AI App Kya Hai In Hindi

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  • Microsoft Clipchamp

    Microsoft Clipchamp

    Microsoft Clipchamp is a freemium video editing tool developed by Australian company Clipchamp Pty Ltd., a subsidiary of Microsoft. It is a web-based, non-linear editing software that allows users to import, edit, and export audiovisual material in a web browser window. The application is designed to be easy to use for beginners. Clipchamp has offices in Australia, the Philippines, Germany, and the United States. According to figures published by the company, at the beginning of 2021, it had more than 14 million users worldwide. In September 2021, Clipchamp Pty Ltd. was acquired by Microsoft. It has since been offered in a personal version through a Microsoft account and in a business or education version through a work or school account that is built on OneDrive and SharePoint. == Features == Microsoft Clipchamp has multiple features that allow further creativity and accessibility. Since July 2023, users can drag and drop files from their computer, OneDrive, and SharePoint (images, sound & video files) into a list of all media uploaded or inserted. Users can insert media into the video timeline as many times as they want. Users can replace an image, sound, or video clip with another by dragging and dropping it over the target. There is also a Gap Remover tool that removes gaps in the video. Videos can be trimmed, along with timings that can be edited. The user can crop videos and images, too. Text can be added anywhere on the screen, and can be in many fonts, and the size can be changed, too. Specific text color can be selected using presets or an HSV picker, and specific Text Styles (bold, medium, italics, normal) can be selected. The aspect ratio can also be selected, including 16:9, 9:16, 1:1, 4:5, 2:3, and 21:9. Clipchamp also supports numerous effects and transitions for videos and images. The user can export videos in 480p, 720p, and 1080p for free. Exporting GIFs are possible, while the video has to be 15 seconds or less. Microsoft Clipchamp uses a hybrid model of desktop and online application. In the personal version of Clipchamp (on Windows and in a web browser), video processing is all done locally on the computer and mobile phones, but the app itself runs online as a browser-based web app. This is done by uploading and saving project data and information like file names online but not the associated media files themselves. In the work version of Clipchamp, which is a part of Microsoft 365, media files are still processed locally but are automatically backed up to the user's OneDrive or SharePoint work or school account so that it can be accessed anywhere. This version also has integration with other Microsoft productivity services like Microsoft Teams and Microsoft Stream. == History == Clipchamp Pty Ltd. was founded as a startup company by Alexander Dreiling (current CEO), Dave Hewitt, Tobias Raub and Soeren Balko, in Brisbane, Australia, in 2013. In an interview given to SmartCompany, Dreiling commented that at first, the company was "trying to build an enormous, distributed supercomputer". Among the first software developed by the company's team was a tool for video compression and conversion. 2014 saw the official launch of the first version of the free, audiovisual browser-based software on the Clipchamp platform. When the supercomputer project ground to a halt, the team decided to keep going with the video programming technology, which was, in the words of Dreiling, "a tool that worked on Chromebooks". In June 2016, Clipchamp was valued at 1.1 million dollars, according to the Wall Street Journal. In the same month, the second version of Clipchamp was launched internationally. By 2018, the firm had amassed 6.5 million users, attracting investors such as Steve Baxter, who invested one million dollars. In 2020, Clipchamp set up a base in Seattle, USA, after achieving capital of 13.2 million dollars, from alliances made with investment funds such as Transition Level Investments, Tola Capital, and TEN13, among others. In February 2021, Clipchamp published on its website that it has 14 million users worldwide, registered in 250 countries and territories. At that time, the company announced that it had an audiovisual library of 800,000 files. On September 7, 2021, Microsoft announced the acquisition of Clipchamp. In a press release, they expressed their interest in learning more about the video content creation market. Johnson Winter Slattery advised Microsoft on its acquisition. Clipchamp was integrated as part of Windows 11 beginning on March 9, 2022, as part of Insider Preview Build 22572.

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  • MarkLogic Server

    MarkLogic Server

    MarkLogic Server is a document-oriented database developed by MarkLogic. It is a NoSQL multi-model database that evolved from an XML database to natively store JSON documents and RDF triples, the data model for semantics. MarkLogic is designed to be a data hub for operational and analytical data. == History == MarkLogic Server was built to address shortcomings with existing search and data products. The product first focused on using XML as the document markup standard and XQuery as the query standard for accessing collections of documents up to hundreds of terabytes in size. Currently the MarkLogic platform is widely used in publishing, government, finance and other sectors. MarkLogic's customers are mostly Global 2000 companies. == Technology == MarkLogic uses documents without upfront schemas to maintain a flexible data model. In addition to having a flexible data model, MarkLogic uses a distributed, scale-out architecture that can handle hundreds of billions of documents and hundreds of terabytes of data. It has received Common Criteria certification, and has high availability and disaster recovery. MarkLogic is designed to run on-premises and within public or private cloud environments like Amazon Web Services. == Features == Indexing MarkLogic indexes the content and structure of documents including words, phrases, relationships, and values in over 200 languages with tokenization, collation, and stemming for core languages. Functionality includes the ability to toggle range indexes, geospatial indexes, the RDF triple index, and reverse indexes on or off based on your data, the kinds of queries that you will run, and your desired performance. Full-text search MarkLogic supports search across its data and metadata using a word or phrase and incorporates Boolean logic, stemming, wildcards, case sensitivity, punctuation sensitivity, diacritic sensitivity, and search term weighting. Data can be searched using JavaScript, XQuery, SPARQL, and SQL. Semantics MarkLogic uses RDF triples to provide semantics for ease of storing metadata and querying. ACID Unlike other NoSQL databases, MarkLogic maintains ACID consistency for transactions. Replication MarkLogic provides high availability with replica sets. Scalability MarkLogic scales horizontally using sharding. MarkLogic can run over multiple servers, balancing the load or replicating data to keep the system up and running in the event of hardware failure. Security MarkLogic has built in security features such as element-level permissions and data redaction. Optic API for Relational Operations An API that lets developers view their data as documents, graphs or rows. Security MarkLogic provides redaction, encryption, and element-level security (allowing for control on read and write rights on parts of a document). == Applications == Banking Big Data Fraud prevention Insurance Claims Management and Underwriting Master data management Recommendation engines == Licensing == MarkLogic is available under various licensing and delivery models, namely a free Developer or an Essential Enterprise license.[3] Licenses are available from MarkLogic or directly from cloud marketplaces such as Amazon Web Services and Microsoft Azure. == Releases == 2001 – Cerisent XQE 1: ACID transactions, Full-text search, XML Storage, XQuery, Role-based security 2004 – Cerisent XQE 2: Scale-out architecture, Enhanced search (stemming, thesaurus, wildcard), Backup and restore 2005 – MarkLogic Server 3: Continuing search improvements, Content Processing Framework (including PDF, Word, Excel, PPT), Failover 2008 – MarkLogic Server 4: Geospatial search, entity extraction, advanced XQuery, performance, scalability enhancements, auditing 2011 – MarkLogic Server 5: Flexible replication / DDIL, real-time indexing, advanced search, improved analytics, concurrency enhancements 2012 – MarkLogic Server 6: REST and Java APIs, App Builder, enhanced UI, improved search 2013 – MarkLogic Server 7: Semantic graph, bitemporal data, tiered storage, improved search, better management 2015 – MarkLogic Server 8: A Native JSON storage, Server-side JavaScript, Bitemporal, Node.js client API, Incremental backup, Flexible replication[16] 2017 – MarkLogic Server 9: Data integration across Relational and Non-Relational data, Advanced Encryption, Element Level Security, Redaction 2019 – MarkLogic Server 10: Enhanced Data Hub, improved SQL, security, analytics performance, cloud support 2022 – MarkLogic Server 11: MarkLogic Ops Director (Monitoring and Administration Improvements), expanded PKI 2025 – MarkLogic Server 12: Generative AI and Native Vector Search, Graph Algorithm Support, Virtual TDEs (relational views on the fly)

