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Circular thresholding

Circular thresholding is an algorithm for automatic image threshold selection in image processing. Most threshold selection algorithms assume that the values (e.g. intensities) lie on a linear scale. However, some quantities such as hue and orientation are a circular quantity, and therefore require circular thresholding algorithms. The example shows that the standard linear version of Otsu's method when applied to the hue channel of an image of blood cells fails to correctly segment the large white blood cells (leukocytes). In contrast the white blood cells are correctly segmented by the circular version of Otsu's method. == Methods == There are a relatively small number of circular image threshold selection algorithms. The following examples are all based on Otsu's method for linear histograms: (Tseng, Li and Tung 1995) smooth the circular histogram, and apply Otsu's method. The histogram is cyclically rotated so that the selected threshold is shifted to zero. Otsu's method and histogram rotation are applied iteratively until several heuristics involving class size, threshold location, and class variance are satisfied. (Wu et al. 2006) smooth the circular histogram until it contains only two peaks. The histogram is cyclically rotated so that the midpoint between the peaks is shifted to zero. Otsu's method and histogram rotation are applied iteratively until convergence of the threshold. (Lai and Rosin 2014) applied Otsu's method to the circular histogram. For the two class circular thresholding task they showed that, for a histogram with an even number of bins, the optimal solution for Otsu's criterion of within-class variance is obtained when the histogram is split into two halves. Therefore the optimal solution can be efficiently obtained in linear rather than quadratic time. == References and further reading == D.-C. Tseng, Y.-F. Li, and C.-T. Tung, Circular histogram thresholding for color image segmentation in Proc. Int. Conf. Document Anal. Recognit., 1995, pp. 673–676. J. Wu, P. Zeng, Y. Zhou, and C. Olivier, A novel color image segmentation method and its application to white blood cell image analysis in Proc. Int. Conf. Signal Process., vol. 2. 2006, pp. 16–20. Y.K. Lai, P.L. Rosin, Efficient Circular Thresholding, IEEE Trans. on Image Processing 23(3), 992–1001 (2014). doi:10.1109/TIP.2013.2297014
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Flex (lexical analyzer generator)

Flex (fast lexical analyzer generator) is a free and open-source software alternative to lex. It is a computer program that generates lexical analyzers (also known as "scanners" or "lexers"). It is frequently used as the lex implementation together with Berkeley Yacc parser generator on BSD-derived operating systems (as both lex and yacc are part of POSIX), or together with GNU bison (a version of yacc) in BSD ports and in Linux distributions. Unlike Bison, flex is not part of the GNU Project and is not released under the GNU General Public License, although a manual for Flex was produced and published by the Free Software Foundation. == History == Flex was written in C around 1987 by Vern Paxson, with the help of many ideas and much inspiration from Van Jacobson. Original version by Jef Poskanzer. The fast table representation is a partial implementation of a design done by Van Jacobson. The implementation was done by