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Static program analysis

In computer science, static program analysis (also known as static analysis or static simulation) is the analysis of computer programs performed without executing them, in contrast with dynamic program analysis, which is performed on programs during their execution in the integrated environment. The term is usually applied to analysis performed by an automated tool, with human analysis typically being called "program understanding", program comprehension, or code review. In the last of these, software inspection and software walkthroughs are also used. In most cases the analysis is performed on some version of a program's source code, and, in other cases, on some form of its object code. Two leading approaches to resource certification have been Static Analysis (SA) and Implicit Computational Complexity (ICC). SA is algorithmic in nature: it focuses on a broad programming language of choice, and seeks to determine by syntactic means whether given programs in that language are feasible. In contrast, ICC attempts to create from the outset specialized programming languages or methods that delineate a complexity class. Thus, SA's focus is on compile time, making no demand on the programmer; whereas ICC is a language-design discipline." The discipline of static analysis should not be confused with linting, which is the process of checking for coding style mistakes. == Rationale == The sophistication of the analysis performed by tools varies from those that only consider the behaviour of individual statements and declarations, to those that include the complete source code of a program in their analysis. The uses of the information obtained from the analysis vary from highlighting possible coding errors (e.g., the lint tool) to formal methods that mathematically prove properties about a given program (e.g., its behaviour matches that of its specification). Software metrics and reverse engineering can be described as forms of static analysis. Deriving software metrics and static analysis are increasingly deployed together, especially in creation of embedded systems, by defining so-called software quality objectives. A growing commercial use of static analysis is in the verification of properties of software used in safety-critical computer systems and locating potentially vulnerable code. For example, the following industries have identified the use of static code analysis as a means of improving the quality of increasingly sophisticated and complex software: Medical software: The US Food and Drug Administration (FDA) has identified the use of static analysis for medical devices. Nuclear software: In the UK the Office for Nuclear Regulation (ONR) recommends the use of static analysis on reactor protection systems. Aviation software (in combination with dynamic analysis). Automotive & Machines (functional safety features form an integral part of each automotive product development phase, ISO 26262, section 8). A study in 2012 by VDC Research reported that 28.7% of the embedded software engineers surveyed use static analysis tools and 39.7% expect to use them within 2 years. A study from 2010 found that 60% of the interviewed developers in European research projects made at least use of their basic IDE built-in static analyzers. However, only about 10% employed an additional other (and perhaps more advanced) analysis tool. In the application security industry the name static application security testing (SAST) is also used. SAST is an important part of Security Development Lifecycles (SDLs) such as the SDL defined by Microsoft and a common practice in software companies. == Tool types == The OMG (Object Management Group) published a study regarding the types of software analysis required for software quality measurement and assessment. This document on "How to Deliver Resilient, Secure, Efficient, and Easily Changed IT Systems in Line with CISQ Recommendations" describes three levels of software analysis. Unit Level Analysis that takes place within a specific program or subroutine, without connecting to the context of that program. Technology Level Analysis that takes into account interactions between unit programs to get a more holistic and semantic view of the overall program in order to find issues and avoid obvious false positives. System Level Analysis that takes into account the interactions between unit programs, but without being limited to one specific technology or programming language. A further level of software analysis can be defined. Mission/Business Level Analysis that takes into account the business/mission layer terms, rules and processes that are implemented within the software system for its operation as part of enterprise or program/mission layer activities. These elements are implemented without being limited to one specific technology or programming language and in many cases are distributed across multiple languages, but are statically extracted and analyzed for system understanding for mission assurance. == Formal methods == Formal methods is the term applied to the analysis of software (and computer hardware) whose results are obtained purely through the use of rigorous mathematical methods. The mathematical techniques used include denotational semantics, axiomatic semantics, operational semantics, and abstract interpretation. By a straightforward reduction to the halting problem, it is possible to prove that (for any Turing complete language), finding all possible run-time errors in an arbitrary program (or more generally any kind of violation of a specification on the final result of a program) is undecidable: there is no mechanical method that can always answer truthfully whether an arbitrary program may or may not exhibit runtime errors. This result dates from the works of Church, Gödel and Turing in the 1930s (see: Halting problem and Rice's theorem). As with many undecidable questions, one can still attempt to give useful approximate solutions. Some of the implementation techniques of formal static analysis include: Abstract interpretation, to model the effect that every statement has on the state of an abstract machine (i.e., it 'executes' the software based on the mathematical properties of each statement and declaration). This abstract machine over-approximates the behaviours of the system: the abstract system is thus made simpler to analyze, at the expense of incompleteness (not every property true of the original system is true of the abstract system). If properly done, though, abstract interpretation is sound (every property true of the abstract system can be mapped to a true property of the original system). Data-flow analysis, a lattice-based technique for gathering information about the possible set of values; Hoare logic, a formal system with a set of logical rules for reasoning rigorously about the correctness of computer programs. There is tool support for some programming languages (e.g., the SPARK programming language (a subset of Ada) and the Java Modeling Language—JML—using ESC/Java and ESC/Java2, Frama-C WP (weakest precondition) plugin for the C language extended with ACSL (ANSI/ISO C Specification Language) ). Model checking, considers systems that have finite state or may be reduced to finite state by abstraction; Symbolic execution, as used to derive mathematical expressions representing the value of mutated variables at particular points in the code. Nullable reference analysis == Data-driven static analysis == Data-driven static analysis leverages extensive codebases to infer coding rules and improve the accuracy of the analysis. For instance, one can use all Java open-source packages available on GitHub to learn good analysis strategies. The rule inference can use machine learning techniques. It is also possible to learn from a large amount of past fixes and warnings. == Remediation == Static analyzers produce warnings. For certain types of warnings, it is possible to design and implement automated remediation techniques. For example, Logozzo and Ball have proposed automated remediations for C# cccheck.
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Aapo Hyvärinen