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  • Information history

    Information history

    Information history may refer to the history of each of the categories listed below (or to combinations of them). It should be recognized that the understanding of, for example, libraries as information systems only goes back to about 1950. The application of the term information for earlier systems or societies is a retronym. == Academic discipline == Information history is an emerging discipline related to, but broader than, library history. An important introduction and review was made by Alistair Black (2006). A prolific scholar in this field is also Toni Weller, for example, Weller (2007, 2008, 2010a and 2010b). As part of her work Toni Weller has argued that there are important links between the modern information age and its historical precedents. A description from Russia is Volodin (2000). Alistair Black (2006, p. 445) wrote: "This chapter explores issues of discipline definition and legitimacy by segmenting information history into its various components: The history of print and written culture, including relatively long-established areas such as the histories of libraries and librarianship, book history, publishing history, and the history of reading. The history of more recent information disciplines and practice, that is to say, the history of information management, information systems, and information science. The history of contiguous areas, such as the history of the information society and information infrastructure, necessarily enveloping communication history (including telecommunications history) and the history of information policy. The history of information as social history, with emphasis on the importance of informal information networks." "Bodies influential in the field include the American Library Association’s Round Table on Library History, the Library History Section of the International Federation of Library Associations and Institutions (IFLA), and, in the U.K., the Library and Information History Group of the Chartered Institute of Library and Information Professionals (CILIP). Each of these bodies has been busy in recent years, running conferences and seminars, and initiating scholarly projects. Active library history groups function in many other countries, including Germany (The Wolfenbuttel Round Table on Library History, the History of the Book and the History of Media, located at the Herzog August Bibliothek), Denmark (The Danish Society for Library History, located at the Royal School of Library and Information Science), Finland (The Library History Research Group, University of Tamepere), and Norway (The Norwegian Society for Book and Library History). Sweden has no official group dedicated to the subject, but interest is generated by the existence of a museum of librarianship in Bods, established by the Library Museum Society and directed by Magnus Torstensson. Activity in Argentina, where, as in Europe and the U.S., a "new library history" has developed, is described by Parada (2004)." (Black (2006, p. 447). === Journals === Information & Culture (previously Libraries & the Cultural Record, Libraries & Culture) Library & Information History (until 2008: Library History; until 1967: Library Association. Library History Group. Newsletter) == Information technology (IT) == The term IT is ambiguous although mostly synonym with computer technology. Haigh (2011, pp. 432-433) wrote "In fact, the great majority of references to information technology have always been concerned with computers, although the exact meaning has shifted over time (Kline, 2006). The phrase received its first prominent usage in a Harvard Business Review article (Haigh, 2001b; Leavitt & Whisler, 1958) intended to promote a technocratic vision for the future of business management. Its initial definition was at the conjunction of computers, operations research methods, and simulation techniques. Having failed initially to gain much traction (unlike related terms of a similar vintage such as information systems, information processing, and information science) it was revived in policy and economic circles in the 1970s with a new meaning. Information technology now described the expected convergence of the computing, media, and telecommunications industries (and their technologies), understood within the broader context of a wave of enthusiasm for the computer revolution, post-industrial society, information society (Webster, 1995), and other fashionable expressions of the belief that new electronic technologies were bringing a profound rupture with the past. As it spread broadly during the 1980s, IT increasingly lost its association with communications (and, alas, any vestigial connection to the idea of anybody actually being informed of anything) to become a new and more pretentious way of saying "computer". The final step in this process is the recent surge in references to "information and communication technologies" or ICTs, a coinage that makes sense only if one assumes that a technology can inform without communicating". Some people use the term information technology about technologies used before the development of the computer. This is however to use the term as a retronym. =

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  • Tagsistant

    Tagsistant

    Tagsistant is a semantic file system for the Linux kernel, written in C and based on FUSE. Unlike traditional file systems that use hierarchies of directories to locate objects, Tagsistant introduces the concept of tags. == Design and differences with hierarchical file systems == In computing, a file system is a type of data store which could be used to store, retrieve and update files. Each file can be uniquely located by its path. The user must know the path in advance to access a file and the path does not necessarily include any information about the content of the file. Tagsistant uses a complementary approach based on tags. The user can create a set of tags and apply those tags to files, directories and other objects (devices, pipes, ...). The user can then search all the objects that match a subset of tags, called a query. This kind of approach is well suited for managing user contents like pictures, audio recordings, movies and text documents but is incompatible with system files (like libraries, commands and configurations) where the univocity of the path is a security requirement to prevent the access to a wrong content. == The tags/ directory == A Tagsistant file system features four main directories: archive/ relations/ stats/ tags/ Tags are created as sub directories of the tags/ directory and can be used in queries complying to this syntax: tags/subquery/[+/subquery/[+/subquery/]]/@/ where a subquery is an unlimited list of tags, concatenated as directories: tag1/tag2/tag3/.../tagN/ The portion of a path delimited by tags/ and @/ is the actual query. The +/ operator joins the results of different sub-queries in one single list. The @/ operator ends the query. To be returned as a result of the following query: tags/t1/t2/+/t1/t4/@/ an object must be tagged as both t1/ and t2/ or as both t1/ and t4/. Any object tagged as t2/ or t4/, but not as t1/ will not be retrieved. The query syntax deliberately violates the POSIX file system semantics by allowing a path token to be a descendant of itself, like in tags/t1/t2/+/t1/t4/@ where t1/ appears twice. As a consequence a recursive scan of a Tagsistant file system will exit with an error or endlessly loop, as done by Unix find: This drawback is balanced by the possibility to list the tags inside a query in any order. The query tags/t1/t2/@/ is completely equivalent to tags/t2/t1/@/ and tags/t1/+/t2/t3/@/ is equivalent to tags/t2/t3/+/t1/@/. The @/ element has the precise purpose of restoring the POSIX semantics: the path tags/t1/@/directory/ refers to a traditional directory and a recursive scan of this path will properly perform. == The reasoner and the relations/ directory == Tagsistant features