Kevin Gong and Vern Paxson. == Example lexical analyzer == This is an example of a Flex scanner for the instructional programming language PL/0. The tokens recognized are: '+', '-', '', '/', '=', '(', ')', ',', ';', '.', ':=', '<', '<=', '<>', '>', '>='; numbers: 0-9 {0-9}; identifiers: a-zA-Z {a-zA-Z0-9} and keywords: begin, call, const, do, end, if, odd, procedure, then, var, while. == Internals == These programs perform character parsing and tokenizing via the use of a deterministic finite automaton (DFA). A DFA is a theoretical machine accepting regular languages, and is equivalent to read-only right moving Turing machines. The syntax is based on the use of regular expressions. See also nondeterministic finite automaton. == Issues == === Time complexity === A Flex lexical analyzer usually has time complexity O ( n ) {\displaystyle O(n)} in the length of the input. That is, it performs a constant number of operations for each input symbol. This constant is quite low: GCC generates 12 instructions for the DFA match loop. Note that the constant is independent of the length of the token, the length of the regular expression and the size of the DFA. However, using the REJECT macro in a scanner with the potential to match extremely long tokens can cause Flex to generate a scanner with non-linear performance. This feature is optional. In this case, the programmer has explicitly told Flex to "go back and try again" after it has already matched some input. This will cause the DFA to backtrack to find other accept states. The REJECT feature is not enabled by default, and because of its performance implications its use is discouraged in the Flex manual. === Reentrancy === By default the scanner generated by Flex is not reentrant. This can cause serious problems for programs that use the generated scanner from different threads. To overcome this issue there are options that Flex provides in order to achieve reentrancy. A detailed description of these options can be found in the Flex manual. === Usage under non-Unix environments === Normally the generated scanner contains references to the unistd.h header file, which is Unix specific. To avoid generating code that includes unistd.h, %option nounistd should be used. Another issue is the call to isatty (a Unix library function), which can be found in the generated code. The %option never-interactive forces flex to generate code that does not use isatty. === Using flex from other languages === Flex can only generate code for C and C++. To use the scanner code generated by flex from other languages a language binding tool such as SWIG can be used. === Unicode support === Flex is limited to matching 1-byte (8-bit) binary values and therefore does not support Unicode. RE/flex and other alternatives do support Unicode matching. == Flex++ == flex++ is a similar lexical scanner for C++ which is included as part of the flex package. The generated code does not depend on any runtime or external library except for a memory allocator (malloc or a user-supplied alternative) unless the input also depends on it. This can be useful in embedded and similar situations where traditional operating system or C runtime facilities may not be available. The flex++ generated C++ scanner includes the header file FlexLexer.h, which defines the interfaces of the two C++ generated classes.
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Best AI Code Generators in 2026