Aapo Johannes Hyvärinen (born 1970 in Helsinki) is a Finnish professor of computer science at the University of Helsinki and known for his research in independent component analysis. == Education and career == Hyvärinen was born in Helsinki and studied mathematics at the University of Helsinki and received his Doctor of Technology in information science in 1997 at the Helsinki University of Technology under the supervision of Erkki Oja. His doctoral thesis, titled "Independent component analysis: A neural network approach", introduced the FastICA algorithm. Since then, Hyvärinen has conducted research especially in relation to the independent component analysis, as well as score matching (also known as Hyvärinen scoring rule). In November 2007, he was appointed as a professor at the University of Helsinki. Hyvärinen has been a member of the Finnish Academy of Sciences since 2016. From August 2016 to March 2019, he held a professorship in machine learning at the Gatsby Computational Neuroscience Unit of the University College London.
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AI Background Removers Reviews: What Actually Works in 2026

Trying to pick the best AI background remover? An AI background remover is software that uses machine learning to help you get more done — it scales effortlessly from a single task to thousands. The best picks balance beginner-friendly simplicity with the depth power users need, and they ship updates often. Whether you are a beginner or a pro, the right AI background remover slots into your workflow and pays for itself fast. This guide breaks down the top picks, their pros and cons, and who each one is best for.
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Krzysztof Wołk

Krzysztof Wołk (born 16 August 1986) is a Polish IT researcher who specializes in artificial intelligence, machine learning, mobile applications, linguistic engineering, multimedia, NLP and graphic applications. His research works have been cited in more than 70 international research journals, books and research papers. He is member of scientific committee at the Health and Social Care Information Systems and Technologies (HCist), an international conference which brings in new ideas, new technologies, academic scientists, healthcare IT professionals, managers and solution providers from all over the world. His research in statistical machine learning has been recognized as one of the most cited researches in the world. He is the member of Scientific Committee-Reviewers at Research Conference in Technical Disciplines (RCITD), based in Slovakia, which brings together the academic scientists and researchers from all around the world. == Biography == He obtained the doctorate degree in 2016 from the Polish-Japanese Academy of Information and Technology in Warsaw, Poland. He is currently working as researcher and assistant professor at the Polish-Japanese Computer Science Academy (PJATK) in Warsaw, Poland. == Achievements == He has published three books: Biblia Windows Server 2012, Administrator's Guide, Mac OS X Server 10.8, and MAC OS X Server 10.6 and 10.7 Practical Guide has been cited by many researchers in the scholarly books, research journals and articles. His research work on the Polish-English statistical machine translation has been featured in the book New Research in Multimedia and Internet System. Similarly, his works regarding the machine translation system have been featured in the books New Perspective in Information System and Technologies Volume 1, Multimedia and Network Information System, and Recent Advances in Information Systems and Technologies, Volume 1.
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SUPS