a simple reasoner which expands the results of a query by including objects tagged with related tags. A relation between two tags can be established inside the relations/ directory following a three level pattern: relations/tag1/rel/tag2/ The rel element can be includes or is_equivalent. To include the rock tag in the music tag, the Unix command mkdir can be used: mkdir -p relations/music/includes/rock The reasoner can recursively resolve relations, allowing the creation of complex structures: mkdir -p relations/music/includes/rock mkdir -p relations/rock/includes/hard_rock mkdir -p relations/rock/includes/grunge mkdir -p relations/rock/includes/heavy_metal mkdir -p relations/heavy_metal/includes/speed_metal The web of relations created inside the relations/ directory constitutes a basic form of ontology. == Autotagging plugins == Tagsistant features an autotagging plugin stack which gets called when a file or a symlink is written. Each plugin is called if its declared MIME type matches The list of working plugins released with Tagsistant 0.6 is limited to: text/html: tags the file with each word in and <keywords> elements and with document, webpage and html too image/jpeg: tags the file with each Exif tag == The repository == Each Tagsistant file system has a corresponding repository containing an archive/ directory where the objects are actually saved and a tags.sql file holding tagging information as an SQLite database. If the MySQL database engine was specified with the --db argument, the tags.sql file will be empty. Another file named repository.ini is a GLib ini store with the repository configuration. Tagsistant 0.6 is compatible with the MySQL and Sqlite dialects of SQL for tag reasoning and tagging resolution. While porting its logic to other SQL dialects is possible, differences in basic constructs (especially the INTERSECT SQL keyword) must be considered. == The archive/ and stats/ directories == The archive/ directory has been introduced to provide a quick way to access objects without using tags. Objects are listed with their inode number prefixed. The stats/ directory features some read-only files containing usage statistics. A file configuration holds both compile time information and current repository configuration. == Main criticisms == It has been highlighted that relying on an external database to store tags and tagging information could cause the complete loss of metadata if the database gets corrupted. It has been highlighted that using a flat namespace tends to overcrowd the tags/ directory. This could be mitigated introducing triple tags.</p> <a href="https://bbs.aizhi.co/html/60b799932.html" class="read-more" title="Tagsistant">Read more →</a> </div> </article> </li> <li class="article-item"> <article class="article-card"> <a href="https://bbs.aizhi.co/html/269e399727.html" class="card-thumb-link" title="IruSoft"><img class="card-thumb" src="https://upload.wikimedia.org/wikipedia/commons/4/4a/FAE_visualization.jpg" alt="IruSoft" loading="lazy"></a> <div class="card-body"> <h2><a href="https://bbs.aizhi.co/html/269e399727.html" title="IruSoft">IruSoft</a></h2> <p class="article-excerpt">IruSoft (Arabic: آيروسوفت) is an insurance regulatory platform designated for licensing, supervision and inspection of the insurance sector within a country. The platform introduced unique supervision-technology (suptech), insurance-technology (insurtech) and regulatory-technology (regtech) automated modules by which a regulator requires less resources to ensure fairness, transparency and competition and to prevent conflicts of interest in the sector. IruSoft was founded by Abdullah Al-Salloum and owned by the Insurance Regulatory Unit in Kuwait. The Insurance Regulatory Unit optimized processing insurance-sector's customer complaints by issuing Resolution No. (1) of 2022 that introduced IruSoft's complaints public module; an automated resolution center, by which the process of receiving submitted complaints, passing them on to the platforms of licensed insurance companies, tracking matter-related discussions and updates and getting them escalated if unresolved to be discussed by a committee assigned by the unit is integrally automated and analyzed for better key performance indicators.</p> <a href="https://bbs.aizhi.co/html/269e399727.html" class="read-more" title="IruSoft">Read more →</a> </div> </article> </li> <li class="article-item"> <article class="article-card"> <a href="https://bbs.aizhi.co/html/93a799899.html" class="card-thumb-link" title="Reference data"><img class="card-thumb" src="https://upload.wikimedia.org/wikipedia/commons/thumb/5/57/Viewdata_Graphics_1.jpg/960px-Viewdata_Graphics_1.jpg" alt="Reference data" loading="lazy"></a> <div class="card-body"> <h2><a href="https://bbs.aizhi.co/html/93a799899.html" title="Reference data">Reference data</a></h2> <p class="article-excerpt">Reference data is data used to classify or categorize other data. Typically, they are static or slowly changing over time. Examples of reference data include: Units of measurement Country codes Corporate codes Fixed conversion rates e.g., weight, temperature, and length Calendar structure and constraints Reference data sets are sometimes alternatively referred to as a "controlled vocabulary" or "lookup" data. Reference data differs from master data. While both provide context for business transactions, reference data is concerned with classification and categorisation, while master data is concerned with business entities. A further difference between reference data and master data is that a change to the reference data values may require an associated change in business process to support the change, while a change in master data will always be managed as part of existing business processes. For example, adding a new customer or sales product is part of the standard business process. However, adding a new product classification (e.g. "restricted sales item") or a new customer type (e.g. "gold level customer") will result in a modification to the business processes to manage those items. == Externally-defined reference data == For most organisations, most or all reference data is defined and managed within that organisation. Some reference data, however, may be externally defined and managed, for example by standards organizations. An example of externally defined reference data is the set of country codes as defined in ISO 3166-1. == Reference data management == Curating and managing reference data is key to ensuring its quality and thus fitness for purpose. All aspects of an organisation, operational and analytical, are greatly dependent on the quality of an organization's reference data. Without consistency across business process or applications, for example, similar things may be described in quite different ways. Reference data gain in value when they are widely re-used and widely referenced. Examples of good practice in reference data management include: Formalize the reference data management Use external reference data as much as possible Govern the reference data specific to your enterprise Manage reference data at enterprise level Version control your reference data</p> <a href="https://bbs.aizhi.co/html/93a799899.html" class="read-more" title="Reference data">Read more →</a> </div> </article> </li> <li class="article-item"> <article class="article-card"> <a href="https://bbs.aizhi.co/html/284b799708.html" class="card-thumb-link" title="Artificial Intelligence Applications Institute"><img class="card-thumb" src="https://upload.wikimedia.org/wikipedia/commons/thumb/1/1a/Simthyr_3.1_on_openSUSE_11.png/960px-Simthyr_3.1_on_openSUSE_11.png" alt="Artificial Intelligence Applications Institute" loading="lazy"></a> <div class="card-body"> <h2><a href="https://bbs.aizhi.co/html/284b799708.html" title="Artificial Intelligence Applications Institute">Artificial Intelligence Applications Institute</a></h2> <p class="article-excerpt">The Artificial Intelligence Applications Institute (AIAI) at the School of Informatics at the University of Edinburgh is a non-profit technology transfer organisation that promoted research in the field of artificial intelligence. == History == The Artificial Intelligence Applications Institute (AIAI) was founded in 1983 at the University of Edinburgh as a specialist research and technology-transfer unit focusing on the practical uses of artificial intelligence (AI). The institute was established by Professor Jim Howe and colleagues from the Science and Engineering Research Council (SERC) Special Interest Group in AI in the Department of Artificial Intelligence, with a mission to apply AI techniques to solve real-world industrial and governmental problems. Under the directorship of Austin Tate, who served from 1985 