Comparing the best AI code generator? An AI code generator 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 code generator slots into your workflow and pays for itself fast. We tested the leading options and ranked them by quality, value, and ease of use.
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How to Choose an AI Art Generator

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

A co-occurrence matrix or co-occurrence distribution (also referred to as : gray-level co-occurrence matrices GLCMs) is a matrix that is defined over an image to be the distribution of co-occurring pixel values (grayscale values, or colors) at a given offset. It is used as an approach to texture analysis with various applications especially in medical image analysis. == Method == Given a grey-level image I {\displaystyle I} , co-occurrence matrix computes how often pairs of pixels with a specific value and offset occur in the image. The offset, ( Δ x , Δ y ) {\displaystyle (\Delta x,\Delta y)} , is a position operator that can be applied to any pixel in the image (ignoring edge effects): for instance, ( 1 , 2 ) {\displaystyle (1,2)} could indicate "one down, two right". An image with p {\displaystyle p} different pixel values will produce a p × p {\displaystyle p\times p} co-occurrence matrix, for the given offset. The ( i , j ) th {\displaystyle (i,j)^{\text{th}}} value of the co-occurrence matrix gives the number of times in the image that the i th {\displaystyle i^{\text{th}}} and j th {\displaystyle j^{\text{th}}} pixel values occur in the relation given by the offset. For an image with p {\displaystyle p} different pixel values, the p × p {\displaystyle p\times p} co-occurrence matrix C is defined over an n × m {\displaystyle n\times m} image I {\displaystyle I} , parameterized by an offset ( Δ x , Δ y ) {\displaystyle (\Delta x,\Delta y)} , as: C Δ x , Δ y ( i , j ) = ∑ x = 1 n ∑ y = 1 m { 1 , if I ( x , y ) = i and I ( x + Δ x , y + Δ y ) = j 0 , otherwise {\displaystyle C_{\Delta x,\Delta y}(i,j)=\sum _{x=1}^{n}\sum _{y=1}^{m}{\begin{cases}1,&{\text{if }}I(x,y)=i{\text{ and }}I(x+\Delta x,y+\Delta y)=j\\0,&{\text{otherwise}}\end{cases}}} where: i {\displaystyle i} and j {\displaystyle j} are the pixel values; x {\displaystyle x} and y {\displaystyle y} are the spatial positions in the image I; the offsets ( Δ x , Δ y ) {\displaystyle (\Delta x,\Delta y)} define the spatial relation for which this matrix is calculated; and I ( x , y ) {\displaystyle I(x,y)} indicates the pixel value at pixel ( x , y ) {\displaystyle (x,y)} . The 'value' of the image originally referred to the grayscale value of the specified pixel, but could be anything, from a binary on/off value to 32-bit color and beyond. (Note that 32-bit color will yield a 232 × 232 co-occurrence matrix!) Co-occurrence matrices can also be parameterized in terms of a distance, d {\displaystyle d} , and an angle, θ {\displaystyle \theta } , instead of an offset ( Δ x , Δ y ) {\displaystyle (\Delta x,\Delta y)} . Any matrix or pair of matrices can be used to generate a co-occurrence matrix, though their most common application has been in measuring texture in images, so the typical definition, as above, assumes that the matrix is an image. It is also possible to define the matrix across two different images. Such a matrix can then be used for color mapping. == Aliases == Co-occurrence matrices are also referred to as: GLCMs (gray-level co-occurrence matrices) GLCHs (gray-level co-occurrence histograms) spatial dependence matrices == Application to image analysis == Whether considering the intensity or grayscale values of the image or various dimensions of color, the co-occurrence matrix can measure the texture of the image. Because co-occurrence matrices are typically large and sparse, various metrics of the matrix are often taken to get a more useful set of features. Features generated using this technique are usually called Haralick features, after Robert Haralick. Texture analysis is often concerned with detecting aspects of an image that are rotationally invariant. To approximate this, the co-occurrence matrices corresponding to the same relation, but rotated at various regular angles (e.g. 0, 45, 90, and 135 degrees), are often calculated and summed. Texture measures like the co-occurrence matrix, wavelet transforms, and model fitting have found application in medical image analysis in particular. == Other applications == Co-occurrence matrices are also used for words processing in natural language processing (NLP).
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The Best Free AI Virtual Assistant for Beginners

