a model of proper language. The software does not analyze the text at the level of the word, but of the whole sentence. Dyslexics can have trouble choosing the right word – hence the attention to the sentence as a whole. From 2010, Ginger Software included a new target segment in its marketing outreach – users of English as a second language (ESL). Its contextual-based writing correction tool could benefit those who are not proficient in the English language. == Business model == The main business model for consumers is freemium. The free version offers contextual-based grammar and spelling checker with some limitations. Its premium features include unlimited access to Grammar Checker, the grammar and spelling checker, and Sentence Rephraser the rephrasing tool. Ginger Keyboard is free to download and use, although it does offer in-app purchases like themes and theme packs. It also disables your original spell checker. Ginger also provides a powerful Rest API which can correct full documents in one call.
VieON
VieON is an mobile application for television and video on demand provided by VieON Joint Stock Company (formerly Dzones), a subsidiary of DatVietVAC Media and Entertainment Group in Vietnam. The app was launched in 2020, featuring over 140 domestic and international television channels, original series, popular entertainment programs known nationwide, top-tier sports events and live streaming of major events. Additionally, VieON provides animated films, television series and television programs from various countries such as South Korea and China. == History == The application was planned for development in 2016, with the cooperation of strategic consulting partner BCG Digital Ventures from the United States. Prior to 2020, VieON was a rebranded version of VTVcab ON, a product managed by Vietnam Cable Television Corporation (VTVCab) and DatVietVAC. On June 15, 2020, after four years of research and testing, the new version of VieON was officially released by DatVietVAC Group, with Vie Channel Joint Stock Company as the business entity and service provider. This is considered the official launch date of the application. On July 21, 2023, VieON transitioned its business operations and service provision to VieON Joint Stock Company. In January 2024, VieON officially launched its global version, VieON Global, targeting Vietnamese users living abroad. == Background == According to Kantar Media Vietnam, up to 84% of Vietnamese people aged 15–54 use social media daily, and in a similar survey by Nielsen, 90% of respondents said they watch live TV weekly. Additionally, according to research organization Muvi, Southeast Asia's OTT market revenue could reach $650 million annually starting next year. Understanding this, DatVietVAC Group has planned to research and develop an OTT application, even though the Vietnamese market already has some major players such as FPT Play and the international giant Netflix. Additionally, DatVietVAC does not hide its ambition to make this application the number one entertainment channel for Vietnamese people.
Bidyut Baran Chaudhuri
Bidyut Baran Chaudhuri (B. B. Chauduri) is a senior computer scientist and an emeritus professor of Techno India University in West Bengal, India. He is also adjuncted to Indian Statistical Institute, where he was a professor for about three decades. He was the founding Head of Computer Vision and Pattern Recognition Unit (which was established in 1994) of ISI. Moreover, he was a J.C. Bose Fellow and Indian National Academy of Engineering Distinguished Professor at ISI. He was the vice-president of the Society for Natural Language Technology Research (SNLTR). His primary research contributes to the fields of computer vision, image processing and pattern recognition. He is a pioneer of "Indian language script OCR". == Education == Chaudhuri received his BSc (Hons.), BTech and MTech degrees from University of Calcutta, India in 1969, 1972 and 1974, respectively and PhD Degree from Indian Institute of Technology Kanpur in 1980. He did his post-doc work during 1981-1982 from Queen's University, U.K, through Leverhulme Overseas Fellowship. He also worked as a visiting faculty at Tech University, Hannover during 1986-87 as well as at GSF Institute of Radiation Protection (now Leibnitz Institute), Munich in 1990 and 1992. == Awards and recognition == Chaudhuri has been elected as a Life Fellow of IEEE "for contributions to pattern recognition, especially Indian language script OCR, document processing and natural language processing". He has become a Fellow of International Association for Pattern Recognition (IAPR) "for contributions to character recognition and speech synthesis in Indian language". He is also Fellow of The World Academy of Sciences (TWAS), Indian National Science Academy (INSA), Indian National Academy of Engineering (INAE), National Academy of Sciences (NASI), and Institute of Electronics and Telecommunication Engineering (IETE). In 2011, Chaudhuri received the Om Prakash Bhasin Award for his contribution in the field of electronics and information technology. Chaudhuri's interview on some of his works has been reported in Indian newspaper as well. He is within world's top 2% scientists and top-10 Indian AI scientists according to a study conducted by Stanford University. He has also been featured as top-10 machine learning researcher from India.