to 2019, AIAI became one of the leading UK research centres devoted to AI programming systems, intelligent planning systems, decision support, and knowledge-based engineering. It collaborated with both academic partners and international organisations such as the European Space Agency and the UK Ministry of Defence. In 2001, AIAI joined the newly created Centre for Intelligent Systems and their Applications (CISA) within the University's School of Informatics. In December 2019, the institute was renamed the Artificial Intelligence and its Applications Institute to reflect a broader integration of fundamental and applied AI research. == Research programmes == AIAI’s research spans multiple areas of artificial intelligence, including: AI programming Systems - Edinburgh Prolog, Edinburgh Common Lisp, Logo; Knowledge representation and reasoning – development of ontologies, rule-based inference, and semantic modelling; Automated planning and scheduling – intelligent task management systems used in aerospace, manufacturing, and emergency response; Natural language processing and intelligent agents – interaction frameworks for human–computer collaboration; AI ethics and decision-making – research into responsible deployment and evaluation of autonomous systems. The institute also contributes to interdisciplinary fields such as computational creativity, explainable AI, and human–AI interaction. AIAI maintains close collaboration with the Bayes Centre and the Alan Turing Institute through joint research programmes and doctoral training initiatives. == Technology transfer and impact == From its inception, AIAI has combined academic research with technology-transfer activity, offering professional training, industrial consultancy, and bespoke software systems. It pioneered one of the earliest knowledge-based project-management systems, O-Plan, later evolved into the I-Plan framework used for autonomous planning and workflow management.</p> <a href="https://bbs.aizhi.co/html/284b799708.html" class="read-more" title="Artificial Intelligence Applications Institute">Read more →</a> </div> </article> </li> <li class="article-item"> <article class="article-card"> <a href="https://bbs.aizhi.co/html/291b799701.html" class="card-thumb-link" title="Aidoc"><img class="card-thumb" src="https://upload.wikimedia.org/wikipedia/commons/0/07/SPREEAI.jpg" alt="Aidoc" loading="lazy"></a> <div class="card-body"> <h2><a href="https://bbs.aizhi.co/html/291b799701.html" title="Aidoc">Aidoc</a></h2> <p class="article-excerpt">Aidoc Medical is an Israeli technology company that develops computer-aided simple triage and notification systems. Aidoc has obtained U.S. Food and Drug Administration and CE mark approval for its stroke, pulmonary embolism, cervical fracture, intracranial hemorrhage, intra-abdominal free gas, and incidental pulmonary embolism algorithms. Aidoc algorithms are in use in more than 900 hospitals and imaging centers, including Montefiore Nyack Hospital, LifeBridge Health, LucidHealth, Yale New Haven Hospital, Cedars-Sinai Medical Center, University of Rochester Medical Center, and Sheba Medical Center. == History == Aidoc was founded in 2016 by Elad Walach as the CEO, Michael Braginsky as the CTO and Guy Reiner as the VP. In April 2017, the company raised $7M, led by TLV Partners, and in April 2019, the company raised another $27M, led by Square Peg capital. There have been several additional rounds of funding as well, bringing Aidoc's total investment to $370M as of July 2025. In August 2018, Aidoc gained FDA clearance for its intracranial hemorrhage system, and in May 2019 it received clearance for the pulmonary embolism system. In January 2020, the system for detecting large-vessel occlusions (LVOs) in head CTA examinations obtained FDA clearance. In October 2024, it was reported that Aidoc is working with NVIDIA to develop a framework for deployment and integration of artificial intelligence tools in healthcare. The Blueprint for Resilient Integration and Deployment of Guided Excellence (BRIDGE) is a guideline to facilitate AI adoption in the healthcare industry. == Products and market == Aidoc has developed a suite of artificial intelligence products that flag both time-sensitive and time-consuming (for the radiologist) abnormalities across the body. The algorithms are developed with large quantities of data to provide diagnostic aid for a broad set of pathologies. The company offers an array of algorithms that span across the body, including for intracranial hemorrhage, spine fractures (C, T & L), free air in the abdomen, pulmonary embolism, and more. It developed "Always-on AI", a term coined by Elad Walach that refers to a type of artificial intelligence that is "Always-on—constantly running in the background and automatically analyzing medical imaging data, identifying urgent findings, and sparing radiologists from "drowning" in vast amounts of irrelevant data. Aidoc's solutions cover medical conditions prevalent in all settings (ED/inpatient/outpatient), including level 1 trauma centers, outpatient imaging centers, teleradiology groups and, are set up in over 200 medical centers worldwide. Notable customers include the University of Rochester Medical Center and Global Diagnostics Australia. Aidoc announced in 2024 that its new Clinical AI Reasoning Engine (CARE1) had been submitted for FDA approval. In September 2025 Aidoc received a "Breakthrough Device Designation" from the FDA for a new multi-triage solution that spans numerous acute findings in CT scans. Aidoc's CARE1 foundation model was the basis of the workflow on which the designation was made, enabling simultaneous coverage of multiple pathologies. This new designation allows parallel FDA review of multiple indications under a single submission. In April 2026, Aidoc raised million in a Series E funding round led by Growth Equity at Goldman Sachs Alternatives, with participation from General Catalyst and NVentures. The financing brought the company's total funding to over million. == Clinical Research == A clinical study on Aidoc’ accuracy of deep convolutional neural networks for the detection of pulmonary embolism (PE) on CT pulmonary angiograms (CTPAs) was performed by the University Hospital of Basel and presented at the European Congress of Radiology, showing that the Aidoc algorithm reached 93% sensitivity and 95% specificity. Clinical research has also been performed to test the diagnostic performance of Aidoc's deep learning-based triage system for the flagging of acute findings in abdominal computed tomography (CT) examinations. Overall, the algorithm achieved 93% sensitivity (91/98, 7 false negatives) and 97% specificity (93/96, 3 false-positive) in the detection of acute abdominal findings. Additional clinical research on Aidoc's Intracranial hemorrhage algorithm accuracy was presented at the European Congress of Radiology by Antwerp University Hospital, evaluating the use of its deep learning algorithm for the detection of intracranial hemorrhage on non-contrast enhanced CT of the brain. The University of Washington completed a study on the accuracy of Aidoc's intracranial hemorrhage algorithm.</p> <a href="https://bbs.aizhi.co/html/291b799701.html" class="read-more" title="Aidoc">Read more →</a> </div> </article> </li> <li class="article-item"> <article class="article-card"> <a href="https://bbs.aizhi.co/html/483e399513.html" class="card-thumb-link" title="Error level analysis"><img class="card-thumb" src="https://upload.wikimedia.org/wikipedia/commons/thumb/5/5a/Neural_Network_Intelligence_logo.svg/960px-Neural_Network_Intelligence_logo.svg.png" alt="Error level analysis" loading="lazy"></a> <div class="card-body"> <h2><a href="https://bbs.aizhi.co/html/483e399513.html" title="Error level analysis">Error level analysis</a></h2> <p class="article-excerpt">Error level analysis (ELA) is the analysis of compression artifacts in digital data with lossy compression such as JPEG. == Principles == When used, lossy compression is normally applied uniformly to a set of data, such as an image, resulting in a uniform level of compression artifacts. Alternatively, the data may consist of parts with different levels of compression artifacts. This difference may arise from the different parts having been repeatedly subjected to the same lossy compression a different number of times, or the different parts having been subjected to different kinds of lossy compression. A difference in the level of compression artifacts in different parts of the data may therefore indicate that the data has been edited. In the case of JPEG, even a composite with parts subjected to matching compressions will have a difference in the compression artifacts. In order to make the typically faint compression artifacts