Comparing the best AI virtual assistant? An AI virtual assistant 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 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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AI Sales Assistants: Free vs Paid (2026)

Trying to pick the best AI sales assistant? An AI sales assistant 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 sales assistant 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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Margaret Mitchell (scientist)

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

Security awareness is the knowledge and attitude members of an organization possess regarding the protection of the physical, and especially informational, assets of that organization. However, it is very tricky to implement because organizations are not able to impose such awareness directly on employees as there are no ways to explicitly monitor people's behavior. That being said, the literature does suggest several ways that such security awareness could be improved. Many organizations require formal security awareness training for all workers when they join the organization and periodically thereafter, usually annually. Another main force that is found to have a strong correlation with employees' security awareness is managerial security participation. It also bridges security awareness with other organizational aspects. == Relationship between Security Awareness and Human Factors == Employees' behavior, cognitive biases, and decision-making processes influence the effectiveness of security measures. Research indicates that psychological factors, such as optimism bias, overconfidence, and habitual behaviors, can undermine security awareness initiatives. To address these challenges, organizations are increasingly using behavioral analytics and security nudges—subtle prompts like password reminders and phishing warnings—to encourage secure behavior. Human error remains the leading cause of cybersecurity incidents. A 2023 IBM Security report found that 95% of breaches are due to human mistakes, including falling for phishing emails, using weak passwords, and mishandling sensitive data. Organizations emphasize security awareness training as a key strategy to mitigate this risk. It is particularly important for leadership to foster a culture of cybersecurity and to provide targeted training to increase security awareness among all employees across the organization. == Coverage == Topics covered in security awareness training include: The nature of sensitive material and physical assets they may come in contact with, such as trade secrets, privacy concerns and government classified information Employee and contractor responsibilities in handling sensitive information, including review of employee nondisclosure agreements Requirements for proper handling of sensitive material in physical form, including marking, transmission, storage and destruction Proper methods for protecting sensitive information on computer systems, including password policy and use of two-factor authentication Other computer security concerns, including malware, phishing, social engineering, etc. Workplace security, including building access, wearing of security badges, reporting of Incidents, forbidden articles, etc. Consequences of failure to properly protect information, including potential loss of employment, economic consequences to the firm, damage to individuals whose private records are divulged, and possible civil and criminal penalties Security awareness means understanding that there is the potential for some people to deliberately or accidentally steal, damage, or misuse the data that is stored within a company's computer systems and throughout its organization. Therefore, it would be prudent to support the assets of the institution (information, physical, and personal) by trying to stop that from happening. According to the European Network and Information Security Agency, "Awareness of the risks and available safeguards is the first line of defence for the security of information systems and networks." "The focus of Security Awareness consultancy should be to achieve a long term shift in the attitude of employees towards security, whilst promoting a cultural and behavioural change within an organisation. Security policies should be viewed as key enablers for the organisation, not as a series of rules restricting the efficient working of your business." == Role of Gamification and Interactive Training == Modern security awareness programs increasingly utilize gamification, phishing simulations, and interactive learning modules. Studies have shown that engaging employees through serious games, reward systems, and real-world attack simulations improves retention and application of security practices. One example is phishing simulation training, where employees receive simulated phishing emails to test their ability to recognize threats. Research indicates that repeated exposure to such exercises leads to long-term improvements in security awareness. == Legislation and Compliance Requirements == Many industries mandate security awareness training to comply with regulations such as: General Data Protection Regulation (GDPR) – requires organizations to ensure data protection awareness among employees. Health Insurance Portability and Accountability Act (HIPAA) – mandates security awareness programs for healthcare providers. Payment Card Industry Data Security Standard (PCI-DSS) – enforces security training for businesses handling payment card information. == Measuring security awareness == In a 2016 study, researchers developed a method of measuring security awareness. Specifically they measured "understanding about circumventing security protocols, disrupting the intended functions of systems or collecting valuable information, and not getting caught" (p. 38). The researchers created a method that could distinguish between experts and novices by having people organize different security scenarios into groups. Experts will organize these scenarios based on centralized security themes where novices will organize the scenarios based on superficial themes. Security awareness is also assessed through real-time security metrics, such as tracking phishing click rates, password reuse tendencies, and policy adherence rates. Organizations are adopting continuous monitoring strategies to provide immediate feedback to employees about risky behavior and suggest corrective actions. == Evolving cyber threats and security awareness strategies == As cyber threats continue to evolve, security awareness programs must adapt to new attack vectors, such as AI-driven cyberattacks, deepfakes, and insider threats. ENISA's Threat Landscape report highlights the increasing prominence of these emerging threats, stressing the need for security measures that address both traditional attacks like ransomware and malware, as well as more sophisticated techniques such as Living Off Trusted Sites (LOTS) and advanced evasion methods used by cybercriminals.
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Lenhart Schubert

Lenhart Karl Otto Schubert is a professor of Computer Science at the University of Rochester, as well as a member of the Center for Language Sciences and the Center for Computation and the Brain. Schubert is a prominent researcher in the field of common sense reasoning. == Biography == Schubert received his Ph.D. from the University of Toronto in 1970. He was on the faculty of the University of Alberta between 1973 and 1988 and joined the faculty at the University of Rochester in 1988. He was elected fellow of Association for Advancement of Artificial Intelligence in 1993 for "fundamental contributions in NLP, esp. in the formalization, representation, and practical implementation of non-first order concepts".
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Frank Hutter