In computational neuroscience, SUPS (for Synaptic Updates Per Second) or formerly CUPS (Connections Updates Per Second) is a measure of a neuronal network performance, useful in fields of neuroscience, cognitive science, artificial intelligence, and computer science. == Computing == For a processor or computer designed to simulate a neural network SUPS is measured as the product of simulated neurons N {\displaystyle N} and average connectivity c {\displaystyle c} (synapses) per neuron per second: S U P S = c × N {\displaystyle SUPS=c\times N} Depending on the type of simulation it is usually equal to the total number of synapses simulated. In an "asynchronous" dynamic simulation if a neuron spikes at υ {\displaystyle \upsilon } Hz, the average rate of synaptic updates provoked by the activity of that neuron is υ c N {\displaystyle \upsilon cN} . In a synchronous simulation with step Δ t {\displaystyle \Delta t} the number of synaptic updates per second would be c N Δ t {\displaystyle {\frac {cN}{\Delta t}}} . As Δ t {\displaystyle \Delta t} has to be chosen much smaller than the average interval between two successive afferent spikes, which implies Δ t < 1 υ N {\displaystyle \Delta t<{\frac {1}{\upsilon N}}} , giving an average of synaptic updates equal to υ c N 2 {\displaystyle \upsilon cN^{2}} . Therefore, spike-driven synaptic dynamics leads to a linear scaling of computational complexity O(N) per neuron, compared with the O(N2) in the "synchronous" case. == Records == Developed in the 1980s Adaptive Solutions' CNAPS-1064 Digital Parallel Processor chip is a full neural network (NNW). It was designed as a coprocessor to a host and has 64 sub-processors arranged in a 1D array and operating in a SIMD mode. Each sub-processor can emulate one or more neurons and multiple chips can be grouped together. At 25 MHz it is capable of 1.28 GMAC. After the presentation of the RN-100 (12 MHz) single neuron chip at Seattle 1991 Ricoh developed the multi-neuron chip RN-200. It had 16 neurons and 16 synapses per neuron. The chip has on-chip learning ability using a proprietary backdrop algorithm. It came in a 257-pin PGA encapsulation and drew 3.0 W at a maximum. It was capable of 3 GCPS (1 GCPS at 32 MHz). In 1991–97, Siemens developed the MA-16 chip, SYNAPSE-1 and SYNAPSE-3 Neurocomputer. The MA-16 was a fast matrix-matrix multiplier that can be combined to form systolic arrays. It could process 4 patterns of 16 elements each (16-bit), with 16 neuron values (16-bit) at a rate of 800 MMAC or 400 MCPS at 50 MHz. The SYNAPSE3-PC PCI card contained 2 MA-16 with a peak performance of 2560 MOPS (1.28 GMAC); 7160 MOPS (3.58 GMAC) when using three boards. In 2013, the K computer was used to simulate a neural network of 1.73 billion neurons with a total of 10.4 trillion synapses (1% of the human brain). The simulation ran for 40 minutes to simulate 1 s of brain activity at a normal activity level (4.4 on average). The simulation required 1 Petabyte of storage.
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Regina Barzilay

Regina Barzilay (Hebrew: רגינה ברזילי; born 1970) is an Israeli-American computer scientist. She is a professor at the Massachusetts Institute of Technology and a faculty lead for artificial intelligence at the MIT Jameel Clinic. Her research interests are in natural language processing and applications of deep learning to chemistry and oncology. == Early life and education == Barzilay was born in Chișinău, Moldova and emigrated to Israel with her parents at the age of 20. She received bachelor's and master's degrees from Ben-Gurion University of the Negev in 1993 and 1998, respectively. She obtained a PhD in computer science from Columbia University in 2003 for research supervised by Kathleen McKeown. == Career and research == After her PhD, she spent a year as a postdoctoral researcher at Cornell University. She was appointed as Delta Electronics Professor of Electrical Engineering and Computer Science at MIT in 2016. She was diagnosed with breast cancer in 2014, which prompted her to conduct research in oncology. Barzilay won the MacArthur Fellowship in 2017. For her doctoral dissertation at Columbia University, she led the development of Newsblaster, which recognized stories from different news sources as being about the same basic subject, and then paraphrased elements from the stories to create a summary. In computational linguistics, Barzilay created algorithms that learned annotations from common languages (i.e. English) to analyze less understood languages. Prompted by her experience with breast cancer, Barzilay is applying machine learning to oncology. She is collaborating with physicians and students to devise deep learning models that utilize images, text, and structured data to identify trends that affect early diagnosis, treatment, and disease prevention. Frontline Documentary Following her battle with breast cancer in 2014, and her researching into applying artificial intelligence to improve early detection methods, she collaborated with Dr. Connie Lehman at Massachusetts General Hospital. While there Barzilay developed an AI-based system capable of predicting the likelihood of breast cancer up to five years in advance. The system leverages deep learning techniques to analyze mammograms and diagnostic notes, surpassing traditional pattern recognition by human radiologists. This breakthrough, while still in development, has the potential to significantly enhance early diagnosis and treatment outcomes. [1] Barzilay's work in this area was featured in the FRONTLINE documentary In the Age of AI, which explores the broader impact of artificial intelligence on society. === MIT Jameel Clinic === In 2018, Barzilay was appointed faculty lead for AI at the new MIT Jameel Clinic, a research center in the field of AI health sciences, including disease detection, drug discovery, and the development of medical devices. In 2020, she was part of the team—with fellow MIT Jameel Clinic faculty lead Professor James J. Collins—that announced the discovery through deep learning of halicin, the first new antibiotic compound for 30 years, which kills over 35 powerful bacteria, including antimicrobial-resistant tuberculosis, the superbug C. difficile, and two of the World Health Organization's top-three most deadly bacteria. In 2020, Collins, Barzilay and the MIT Jameel Clinic were also awarded funding through The Audacious Project to expand on the discovery of halicin in using AI to respond to the antibiotic resistance crisis through the development of new classes of antibiotics. == Awards and recognition == In 2017, Barzilay won the MacArthur Fellowship, known as the "Genius Grant", for "developing machine learning methods that enable computers to process and analyze vast amounts of human language data." She is also a recipient of various awards including the NSF Career Award, the MIT Technology Review TR-35 Award, Microsoft Faculty Fellowship and several Best Paper Awards at NAACL and ACL. Her teaching has also been recognized by MIT as she won the Jamieson Teaching Award in 2016. She was nominated an AAAI Fellow in 2018 by the Association for the Advancement of Artificial Intelligence. In 2020, she became the first recipient of the $1 million AAAI Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity. In 2023, she was elected to the National Academy of Medicine and the National Academy of Engineering.
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Best AI Virtual Assistants in 2026