more readily visible, the data to be analyzed is subjected to an additional round of lossy compression, this time at a known, uniform level, and the result is subtracted from the original data under investigation. The resulting difference image is then inspected manually for any variation in the level of compression artifacts. In 2007, N. Krawetz denoted this method "error level analysis". Additionally, digital data formats such as JPEG sometimes include metadata describing the specific lossy compression used. If in such data the observed compression artifacts differ from those expected from the given metadata description, then the metadata may not describe the actual compressed data, and thus indicate that the data have been edited. == Limitations == By its nature, data without lossy compression, such as a PNG image, cannot be subjected to error level analysis. Consequently, since editing could have been performed on data without lossy compression with lossy compression applied uniformly to the edited, composite data, the presence of a uniform level of compression artifacts does not rule out editing of the data. Additionally, any non-uniform compression artifacts in a composite may be removed by subjecting the composite to repeated, uniform lossy compression. Also, if the image color space is reduced to 256 colors or less, for example, by conversion to GIF, then error level analysis will generate useless results. More significant, the actual interpretation of the level of compression artifacts in a given segment of the data is subjective, and the determination of whether editing has occurred is therefore not robust. == Controversy == In May 2013, Dr Neal Krawetz used error level analysis on the 2012 World Press Photo of the Year and concluded on his Hacker Factor blog that it was "a composite" with modifications that "fail to adhere to the acceptable journalism standards used by Reuters, Associated Press, Getty Images, National Press Photographer's Association, and other media outlets". The World Press Photo organizers responded by letting two independent experts analyze the image files of the winning photographer and subsequently confirmed the integrity of the files. One of the experts, Hany Farid, said about error level analysis that "It incorrectly labels altered images as original and incorrectly labels original images as altered with the same likelihood". Krawetz responded by clarifying that "It is up to the user to interpret the results. Any errors in identification rest solely on the viewer". In May 2015, the citizen journalism team Bellingcat wrote that error level analysis revealed that the Russian Ministry of Defense had edited satellite images related to the Malaysia Airlines Flight 17 disaster. In a reaction to this, image forensics expert Jens Kriese said about error level analysis: "The method is subjective and not based entirely on science", and that it is "a method used by hobbyists". On his Hacker Factor Blog, the inventor of error level analysis Neal Krawetz criticized both Bellingcat's use of error level analysis as "misinterpreting the results" but also on several points Jens Kriese's "ignorance" regarding error level analysis.</p> <a href="https://bbs.aizhi.co/html/483e399513.html" class="read-more" title="Error level analysis">Read more →</a> </div> </article> </li> <li class="article-item"> <article class="article-card"> <a href="https://bbs.aizhi.co/html/319e799673.html" class="card-thumb-link" title="AVT Statistical filtering algorithm"><img class="card-thumb" src="https://upload.wikimedia.org/wikipedia/commons/f/f1/Arg%C3%BCman_logo_large.png" alt="AVT Statistical filtering algorithm" loading="lazy"></a> <div class="card-body"> <h2><a href="https://bbs.aizhi.co/html/319e799673.html" title="AVT Statistical filtering algorithm">AVT Statistical filtering algorithm</a></h2> <p class="article-excerpt">AVT Statistical filtering algorithm is an approach to improving quality of raw data collected from various sources. It is most effective in cases when there is inband noise present. In those cases AVT is better at filtering data then, band-pass filter or any digital filtering based on variation of. Conventional filtering is useful when signal/data has different frequency than noise and signal/data is separated/filtered by frequency discrimination of noise. Frequency discrimination filtering is done using Low Pass, High Pass and Band Pass filtering which refers to relative frequency filtering criteria target for such configuration. Those filters are created using passive and active components and sometimes are implemented using software algorithms based on Fast Fourier transform (FFT). AVT filtering is implemented in software and its inner working is based on statistical analysis of raw data. When signal frequency/(useful data distribution frequency) coincides with noise frequency/(noisy data distribution frequency) we have inband noise. In this situations frequency discrimination filtering does not work since the noise and useful signal are indistinguishable and where AVT excels. To achieve filtering in such conditions there are several methods/algorithms available which are briefly described below. == Averaging algorithm == Collect n samples of data Calculate average value of collected data Present/record result as actual data == Median algorithm == Collect n samples of data Sort the data in ascending or descending order. Note that order does not matter Select the data that happen to be in n/2 position and present/record it as final result representing data sample == AVT algorithm == AVT algorithm stands for Antonyan Vardan Transform and its implementation explained below. Collect n samples of data Calculate the standard deviation and average value Drop any data that is greater or less than average ± one standard deviation Calculate average value of remaining data Present/record result as actual value representing data sample This algorithm is based on amplitude discrimination and can easily reject any noise that is not like actual signal, otherwise statistically different than 1 standard deviation of the signal. Note that this type of filtering can be used in situations where the actual environmental noise is not known in advance. Notice that it is preferable to use the median in above steps than average. Originally the AVT algorithm used average value to compare it with results of median on the data window. == Filtering algorithms comparison == Using a system that has signal value of 1 and has noise added at 0.1% and 1% levels will simplify quantification of algorithm performance. The R script is used to create pseudo random noise added to signal and analyze the results of filtering using several algorithms. Please refer to "Reduce Inband Noise with the AVT Algorithm" article for details. This graphs show that AVT algorithm provides best results compared with Median and Averaging algorithms while using data sample size of 32, 64 and 128 values. Note that this graph was created by analyzing random data array of 10000 values. Sample of this data is graphically represented below. From this graph it is apparent that AVT outperforms other filtering algorithms by providing 5% to 10% more accurate data when analyzing same datasets. Considering random nature of noise used in this numerical experiment that borderlines worst case situation where actual signal level is below ambient noise the precision improvements of processing data with AVT algorithm are significant. == AVT algorithm variations == === Cascaded AVT === In some situations better results can be obtained by cascading several stages of AVT filtering. This will produce singular constant value which can be used for equipment that has known stable characteristics like thermometers, thermistors and other slow acting sensors. === Reverse AVT === Collect n samples of data Calculate the standard deviation and average value Drop any data that is within one standard deviation ± average band Calculate average value of remaining data Present/record result as actual data This is useful for detecting minute signals that are close to background noise level. == Possible applications and uses == Use to filter data that is near or below noise level Used in planet detection to filter out raw data from the Kepler space telescope Filter out noise from sound sources where all other filtering methods (Low-pass filter, High-pass filter, Band-pass filter, Digital filter) fail. Pre-process scientific data for data analysis (Smoothness) before plotting see (Plot (graphics)) Used in SETI (Search for extraterrestrial intelligence) for detecting/distinguishing extraterrestrial signals from cosmic background Use AVT as image filtering algorithm to detect altered images. This image of Jupiter generated from this program, detecting alterations in original picture that was modified to be visually appealing by applying filters. Another version of this comparison is the Reverse AVT filter applied to the same original Jupiter Image, where we only see that altered portion as Noise that was eliminated by AVT algorithm. Use AVT as image filtering algorithm to estimate data density from images. Picture of Pillars of Creation Nebula shows data density in filtered images from Hubble and Webb. Note that image on the left has big patches of missing data marked with simpler color patterns.