Frank Hutter is a German computer scientist recognized for his contributions to machine learning, particularly in the areas of automated machine learning (AutoML), hyperparameter optimization, meta-learning and tabular machine learning. He is currently a Hector-Endowed Fellow and PI at the ELLIS Institute Tübingen and a Full Professor (W3) for Machine Learning at the Department of Computer Science, University of Freiburg. Hutter is known for his role in establishing AutoML as a key area in artificial intelligence research. == Education and academic career == Frank Hutter received his academic training in computer science at Darmstadt University of Technology, where he completed his Vordiplom (comparable to a BSc) and Hauptdiplom (equivalent to MSc) by 2004. He later pursued his PhD at the University of British Columbia, under the supervision of Profs. Holger Hoos, Kevin Leyton-Brown and Kevin Murphy, where his doctoral thesis, titled "Automated Configuration of Algorithms for Solving Hard Computational Problems," was awarded the CAIAC Doctoral Dissertation Award for the best thesis in Artificial Intelligence completed at a Canadian university in 2009. Hutter did his postdoctoral research at the University of British Columbia, where he worked from 2009 to 2013. In 2013, he moved to the University of Freiburg, initially leading an Emmy Noether Research Group, and in 2017, he was appointed as a Full Professor. His contributions to machine learning have been recognized globally, particularly his work in AutoML and hyperparameter optimization. Overall, Hutter has authored over 180 peer-reviewed publications, which have garnered more than 89,000 citations, reflecting the high impact of his work. == Contributions in AutoML == Hutter's early research laid the groundwork for the field of Automated Machine Learning (AutoML). He has been a key figure in establishing AutoML as a distinct research area. Along with various colleagues, he organized the AutoML workshops from 2014 to 2021, wrote the first book on AutoML and taught the first MOOC on AutoML. He also co-founded the AutoML conference in 2022 and served as its general chair the first two years. He also published prominent works in various subfields of AutoML, such as hyperparameter optimization, neural architecture search, meta-Learning and AutoML systems. He is currently the most highly cited researcher in AutoML. == Contributions in machine learning for tabular data == Hutter has also made many contributions to machine learning for tabular data. He led the development of the first widely adopted AutoML system for tabular data, AutoWEKA, which was published at KDD 2013 and received the test of time award at KDD (2023). Subsequently, he led the development of Auto-sklearn, the first highly used AutoML system for tabular data in Python, and with it, won the first international AutoML challenge and the subsequent second international AutoML challenge, both of which only included tabular data. More recently, he focused on tabular foundation models, including TabPFN, which was published in Nature magazine. In 2024, he also co-founded Prior Labs, the first company focusing on tabular foundation models. == Awards and honors == Hutter has received numerous awards throughout his career. In 2023, he won the KDD Test of Time Award for Research together with Chris Thornton, Holger H. Hoos, and Kevin Leyton-Brown. He has received three grants from the ERC, including the ERC Starting Grant (2016) and ERC Consolidator Grant (2022), as well as an ERC Proof of Concept Grant (2020). In 2021, he became an ELLIS Unit Director and was also recognized as a EurAI Fellow, in addition to receiving the AIJ Prominent Paper Award. Earlier, he was a recipient of the Google Faculty Research Award in 2018. His groundbreaking research was acknowledged early in his career with the IJCAI Distinguished Paper Award in 2013 and the IJCAI/JAIR Best Paper Prize in 2010. == Representative publications == Hutter, F. Kotthoff, L. and Vanschoren, J., editors. Automated machine learning: methods, systems, challenges, Springer Nature, 2019. www.automl.org/book. Feurer, M., Klein, A., Eggensperger, K., Springenberg, T., Blum, M., Hutter, F. Efficient and Robust Automated Machine Learning. In NeurIPS 2015. Loshchilov, I., and Hutter, F. Decoupled weight decay regularization. In ICLR 2018. Zela, A., Elsken, T. ,Saikia, T. ,Marrakschi, Y. ,Brox, T. and Hutter. ,F.Understanding and Robustifying Differentiable Architecture Search. In ICLR 2020. Hollmann, N., Müller, S., Eggensperger, K. and Hutter, F. TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second, In ICLR 2023.
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Li Sheng (computer scientist)