Shopping for the best AI virtual assistant? An AI virtual assistant is software that uses machine learning to help you get more done — it keeps getting smarter as the underlying models improve. Pricing, accuracy, and the size of the model behind the tool are the three factors that most affect daily usefulness. Whether you are a beginner or a pro, the right AI virtual assistant slots into your workflow and pays for itself fast. We tested the leading options and ranked them by quality, value, and ease of use.
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Intelligent character recognition

Intelligent character recognition (ICR) is a method of extracting handwritten text from images. It is a more sophisticated type of OCR technology that recognizes different handwriting styles and fonts to intelligently interpret data from physical documents. ICR is used to organize paper-based unstructured data by scanning documents, extracting information, and adapting extracted data for database storage. ICR algorithms collaborate with OCR to automate data entry from forms by removing the need for keystrokes. It has a high degree of accuracy and is a dependable method for processing various handwritten media quickly. == Capabilities == Most ICR software has a self-learning neural network-based algorithms, which automatically update the recognition database for new handwriting patterns. It extends the usefulness of scanning devices for the purpose of document processing, from printed character recognition (a function of OCR) to hand-written matter recognition. Because this process is involved in recognizing hand writing, accuracy levels may, in some circumstances, not be very good but can achieve 97%+ accuracy rates in reading handwriting in structured forms. Often to achieve these high recognition rates several read engines are used within the software and each is given elective voting rights to determine the true reading of characters. In numeric fields, engines which are designed to read numbers take preference, while in alpha fields, engines designed to read hand written letters have higher elective rights. When used in conjunction with a bespoke interface hub, hand-written data can be automatically populated into a back office system avoiding laborious manual keying and can be more accurate than traditional human data entry. === Automated forms processing === An important development of ICR was the invention of automated forms processing in 1993 by Joseph Corcoran who was awarded a patent on the invention. This involved a three-stage process of capturing the image of the form to be processed by ICR and preparing it to enable the ICR engine to give best results, then capturing the information using the ICR engine and finally processing the results to automatically validate the output from the ICR engine. This application of ICR increased the usefulness of the technology and made it applicable for use with real world forms in normal business applications. Modern software applications use ICR as a technology of recognizing text in forms filled in by hand (hand-printed). == Differences between ICR and OCR == === OCR === Optical character recognition (OCR) is commonly considered to apply to any recognition technique that reads machine printed text. An example of a traditional OCR use case would be to translate the characters from an image of a printed document, such as a book page, newspaper clipping, or legal contract, into a separate file that could be searched and updated with a word processor or document viewer. It's also quite helpful for automating the processing of forms. Information can be swiftly extracted from form fields and entered into another application, like a spreadsheet or database, by zonally applying the OCR engine to those fields. Yet, data is typically manually input rather than typed into form fields. Character identification becomes even more challenging while reading handwritten material. The diversity of more than 700,000 printed font variants is tiny compared to the near unlimited variations in hand-printed characters. The recognition program must take into account not just stylistic differences but also the kind of writing implement used, the standard of the paper, errors, hand stability, and smudges or running ink. === ICR === Intelligent character recognition (ICR) makes use of continuously improving algorithms to collect more information about the variances in hand-printed characters and more precisely identify them. ICR, which was created in the early 1990s to aid in the automation of forms processing, enables the conversion of manually entered data into text that is simple to read, search for, and change. When used to read characters that are obviously divided into distinct areas or zones, such as fixed fields seen on many structured forms, it works best. Both OCR and ICR can be configured to read a variety of languages; however, limiting the expected character set to a smaller number of languages will produce better recognition outcomes. ICR cannot read cursive handwriting since it must still be able to assess each character individually. While writing in cursive, it might be difficult to tell where one character ends and another one begins, and there are more differences across samples than when hand-printing text. A more recent method called intelligent word recognition (IWR) focuses on reading a word in context rather than recognizing individual characters. == Intelligent word recognition == Intelligent word recognition (IWR) can recognize and extract not only printed-handwritten information, cursive handwriting as well. ICR recognizes on the character-level, whereas IWR works with full words or phrases. Capable of capturing unstructured information from every day pages, IWR is said to be more evolved than hand print ICR. Not meant to replace conventional ICR and OCR systems, IWR is optimized for processing real-world documents that contain mostly free-form, hard-to-recognize data fields that are inherently unsuitable for ICR. This means that the highest and best use of IWR is to eliminate a high percentage of the manual entry of handwritten data and run-on hand print fields on documents that otherwise could be keyed only by humans.
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Interlacing (bitmaps)