</p> <a href="https://bbs.aizhi.co/html/319e799673.html" class="read-more" title="AVT Statistical filtering algorithm">Read more →</a> </div> </article> </li> <li class="article-item"> <article class="article-card"> <a href="https://bbs.aizhi.co/html/212f799780.html" class="card-thumb-link" title="Voiceverse NFT plagiarism scandal"><img class="card-thumb" src="https://upload.wikimedia.org/wikipedia/commons/thumb/6/6a/Figure-ai-logo.svg/960px-Figure-ai-logo.svg.png" alt="Voiceverse NFT plagiarism scandal" loading="lazy"></a> <div class="card-body"> <h2><a href="https://bbs.aizhi.co/html/212f799780.html" title="Voiceverse NFT plagiarism scandal">Voiceverse NFT plagiarism scandal</a></h2> <p class="article-excerpt">In January 2022, 15—the pseudonymous Massachusetts Institute of Technology (MIT) artificial intelligence researcher and creator of the non-commercial generative artificial intelligence voice synthesis research project 15.ai—discovered that the blockchain-based technology company Voiceverse had plagiarized from their platform. Voiceverse marketed itself as a service that offered AI voice cloning technology that could be purchased and traded as non-fungible tokens (NFTs). Amid heightened controversy over NFTs in the gaming industry, voice actor Troy Baker (who has been described as one of the most famous voice actors in video games) announced his partnership with Voiceverse on January 14, 2022, triggering immediate backlash over concerns about the environmental impact of NFTs, potential for fraud, predatory monetization in video games, and the potential of AI displacing jobs for human voice actors. Later that same day, 15 revealed through server logs that Voiceverse had generated voice lines using 15's free text-to-speech platform, pitch-shifted the audio to make them unrecognizable, and falsely marketed the samples as their own technology before selling them as NFTs. Within an hour of being confronted with evidence, Voiceverse confessed and stated that their marketing team had used 15.ai without proper attribution while rushing to create a technology demo to coincide with Baker's partnership announcement, further exacerbating the already negative reception to the original announcement. In response, 15 replied "Go fuck yourself"; the interaction went viral and garnered a large amount of support for the developer. News publications universally characterized this incident as Voiceverse having "stolen" from 15.ai. The next day, Baker appeared on a podcast and stated that his motivation had been to help independent creators who were unable to afford professional voice actors. Following continued backlash and the plagiarism revelation, Baker ended his partnership with Voiceverse on January 31, 2022. Subsequently, the incident was documented in multiple AI ethics databases, criticisms of predatory monetization in video games, and retrospectives as one of the earliest instances of plagiarism and theft stemming from artificial intelligence during the AI boom. == Background == === Troy Baker === Troy Baker is a prominent voice actor in the video game industry best known for his performances as Joel Miller in The Last of Us franchise. Baker has been described as "ubiquitous" by Polygon, "one of the most high-profile and prolific voice actors in video games" by Eurogamer, and "arguably the most famous voice actor in the gaming industry" by GameGuru. His other prominent roles include voicing Agent John "Jonesy" Jones in Fortnite, Booker DeWitt in BioShock Infinite, and both Batman and Joker in multiple Batman video games. As of October 2025, Baker holds the record for the most acting nominations at the BAFTA Games Awards, with five between 2013 and 2021. === Voiceverse === Voiceverse is a blockchain-based startup founded by the Bored Ape Yacht Club that marketed itself as offering AI voice cloning technology in the form of NFTs. Prior to the announcement of their partnership with Baker, Voiceverse had partnered with LOVO, Inc., an AI voice platform that, according to LOVO, could generate human-like voices. Voiceverse stated that any user who purchases a voice NFT would have unlimited and perpetual access to the voice model, which could be used to create content such as audiobooks, YouTube videos, podcasts, e-learning materials, in-game voice chat, and Zoom calls. Voiceverse promised that buyers would "OWN [sic] all of the IP" of content they created using these voices. Voiceverse's roadmap included plans to release 8,888 initial voice NFTs, a feature to add emotions to existing voices, and the ability for users to mint their own voices as NFTs. Prior to Baker's partnership, Voiceverse had also partnered with voice actors Charlet Chung, who voices D.Va in Overwatch, and Andy Milonakis of The Andy Milonakis Show. === 15.ai === 15.ai is a free web application launched in 2020 that uses artificial intelligence to generate text-to-speech voices of fictional characters from popular media. Created by a pseudonymous artificial intelligence researcher known as 15, who began developing the technology as a freshman during their undergraduate research at MIT, it was an early example of an application of generative artificial intelligence during the initial stages of the AI boom. The platform showed that deep neural networks could generate emotionally expressive speech with only 15 seconds of speech; the name "15.ai" references the creator's statement that a voice can be convincingly cloned with just 15 seconds of audio, as opposed to the tens of hours of data previously required. 15.ai became an Internet phenomenon in early 2021 when content utilizing it went viral on social media and quickly gained widespread use among various Internet fandoms. 15 has emphasized that it remain free and non-commercial; it only requires users to give proper credit when using the service for content creation. === NFTs in the video game industry === By early 2022, NFTs had become highly controversial within the gaming industry. Critics raised concerns about their environmental impact due to the significant energy consumption of blockchain technology. In addition, the prevalence of scams, fraud, and potential money laundering associated with NFT sales, as well as fears that NFTs were a new form of predatory monetization following the increasing frequency of loot boxes, caused vocal pushback from the gaming community. Several major gaming companies had begun exploring NFT integration into their products, though fan backlash had already forced some projects to be cancelled. On December 16, 2021, the developers of S.T.A.L.K.E.R. 2: Heart of Chernobyl announced that they would be including NFTs in the game, but cancelled within an hour of the announcement due to immediate universal backlash. Simultaneously, the rise of AI voice technology raised concerns among voice actors about potential job displacement and the devaluation of their work amidst the voice acting industry's ongoing struggles for better compensation and working conditions. == Partnership announcement and backlash == On January 14, 2022, 1:02 a.m. EST, Baker announced on Twitter that he was partnering with Voiceverse "to explore ways where together we might bring new tools to new creators to make new things, and allow everyone a chance to own & invest in the IP's they create." The announcement concluded with the statement "You can hate. Or you can create." Baker's specific role with Voiceverse remained unclear at the time of the announcement. Along with Baker's announcement, Voiceverse promoted their supposed voice AI technology on Twitter by posting animated videos that featured a cat character created by NFT firm Chubbiverse. The videos concluded with text that read "The Voice Powered By Voiceverse"; Voiceverse stated on Twitter that the voices in the animations had been generated using their own AI voice synthesis technology and presented the videos as a technology demonstration of their voice NFT capabilities. The announcement provoked immediate and widespread backlash from the gaming community. Baker's tweet received thousands of replies and quote retweets (the vast majority of which were negative), far more than the number of