Li Sheng (Chinese: 李生; born 1943), is a professor at the School of Computer Science and Engineering, Harbin Institute of Technology (HIT), China. He began his research on Chinese-English machine translation in 1985, making himself one of the earliest Chinese scholars in this field. After that, he pursued in vast topics of natural language processing, including machine translation, information retrieval, question answering and applied artificial intelligence. He was the final review committee member for computer area in NSF China. Born and raised in Heilongjiang province, he graduated in 1965 from the computer specialty of HIT, which is one of the earliest computer specialties in Chinese universities. Then he started to work as a staff in the Computer specialty of HIT, which was finally granted as a department in 1985. Also from 1985, he was appointed to undertake a series administrative positions in HIT, e.g. Dean of Computer Department(1987–1988), Director of R&D Division (1988–1990), Chief R&D Officer and several other key leading positions in HIT. Resigned all his administrative positions in 2004, Li devoted himself as the director of MOE-Microsoft Join Key Lab of NLP& Speech (HIT), making it a leading NLP research group with more than 100 staffs and students working on various aspects of NLP. So far, the lab has already been granted for dozens of technology awards by the ministries of central government and local provincial government of China. Its research progresses are reported annually in top tier conferences including ACL, IJCAI, SIGIR etc. As one of the pioneers in NLP research in China, he contributes NLP in China not only in technology innovations but also in talents education. So far, his research group has graduated more than 60 Ph.D. and almost 200 M.E with NLP major. Most of them are now working as the chief researcher in various NLP groups of universities and companies in China, including several world-known NLP scholars, such as Wang Haifeng of Baidu, Zhou Ming of Microsoft Research, Zhang Min (张民) of Soochow University (China), and Zhao Tiejun (赵铁军) and Liu Ting (刘挺) of HIT. Owing to his contributions in Chinese language processing, Li was elected as the President of Chinese Information Processing Society of China (CIPSC) in 2011. He scaled this top level academic organization in China up to more than 3000 registered members, and promoted NLP into several national projects for research or industry development. In addition, the CIPSC is now enhancing its co-operations with world NLP organizations including ACL. == Machine Intelligence & Translation Laboratory (MI&TLAB) == Originates from Machine Translation Research Group of Computer Science Department, Harbin Institute of Technology, which was started Li in 1985. It is one of the earliest institutions engaged in MT research in China, featured by its investigations into Chinese-English machine translation. It is now running under the Research Center on Language Technology, School of Computer Science and Technology, HIT. Details for staffs and publications can be found at https://mitlab.hit.edu.cn. == MOE-MS Joint Key Lab of Natural Language Processing and Speech (HIT) == In June, 2000, the Joint HIT-Microsoft Machine Translation Lab was founded by MI&T Lab and Microsoft Research (China). It was the third joint lab established by Microsoft Research (China) with Chinese universities, and the only one focusing on Machine Translation. Based on this jointly lab, the cooperation between HIT and Microsoft gradually extended to the areas of machine translation, information retrieval, speech recognition and processing, natural language understanding. In Oct, 2004, the joint key lab was granted as one of the 10 joint key labs supported by the Microsoft Research of Asia and Ministry of Education in China. In July 2006, the Shenzhen extension of the lab was launched. More than 200 staff and students have undertaken research projects, including some sponsored by the National Natural Science Foundation of China and the National 863 program of China. Since 2005, the lab has also been organizing a summer camp in Harbin Institute of Technology, and approximately 150 faculty members and students from universities in China have participated. This summer workshop was organized annually until 2014, when it was organized formally as the summer school series by Chinese Information Processing Society, China. Through the lab, a Microsoft Research of Asia-HIT joint PhD program was implemented in 2012. == CEMT-I MT System == In May 1989, CEMT-I passed the formal project appraisal in Harbin, China. Capable of translating technical paper titles from Chinese to English, it is not only the first MT system completed by Li and his group, but also the first Chinese-English Translation system that passed the technical appraisal by Chinese government according to the public reports. It was then awarded the Second Prize of Ministry Level Technology Innovation by the former National Aerospace Industry Corporation in 1990. == Daya Translation Workstation == Owing to the technical achievements by Li's group in Chinese-English machine translation, the former National Aerospace Industry Corporation of China sponsored a commercial system development of "Daya Translation Station (MT)" in 1993. Designed as a comprehensive English composition aid for Chinese users, this system was finished and put into the market in 1995. And in 1997, this system was awarded the Second Prize of Ministry Level Technology Innovation by the former National Aerospace Industry Corporation. == BT863 MT System == From 1994, the researches in Li's lab were supported by National 863 Hi-tech Research and Development Program. During this period, the BT863 system was explored to employ one engine for both Chinese-English and English-Chinese translation. This system was proved to be the best performance among Chinese-English MT systems in the formal technical evaluation of National 863 program, yielding the Third Prize of Ministry Level Technology Innovation by the former National Aerospace Industry Corporation in 1997. == Next Generation IR == This is a key project granted by NSF China (with a joint sponsorship from MSRA) started form 2008. In contrast to his previous NSF grants for different NLP issues, Li explored in his last PI project on key technologies in personalized IR, together with researchers from Tsinghua University and Institute of Software, Chinese Academy of Science. With impressive publications in top tier journals and conferences (including breakthrough publications in SIGIR of his own group), this projected was approved "A-level" achievements by the NSF China office in 2012.
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Linux Trace Toolkit