In computing, interlacing (also known as interleaving) is a method of encoding a bitmap image such that a person who has partially received it sees a degraded copy of the entire image. When communicating over a slow communications link, this is often preferable to seeing a perfectly clear copy of one part of the image, as it helps the viewer decide more quickly whether to abort or continue the transmission. Interlacing is supported by the following formats, where it is optional: GIF interlacing stores the lines in the order 0 , 8 , 16 , … , ( 8 n ) , 4 , 12 , … , ( 8 n + 4 ) , 2 , 6 , 10 , 14 , … , ( 4 n + 2 ) , 1 , 3 , 5 , 7 , 9 , … , ( 2 n + 1 ) . {\displaystyle 0,8,16,\dots ,(8n),\ 4,12,\dots ,(8n+4),\ 2,6,10,14,\dots ,(4n+2),\ 1,3,5,7,9,\dots ,(2n+1).} PNG uses the Adam7 algorithm, which interlaces in both the vertical and horizontal direction. TGA uses two optional interlacing algorithms: Two-way: 0 , 2 , 4 , … , ( 2 n ) , 1 , 3 , … , ( 2 n + 1 ) , {\displaystyle 0,2,4,\dots ,(2n),\ 1,3,\dots ,(2n+1),} And four-way: 0 , 4 , 8 , … , ( 4 n ) , 1 , 5 , … , ( 4 n + 1 ) , 2 , 6 , … , ( 4 n + 2 ) , 3 , 7 , … , ( 4 n + 3 ) . {\displaystyle 0,4,8,\dots ,(4n),\ 1,5,\dots ,(4n+1),\ 2,6,\dots ,\ (4n+2),3,7,\dots ,(4n+3).} JPEG, JPEG 2000, and JPEG XR (actually using a frequency decomposition hierarchy rather than interlacing of pixel values) PGF (also using a frequency decomposition) Interlacing is a form of incremental decoding, because the image can be loaded incrementally. Another form of incremental decoding is progressive scan. In progressive scan the loaded image is decoded line for line, so instead of becoming incrementally clearer it becomes incrementally larger. The main difference between the interlace concept in bitmaps and in video is that even progressive bitmaps can be loaded over multiple frames. For example: Interlaced GIF is a GIF image that seems to arrive on your display like an image coming through a slowly opening Venetian blind. A fuzzy outline of an image is gradually replaced by seven successive waves of bit streams that fill in the missing lines until the image arrives at its full resolution. Interlaced graphics were once widely used in web design and before that in the distribution of graphics files over bulletin board systems and other low-speed communications methods. The practice is much less common today, as common broadband internet connections allow most images to be downloaded to the user's screen nearly instantaneously, and interlacing is usually an inefficient method of encoding images. Interlacing has been criticized because it may not be clear to viewers when the image has finished rendering, unlike non-interlaced rendering, where progress is apparent (remaining data appears as blank). Also, the benefits of interlacing to those on low-speed connections may be outweighed by having to download a larger file, as interlaced images typically do not compress as well.
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AI Humanizers Reviews: What Actually Works in 2026

Curious about the best AI humanizer? An AI humanizer is software that uses machine learning to help you get more done — it combines speed, accuracy, and an interface that just works. Hands-on testing shows real-world results vary, so a short free trial is the smartest way to decide. Whether you are a beginner or a pro, the right AI humanizer slots into your workflow and pays for itself fast. Read on for hands-on impressions, pricing tiers, and the standout features that matter.
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How to Choose an AI Text-to-video Tool