likes; Michael McWhertor of Polygon described it as a "textbook example of being ratioed" and commented that reactions had been amplified by the final part of Baker's announcement. Michael Beckwith of Metro called Baker's approach "bizarrely aggressive". Later that day, Baker responded to the backlash by apologizing for his choice of words. He said he appreciated people's thoughts and acknowledged that the "hate/create part might have been a bit antagonistic," calling it a "bad attempt to bring levity". Despite the apology, Baker and his fellow voice actors did not distance themselves from Voiceverse at this point. At the same time, Voiceverse attempted to address the criticisms, stating that they were working to move to more environmentally friendly blockchain technology and that voice actors would receive royalties from NFT sales, with actors benefiting from any increase in NFT value. == Plagiarism revelation == On December 13, 2021, amidst the increasingly negative reactions toward NFTs among the general public, the creator of 15.ai (known pseudonymously as 15) announced that they had "no interest in incorporating NFTs into any aspect of [their] work." On January 14, 2022, 11:17 a.m. EST (10 hours after Baker's initial announcement), 15 commented on the Voiceverse venture, stating that it "sounds like a scam". Two hours later, at 1:20 p.m., 15 explicitly accused Voiceverse of "actively attempting to appropriate [15's] work for [Voiceverse's] own benefit." 15 provided evidence through </p> <a href="https://bbs.aizhi.co/html/212f799780.html" class="read-more" title="Voiceverse NFT plagiarism scandal">Read more →</a> </div> </article> </li> <li class="article-item"> <article class="article-card"> <a href="https://bbs.aizhi.co/html/280f799712.html" class="card-thumb-link" title="Artificial intuition"><img class="card-thumb" src="https://upload.wikimedia.org/wikipedia/commons/0/09/Street_in_Sendai.png" alt="Artificial intuition" loading="lazy"></a> <div class="card-body"> <h2><a href="https://bbs.aizhi.co/html/280f799712.html" title="Artificial intuition">Artificial intuition</a></h2> <p class="article-excerpt">Artificial intuition is a theoretical capacity of an artificial software to function similarly to human consciousness, specifically in the capacity of human consciousness known as intuition. == Comparison of human and the theoretically artificial == Intuition is the function of the mind, the experience of which, is described as knowledge based on "a hunch", resulting (as the word itself does) from "contemplation" or "insight". Psychologist Jean Piaget showed that intuitive functioning within the normally developing human child at the Intuitive Thought Substage of the preoperational stage occurred at from four to seven years of age. In Carl Jung's concept of synchronicity, the concept of "intuitive intelligence" is described as something like a capacity that transcends ordinary-level functioning to a point where information is understood with a greater depth than is available in more simple rationally-thinking entities. Artificial intuition is theoretically (or otherwise) a sophisticated function of an artifice that is able to interpret data with depth and locate hidden factors functioning in Gestalt psychology, and that intuition in the artificial mind would, in the context described here, be a bottom-up process upon a macroscopic scale identifying something like the archetypal (see τύπος). To create artificial intuition supposes the possibility of the re-creation of a higher functioning of the human mind, with capabilities such as what might be found in semantic memory and learning. The transferral of the functioning of a biological system to synthetic functioning is based upon modeling of functioning from knowledge of cognition and the brain, for instance as applications of models of artificial neural networks from the research done within the discipline of computational neuroscience. == Application software contributing to its development == The notion of a process of a data-interpretative synthesis has already been found in a computational-linguistic software application that has been created for use in an internal security context. The software integrates computed data based specifically on objectives incorporating a paradigm described as "religious intuitive" (hermeneutic), functional to a degree that represents advances upon the performance of generic lexical data mining.</p> <a href="https://bbs.aizhi.co/html/280f799712.html" class="read-more" title="Artificial intuition">Read more →</a> </div> </article> </li> <li class="article-item"> <article class="article-card"> <a href="https://bbs.aizhi.co/html/373a099626.html" class="card-thumb-link" title="Ciscogate"><img class="card-thumb" src="https://upload.wikimedia.org/wikipedia/commons/thumb/2/28/Beetle.svg/960px-Beetle.svg.png" alt="Ciscogate" loading="lazy"></a> <div class="card-body"> <h2><a href="https://bbs.aizhi.co/html/373a099626.html" title="Ciscogate">Ciscogate</a></h2> <p class="article-excerpt">Ciscogate, also known as the Black Hat Bug, is the name given to a legal incident that occurred at the Black Hat Briefings security conference in Las Vegas, Nevada, on July 27, 2005. On the morning of the first day of the conference, July 26, 2005, some attendees noticed that 30 pages of text had been physically ripped out of the extensive conference presentation booklet the night before at the request of Cisco Systems and the CD-ROM with presentation slides was not included. It was determined the pages covered a talk to be given by Michael Lynn, a security researcher with Atlanta-based IBM Internet Security Systems (ISS). Instead of the pages with the details, attendees found a photographed copy of a notice from Black Hat saying "Due to some last minute changes beyond Black Hat's control, and at the request of the presenter, the included materials aren't up to the standards Black Hat tries to meet. Black Hat will be the first to apologize. We hope the vendors involved will follow suit." According to Lynn's lawyer, his employer had approved of the talk leading up to the conference but changed their minds two days before the scheduled talk, forbidding him from presenting. Lynn's original presentation was to cover a vulnerability in Cisco routers. The presentation was one of four scheduled to follow Jeff Moss' keynote address on the first day of the conference, titled "Cisco IOS Security Architecture". After being told by his employer that he could not present on the topic, Lynn chose an alternate topic. Cisco and ISS had offered to give new joint presentation but this was turned down by Black Hat because the original speaking slot was given to Lynn, not Cisco. Lynn's presentation began by covering security issues in services that allow users to make Voice over IP telephone calls. Shortly after beginning the presentation Lynn changed back to his original topic and began disclosing some technical details of the vulnerability he found in Cisco routers stating that he would rather resign from his job at ISS than keep the details private. == Lawsuit == Shortly after Lynn concluded his talk he met Jennifer Granick, who would soon become his lawyer. During their initial meeting Lynn told Granick that he expected to be sued. Later in the evening Lynn had heard that Cisco and ISS had filed a lawsuit and requested a temporary restraining order against Black Hat but not himself. A public relations representative from Black Hat told Granick that the lawsuit was against both Black Hat and Lynn and that the companies had scheduled an Ex parte hearing in San Francisco the next morning to request the restraining order. That night, Andrew Valentine, an attorney for ISS and Cisco called Lynn who directed them to Granick. During the conversation Valentine explained the claims and accusations against Lynn, which included three things: 1) ISS claimed copyright over the presentation that Lynn gave, 2) Cisco claimed copyright over the decompiled machine code obtained from the router which was included in the presentation, and 3) Cisco claimed the presentation contained trade secrets. These complaints were outlined in a civil complaint at the U.S. Northern District of California and filed against both Lynn and Black Hat. According to Granick, she and Valentine were able agree to an injunction to settle the case without court proceedings. This deal was almost called off due to an inadvertent mistake by Black Hat in which they had restored Lynn's presentation on their web server. Black Hat, Granick, and the plaintiff's lawyers were able to resolve this problem and the deal stood. One condition of the settlement required Lynn to provide an image of all computer data he used in his research to be provided to a third