The Linux Trace Toolkit (LTT) is a set of tools that is designed to log program execution details from a patched Linux kernel and then perform various analyses on them, using console-based and graphical tools. LTT has been mostly superseded by its successor LTTng (Linux Trace Toolkit Next Generation). LTT allows the user to see in-depth information about the processes that were running during the trace period, including when context switches occurred, how long the processes were blocked for, and how much time the processes spent executing vs. how much time the processes were blocked. The data is logged to a text file and various console-based and graphical (GTK+) tools are provided for interpreting that data. In order to do data collection, LTT requires a patched Linux kernel. The authors of LTT claim that the performance hit for a patched kernel compared to a regular kernel is minimal; Their testing has reportedly shown that this is less than 2.5% on a "normal use" system (measured using batches of kernel makes) and less than 5% on a file I/O intensive system (measured using batches of tar). == Usage == === Collecting trace data === Data collection is Started by: trace 15 foo This command will cause the LTT tracedaemon to do a trace that lasts for 15 seconds, writing trace data to foo.trace and process information from the /proc filesystem to foo.proc. The trace command is actually a script which runs the program tracedaemon with some common options. It is possible to run tracedaemon directly and in that case, the user can use a number of command-line options to control the data which is collected. For the complete list of options supported by tracedaemon, see the online manual page for tracedaemon. === Viewing the results === Viewing the results of a trace can be accomplished with: traceview foo This command will launch a graphical (GTK+) traceview tool that will read from foo.trace and foo.proc. This tool can show information in various interesting ways, including Event Graph, Process Analysis, and Raw Trace. The Event Graph is perhaps the most interesting view, showing the exact timing of events like page faults, interrupts, and context switches, in a simple graphical way. The traceview command is a wrapper for a program called tracevisualizer. For the complete list of options supported by tracevisualizer, see the online manual page for tracevisualizer.
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Apache cTAKES

Apache cTAKES: clinical Text Analysis and Knowledge Extraction System is an open-source Natural Language Processing (NLP) system that extracts clinical information from electronic health record unstructured text. It processes clinical notes, identifying types of clinical named entities — drugs, diseases/disorders, signs/symptoms, anatomical sites and procedures. Each named entity has attributes for the text span, the ontology mapping code, context (family history of, current, unrelated to patient), and negated/not negated. cTAKES was built using the UIMA Unstructured Information Management Architecture framework and OpenNLP natural language processing toolkit. == Components == Components of cTAKES are specifically trained for the clinical domain, and create rich linguistic and semantic annotations that can be utilized by clinical decision support systems and clinical research. These components include: Named Section identifier Sentence boundary detector Rule-based tokenizer Formatted list identifier Normalizer Context dependent tokenizer Part-of-speech tagger Phrasal chunker Dictionary lookup annotator Context annotator Negation detector Uncertainty detector Subject detector Dependency parser patient smoking status identifier Drug mention annotator == History == Development of cTAKES began at the Mayo Clinic in 2006. The development team, led by Dr. Guergana Savova and Dr. Christopher Chute, included physicians, computer scientists and software engineers. After its deployment, cTAKES became an integral part of Mayo's clinical data management infrastructure, processing more than 80 million clinical notes. When Dr. Savova's moved to Boston Children's Hospital in early 2010, the core development team grew to include members there. Further external collaborations include: University of Colorado Brandeis University University of Pittsburgh University of California at San Diego Such collaborations have extended cTAKES' capabilities into other areas such as Temporal Reasoning, Clinical Question Answering, and coreference resolution for the clinical domain. In 2010, cTAKES was adopted by the i2b2 program and is a central component of the SHARP Area 4. In 2013, cTAKES released their first release as an Apache Software Foundation incubator project: cTAKES 3.0. In March 2013, cTAKES became an Apache Software Foundation Top Level Project (TLP).
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How to Choose an AI Logo Maker

Trying to pick the best AI logo maker? An AI logo maker 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 logo maker 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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