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

Glottochronology (from Attic Greek γλῶττα 'tongue, language' and χρόνος 'time') is the part of lexicostatistics which involves comparative linguistics and deals with the chronological relationship between languages. The idea was developed by Morris Swadesh in the 1950s in his article on Salish internal relationships. He developed the idea under two assumptions: there indeed exists a relatively stable basic vocabulary (referred to as Swadesh lists) in all languages of the world; and, any replacements happen in a way analogous to radioactive decay in a constant percentage per time elapsed. Using mathematics and statistics, Swadesh developed an equation to determine when languages separated and give an approximate time of when the separation occurred. His methods aimed to aid linguistic anthropologists by giving them a definitive way to determine a separation date between two languages. The formula provides an approximate number of centuries since two languages were supposed to have separated from a singular common ancestor. His methods also purported to provide information on when ancient languages may have existed. Despite multiple studies and literature containing the information of glottochronology, it is not widely used today and is surrounded with controversy. Glottochronology tracks language separation from thousands of years ago but many linguists are skeptical of the concept because it is more of a 'probability' rather than a 'certainty.' On the other hand, some linguists may say that glottochronology is gaining traction because of its relatedness to archaeological dates. Glottochronology is not as accurate as archaeological data, but some linguists still believe that it can provide a solid estimate. Over time many different extensions of the Swadesh method evolved; however, Swadesh's original method is so well known that 'glottochronology' is usually associated with him. == Methodology == The original method of glottochronology presumed that the core vocabulary of a language is replaced at a constant (or constant average) rate across all languages and cultures and so can be used to measure the passage of time. The process makes use of a list of lexical terms and morphemes which are similar to multiple languages. Lists were compiled by Morris Swadesh and assumed to be resistant against borrowing (originally designed in 1952 as a list of 200 items, but the refined 100-word list in Swadesh (1955) is much more common among modern day linguists). The core vocabulary was designed to encompass concepts common to every human language such as personal pronouns, body parts, heavenly bodies and living beings, verbs of basic actions, numerals, basic adjectives, kin terms, and natural occurrences and events. Through a basic word list, one eliminates concepts that are specific to a particular culture or time period. It has been found through differentiating word lists that the ideal is really impossible and that the meaning set may need to be tailored to the languages being compared. Word lists are not homogenous throughout studies and they are often changed and designed to suit both languages being studied. Linguists find that it is difficult to find a word list where all words used are culturally unbiased. Many alternative word lists have been compiled by other linguists and often use fewer meaning slots. The percentage of cognates (words with a common origin) in the word lists is then measured. The larger the percentage of cognates, the more recently the two languages being compared are presumed to have separated. === Glottochronologic constant === Determining word lists rely on morpheme decay or change in vocabulary. Morpheme decay must stay at a constant rate for glottochronology to be applied to a language. This leads to a critique of the glottochronologic formula because some linguists argue that the morpheme decay rate is not guaranteed to stay the same throughout history. American Linguist Robert Lees obtained a value for the "glottochronological constant" (r) of words by considering the known changes in 13 pairs of languages using the 200 word list. He obtained a value of 0.8048 ± 0.0176 with 90% confidence. For his 100-word list Swadesh obtained a value of 0.86, the higher value reflecting the elimination of semantically unstable words. === Divergence time === The basic formula of glottochronology proposed by Morris Swadesh is: t = − ln ⁡ ( c ) 2 ln ⁡ ( r ) {\displaystyle t=-{\frac {\ln(c)}{2\ln(r)}}} t = a given period of time from one stage of the language to another (measured in millennia), c = proportion of wordlist items retained at the end of that period and r = rate of replacement for that word list. By testing historically verifiable cases in which t is known by nonlinguistic data (such as the approximate distance from Classical Latin to modern Romance languages), Swadesh arrived at the empirical value of approximately 0.14 for L, (c?) which means that the rate of replacement constitutes around 14 words from the 100-wordlist per millennium. This is represented in the table below. === Results === Glottochronology was applied to a range of language families, including Salishan, Indo-European, Japonic, Afro-Asiatic, Chinese and Mayan and other American languages. For Amerind, correlations have been obtained with radiocarbon dating and blood groups as well as archaeology. === Example Wordlist === Below is an example of a basic word list composed of basic Turkish words and their English translations. == Discussion == The concept of language change is old, and its history is reviewed in Hymes (1973) and Wells (1973). In some sense, glottochronology is a reconstruction of history and can often be closely related to archaeology. Many linguistic studies find the success of glottochronology to be found alongside archaeological data. Glottochronology itself dates back to the mid-20th century. An introduction to the subject is given in Embleton (1986) and in McMahon and McMahon (2005). Glottochronology has been controversial ever since, partly because of issues of accuracy but also because of the question of whether its basis is sound (for example, Bergsland 1958; Bergsland and Vogt 1962; Fodor 1961; Chrétien 1962; Guy 1980). The concerns have been addressed by Dobson et al. (1972), Dyen (1973) and Kruskal, Dyen and Black (1973). The assumption of a single-word replacement rate can distort the divergence-time estimate when borrowed words are included (Thomason and Kaufman 1988). The presentations vary from "Why linguists don't do dates" to the one by Starostin discussed below. Since its original inception, glottochronology has been rejected by many linguists, mostly Indo-Europeanists of the school of the traditional comparative method. Criticisms have been answered in particular around three points of discussion: Criticism levelled against the higher stability of lexemes in Swadesh lists alone (Haarmann 1990) misses the point because a certain amount of losses only enables the computations (Sankoff 1970). The non-homogeneity of word lists often leads to lack of understanding between linguists. Linguists also have difficulties finding a completely unbiased list of basic cultural words. it can take a long time for linguists to find a viable word list which can take several test lists to find a usable list. Traditional glottochronology presumes that language changes at a stable rate. Thus, in Bergsland & Vogt (1962), the authors make an impressive demonstration, on the basis of actual language data verifiable by extralinguistic sources, that the "rate of change" for Icelandic constituted around 4% per millennium, but for closely connected Riksmal (Literary Norwegian), it would amount to as much as 20% (Swadesh's proposed "constant rate" was supposed to be around 14% per millennium). That and several other similar examples effectively proved that Swadesh's formula would not work on all available material, which is a serious accusation since evidence that can be used to "calibrate" the meaning of L (language history recorded during prolonged periods of time) is not overwhelmingly large in the first place. It is highly likely that the chance of replacement is different for every word or feature ("each word has its own history", among hundreds of other sources:). That global assumption has been modified and downgraded to single words, even in single languages, in many newer attempts (see below). There is a lack of understanding of Swadesh's mathematical/statistical methods. Some linguists reject the methods in full because the statistics lead to 'probabilities' when linguists trust 'certainties' more. A serious argument is that language change arises from socio-historical events that are, of course, unforeseeable and, therefore, uncomputable. == Modifications == Somewhere in between the original concept of Swadesh and the rejection of glottochronology in its entirety lies the idea that glottochronology as a formal method of linguistic
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Metigo