party for forensic analysis before erasing his research and any Cisco data from his systems. The settlement also stipulated that Lynn was prohibited from talking about the vulnerability in the future. == FBI Investigation == Shortly after lawyers for Lynn and ISS / Cisco filed settlement papers, FBI agents from the Las Vegas office arrived at the conference to begin asking questions. According to Granick, they were there at the request of the Atlanta FBI office and Lynn was not of interest. Granick asserted the Fifth and Sixth amendment rights on behalf of her client, Lynn. Granick asserted his rights for the Atlanta office and asked if an arrest warrant had been issued for Lynn. Over the next 24 hours Granick was not able to ascertain the status of a warrant but ultimately determined no warrant was issued. When the FBI was asked about the case by a journalist, spokesman Paul Bresson declined to discuss the case saying "Our policy is to not make any comment on anything that is ongoing. That's not to confirm that something is, because I really don't know". Granick would only confirm to journalists that the "investigation has to do with the presentation". == Response == === Attendees === Attendees of Black Hat Briefings, as well as many that also attended DEF CON, were not happy with vendors threatening legal action over vulnerability disclosure. The term "Ciscogate" was coined quickly by an unknown person, but some attendees were quick to create shirts to commemorate the incident. === Cisco === Mojgan Khalili, a senior manager for corporate PR at Cisco, issued a statement to the press saying "It is important to note that the information Mr. Lynn presented was not a disclosure of a new vulnerability or a flaw with Cisco IOS software. Mr. Lynn's research explores possible ways to expand exploitations of existing security vulnerabilities impacting routers." === ISS === Kim Duffy, managing director of ISS Australia, was asked about ISS's response to the incident. Duffy responded that it was "business as usual" as the company handled the incident "strictly by the book". He gave a brief statement to ZDNet UK saying "ISS has published rules for disclosure and that is what we stick to. We didn't care to publish [the disclosure] because we were not ready. We had not completed the research to our satisfaction so it was not ready to be disclosed". ISS spokesperson Roger Fortier confirmed that Lynn was no longer employed with the company and that ISS was still working with Cisco on the matter. He gave a statement to the Washington Post saying "ISS and Cisco have been working on this in the background and didn't feel at this time that the material was ready for publication. The decision was made on Monday to pull the presentation because we wanted to make sure the research was fully baked."</p> <a href="https://bbs.aizhi.co/html/373a099626.html" class="read-more" title="Ciscogate">Read more →</a> </div> </article> </li> <li class="article-item"> <article class="article-card"> <a href="https://bbs.aizhi.co/html/234d799758.html" class="card-thumb-link" title="Token-based replay"><img class="card-thumb" src="https://upload.wikimedia.org/wikipedia/commons/thumb/6/69/Th%C3%A9%C3%A2tre_D%E2%80%99op%C3%A9ra_Spatial.png/960px-Th%C3%A9%C3%A2tre_D%E2%80%99op%C3%A9ra_Spatial.png" alt="Token-based replay" loading="lazy"></a> <div class="card-body"> <h2><a href="https://bbs.aizhi.co/html/234d799758.html" title="Token-based replay">Token-based replay</a></h2> <p class="article-excerpt">Token-based replay technique is a conformance checking algorithm that checks how well a process conforms with its model by replaying each trace on the model (in Petri net notation ). Using the four counters produced tokens, consumed tokens, missing tokens, and remaining tokens, it records the situations where a transition is forced to fire and the remaining tokens after the replay ends. Based on the count at each counter, we can compute the fitness value between the trace and the model. == The algorithm == Source: The token-replay technique uses four counters to keep track of a trace during the replaying: p: Produced tokens c: Consumed tokens m: Missing tokens (consumed while not there) r: Remaining tokens (produced but not consumed) Invariants: At any time: p + m ≥ c ≥ m {\displaystyle p+m\geq c\geq m} At the end: r = p + m − c {\displaystyle r=p+m-c} At the beginning, a token is produced for the source place (p = 1) and at the end, a token is consumed from the sink place (c' = c + 1). When the replay ends, the fitness value can be computed as follows: 1 2 ( 1 − m c ) + 1 2 ( 1 − r p ) {\displaystyle {\frac {1}{2}}(1-{\frac {m}{c}})+{\frac {1}{2}}(1-{\frac {r}{p}})} == Example == Suppose there is a process model in Petri net notation as follows: === Example 1: Replay the trace (a, b, c, d) on the model M === Step 1: A token is initiated. There is one produced token ( p = 1 {\displaystyle p=1} ). Step 2: The activity a {\displaystyle \mathbf {a} } consumes 1 token to be fired and produces 2 tokens ( p = 1 + 2 = 3 {\displaystyle p=1+2=3} and c = 1 {\displaystyle c=1} ). Step 3: The activity b {\displaystyle \mathbf {b} } consumes 1 token and produces 1 token ( p = 3 + 1 = 4 {\displaystyle p=3+1=4} and c = 1 + 1 = 2 {\displaystyle c=1+1=2} ). Step 4: The activity c {\displaystyle \mathbf {c} } consumes 1 token and produces 1 token ( p = 4 + 1 = 5 {\displaystyle p=4+1=5} and c = 2 + 1 = 3 {\displaystyle c=2+1=3} ). Step 5: The activity d {\displaystyle \mathbf {d} } consumes 2 tokens and produces 1 token ( p = 5 + 1 = 6 {\displaystyle p=5+1=6} , c = 3 + 2 = 5 {\displaystyle c=3+2=5} ). Step 6: The token at the end place is consumed ( c = 5 + 1 = 6 {\displaystyle c=5+1=6} ). The trace is complete. The fitness of the trace ( a , b , c , d {\displaystyle \mathbf {a,b,c,d} } ) on the model M {\displaystyle \mathbf {M} } is: 1 2 ( 1 − m c ) + 1 2 ( 1 − r p ) = 1 2 ( 1 − 0 6 ) + 1 2 ( 1 − 0 6 ) = 1 {\displaystyle {\frac {1}{2}}(1-{\frac {m}{c}})+{\frac {1}{2}}(1-{\frac {r}{p}})={\frac {1}{2}}(1-{\frac {0}{6}})+{\frac {1}{2}}(1-{\frac {0}{6}})=1} === Example 2: Replay the trace (a, b, d) on the model M === Step 1: A token is initiated. There is one produced token ( p = 1 {\displaystyle p=1} ). Step 2: The activity a {\displaystyle \mathbf {a} } consumes 1 token to be fired and produces 2 tokens ( p = 1 + 2 = 3 {\displaystyle p=1+2=3} and c = 1 {\displaystyle c=1} ). Step 3: The activity b {\displaystyle \mathbf {b} } consumes 1 token and produces 1 token ( p = 3 + 1 = 4 {\displaystyle p=3+1=4} and c = 1 + 1 = 2 {\displaystyle c=1+1=2} ). Step 4: The activity d {\displaystyle \mathbf {d} } needs to be fired but there are not enough tokens. One artificial token was produced and the missing token counter is increased by one ( m = 1 {\displaystyle m=1} ). The artificial token and the token at place [ b , d ] {\displaystyle [\mathbf {b,d} ]} are consumed ( c = 2 + 2 = 4 {\displaystyle c=2+2=4} ) and one token is produced at place end ( p = 4 + 1 = 5 {\displaystyle p=4+1=5} ). Step 5: The token in the end place is consumed ( c = 4 + 1 = 5 {\displaystyle c=4+1=5} ). The trace is complete. There is one remaining token at place [ a , c ] {\displaystyle [\mathbf {a,c} ]} ( r = 1 {\displaystyle r=1} ). The fitness of the trace ( a , b , d {\displaystyle \mathbf {a,b,d} } ) on the model M {\displaystyle \mathbf {M} } is: 1 2 ( 1 − m c ) + 1 2 ( 1 − r p ) = 1 2 ( 1 − 1 5 ) + 1 2 ( 1 − 1 5 ) = 0.8 {\displaystyle {\frac {1}{2}}(1-{\frac {m}{c}})+{\frac {1}{2}}(1-{\frac {r}{p}})={\frac {1}{2}}(1-{\frac {1}{5}})+{\frac {1}{2}}(1-{\frac {1}{5}})=0.8}</p> <a href="https://bbs.aizhi.co/html/234d799758.html" class="read-more" title="Token-based replay">Read more →</a> </div> </article> </li> <li class="article-item"> <article class="article-card"> <a href="https://bbs.aizhi.co/html/428b799564.html" class="card-thumb-link" title="Data janitor"><img class="card-thumb" src="https://upload.wikimedia.org/wikipedia/commons/a/af/User_profile_personal_details.png" alt="Data janitor" loading="lazy"></a> <div class="card-body"> <h2><a href="https://bbs.aizhi.co/html/428b799564.html" title="Data janitor">Data janitor</a></h2> <p class="article-excerpt">A data janitor is a person who works to take big data and condense it into useful amounts of information. Also known as a "data wrangler", a data janitor sifts through data for companies in the information technology industry. A multitude of start-ups rely on large amounts of data, so a data janitor works to help these businesses with this basic, but difficult process of interpreting data. While it is a commonly held belief that data janitor work is fully automated, many data scientists are employed primarily as data janitors. 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