metigo is a software application that performs image-based modelling and close range photogrammetry. It produces rectified imagery plans, true ortho-projections on planar, cylindric and conic surfaces, 3D photorealistic models, measurements from photography and mappings on a photographic base for uses in the cultural heritage sector, mainly conservation. == Products == The metigo product line currently consists of the mapping software metigo MAP, the stereo-photogrammetry modeling software metigo 3D, the free viewer metigo VIEW. These products are all standalone and are not depending on other software, such as AutoCAD. === metigo MAP === metigo MAP is mainly used to map findings and conservation measured on a uniform metric photographic base. Therefore, photos of planar surfaces can be rectified based on geometrical informations, e.g. height and width of a rectangle, or cartesian coordinates measured by total station. Beside rectified imagery several other metric mapping bases can be imported and used: true ortho-projections; scaled scans of plans and plots; CAD-files; 3D models, such as digital surface models (DSM) produced by stereo-photogrammetry, SfM or 3D scanning. metigo MAP 's strong point is that rectified imagery taken with different techniques (visual light, sided light, IR, UV, UV-fluorescence, X-ray), historic images and photos taken at various stages of the conservation process can be superimposed and evaluated mutually. The user can allocate several attributes, such as different conservation measures and damage classes, to the mapped geometries. The mappings can be analysed by geometries as well as by user-defined attributes at any stage of the project. metigo MAP targets mainly conservators in different cultural heritage fields. Using it no specialist knowledge of surveying and photogrammetric techniques are needed. === metigo 3D === metigo 3D is a stereo-photogrammetric kit that allows to calculate bundle adjustments (axios3D), create high-quality 3D point clouds using multiple stereo photo pairs combined with metric survey data, mesh these point clouds, texture the meshes with high-resolution image data to create photo-realistic models, ortho-project orientated images on digital surface models (DSM) on planes and best-fit cylinders and cones, create unwrappings and developed views of curved surfaces, analyse deformations of 3D surfaces. metigo 3D targets metric survey specialists working in the cultural heritage sector. == Supported file formats == metigo has the ability to read the following formats: images: JPEG (.jpg), Tiff (.tif), Bitmaps (.bmp), CompuServ (.gif), Encapsualated Postscript (.eps), PCX (.pcx), Photo-CD (.pcd), PICT (.pcd), PNG (.png), Targa (.tga), RAW-format of several camera brands. CAD: DBX, DXF, DWG. 3D: many ASCII-formats (.stl, .wrl, etc.) point data: format editor for ASCII files. == Supported languages == Currently, an English and German version of the software is supported. For metigo MAP beside these a French and Polish GUI is offered for sale. == Applications == The main applications of metigo are: conservation in the cultural heritage context, e.g. stone conservation paintings tapestry etc. architecture, archaeology, many other are possible, e.g. forensics. == History == The first public release of metigo was in 2000.
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Vasant Honavar

Vasant G. Honavar is an Indian-American computer scientist, and artificial intelligence, machine learning, big data, data science, causal inference, knowledge representation, bioinformatics and health informatics researcher and professor. == Early life and education == Vasant Honavar was born at Pune, India to Bhavani G. and Gajanan N. Honavar. He received his early education at the Vidya Vardhaka Sangha High School and M.E.S. College in Bangalore, India. He received a B.E. in Electronics & Communications Engineering from the B.M.S. College of Engineering in Bangalore, India in 1982, when it was affiliated with Bangalore University, an M.S. in electrical and computer engineering in 1984 from Drexel University, and an M.S. in computer science in 1989, and a Ph.D. in 1990, respectively, from the University of Wisconsin–Madison, where he studied Artificial Intelligence and worked with Leonard Uhr. == Career == Honavar is on the faculty of Informatics and Intelligent Systems Department in the Penn State College of Information Sciences and Technology at Pennsylvania State University where he currently holds the Dorothy Foehr Huck and J. Lloyd Huck Chair in Biomedical Data Sciences and Artificial Intelligence and previously held the Edward Frymoyer Endowed Chair in Information Sciences and Technology. He serves on the faculties of the graduate programs in Computer Science, Informatics, Bioinformatics and Genomics, Neuroscience, Operations Research, Public Health Sciences, and of undergraduate programs in Data Science and Artificial Intelligence methods and applications. Honavar serves as the director of the Artificial Intelligence Research Laboratory, Director of Strategic Initiatives for the Institute for Computational and Data Sciences and the director of the Center for Artificial Intelligence Foundations and Scientific Applications at Pennsylvania State University. Honavar served on the Leadership Team of the Northeast Big Data Innovation Hub. Honavar served on the Computing Research Association's Computing Community Consortium Council during 2014-2017, where he chaired the task force on Convergence of Data and Computing, and was a member of the task force on Artificial Intelligence. Honavar was the first Sudha Murty Distinguished Visiting Chair of Neurocomputing and Data Science by the Indian Institute of Science, Bangalore, India. Honavar was named a Distinguished Member of the Association for Computing Machinery for "outstanding scientific contributions to computing"; and elected a Fellow of the American Association for the Advancement of Science for his "distinguished research contributions and leadership in data science". As a Program Director in the Information Integration and Informatics program in the Information and Intelligent Systems Division of the Computer and Information Science and Engineering Directorate of the US National Science Foundation during 2010-13, Honavar led the Big Data Program. Honavar was a professor of computer science at Iowa State University where he led the Artificial Intelligence Research Laboratory which he founded in 1990 and was instrumental in establishing an interdepartmental graduate program in Bioinformatics and Computational Biology (and served as its Chair during 2003–2005). Honavar has held visiting professorships at Carnegie Mellon University, the University of Wisconsin–Madison, and at the Indian Institute of Science. == Research == Honavar's research has contributed to advances in artificial intelligence, machine learning, causal inference, knowledge representation, neural networks, semantic web, big data analytics, and bioinformatics and computational biology. He was a program chair of the Association for the Advancement of Artificial Intelligence(AAAI)'s 36th Conference on Artificial Intelligence. He has published over 300 research articles, including many highly cited ones, as well as several books on these topics. His recent work has focused on federated machine learning algorithms for constructing predictive models from distributed data and linked open data, learning predictive models from high dimensional longitudinal data, reasoning with federated knowledge bases, detecting algorithmic bias, big data analytics, analysis and prediction of protein-protein, protein-RNA, and protein-DNA interfaces and interactions, social network analytics, health informatics, secrecy-preserving query answering, representing and reasoning about preferences, and causal inference from complex, e.g., relational, data, large language models, diffusion models, and meta analysis. Honavar has been active in fostering national and international scientific collaborations in Artificial Intelligence, Data Sciences, and their applications in addressing national, international, and societal priorities in accelerating science, improving health, transforming agriculture through partnerships that bring together academia, non-profits, and industry. He is also active in making the science policy case for major national research initiatives such as AI for accelerating science and AI for combating the epidemic of diseases of despair. == Honors == National Science Foundation Director's Award for Superior Accomplishment, 2013 National Science Foundation Director's Award for Collaborative Integration, 2012 Margaret Ellen White Graduate Faculty Award, Iowa State University, 2011 Outstanding Career Achievement in Research Award, College of Liberal Arts and Sciences, Iowa State University, 2008 Regents Award for Faculty Excellence, Iowa Board of Regents, 2007 Edward Frymoyer Endowed Chair in Information Sciences and Technology, Penn State College of Information Sciences and Technology, Pennsylvania State University, 2013 Senior Faculty Research Excellence Award, Penn State College of Information Sciences and Technology, Pennsylvania State University, 2016 125 People of Impact, Department of Electrical and Computer Engineering, University of Wisconsin-Madison, 2016 Sudha Murty Distinguished (Visiting) Chair of Neurocomputing and Data Science, Indian Institute of Science, 2016-2021 ACM Distinguished Member, 2018 AAAS Fellow American Association for the Advancement of Science, 2018 EAI Fellow European Alliance for Innovation, 2019 Dorothy Foehr Huck and J. Lloyd Huck Chair in Biomedical Data Sciences and Artificial Intelligence, Pennsylvania State University, 2021
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Top 10 AI Headshot Generators Compared (2026)

Trying to pick the best AI headshot generator? An AI headshot generator is software that uses machine learning to help you get more done — it scales effortlessly from a single task to thousands. The best picks balance beginner-friendly simplicity with the depth power users need, and they ship updates often. Whether you are a beginner or a pro, the right AI headshot generator slots into your workflow and pays for itself fast. Read on for hands-on impressions, pricing tiers, and the standout features that matter.
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