Sentence extraction is a technique used for automatic summarization of a text. In this shallow approach, statistical heuristics are used to identify the most salient sentences of a text. Sentence extraction is a low-cost approach compared to more knowledge-intensive deeper approaches which require additional knowledge bases such as ontologies or linguistic knowledge. In short, sentence extraction works as a filter that allows only meaningful sentences to pass. The major downside of applying sentence-extraction techniques to the task of summarization is the loss of coherence in the resulting summary. Nevertheless, sentence extraction summaries can give valuable clues to the main points of a document and are frequently sufficiently intelligible to human readers. == Procedure == Usually, a combination of heuristics is used to determine the most important sentences within the document. Each heuristic assigns a (positive or negative) score to the sentence. After all heuristics have been applied, the highest-scoring sentences are included in the summary. The individual heuristics are weighted according to their importance. === Early approaches and some sample heuristics === Seminal papers which laid the foundations for many techniques used today have been published by Hans Peter Luhn in 1958 and H. P Edmundson in 1969. Luhn proposed to assign more weight to sentences at the beginning of the document or a paragraph. Edmundson stressed the importance of title-words for summarization and was the first to employ stop-lists in order to filter uninformative words of low semantic content (e.g. most grammatical words such as of, the, a). He also distinguished between bonus words and stigma words, i.e. words that probably occur together with important (e.g. the word form significant) or unimportant information. His idea of using key-words, i.e. words which occur significantly frequently in the document, is still one of the core heuristics of today's summarizers. With large linguistic corpora available today, the tf–idf value which originated in information retrieval, can be successfully applied to identify the key words of a text: If for example the word cat occurs significantly more often in the text to be summarized (TF = "term frequency") than in the corpus (IDF means "inverse document frequency"; here the corpus is meant by document), then cat is likely to be an important word of the text; the text may in fact be a text about cats.
Xara Designer Pro+
Xara Designer Pro+ is an image editing program incorporating photo editing and vector illustration tools created by British software company Xara. Xara Xtreme LX was an early open source version for Linux. The Windows version was previously sold under the names Xara Studio, Xara X and Xara Xtreme, and traces its origin in the late 1980s to a title called ArtWorks for the Acorn Archimedes line of computers using RISC OS. There is a pro version called Xara Designer Pro (formerly Xara Xtreme Pro). The current commercial version of Xara Photo & Graphic Designer runs only on Windows, although Xara documents can be edited in a web browser on any platform using the Xara Cloud service. Versions up to 4.x can be run on Linux using Wine. == History == ArtWorks, the predecessor of Xara Photo and Graphic Designer, was developed on Acorn Archimedes and Risc PC 32-bit RISC computers running RISC OS by Computer Concepts during the late 1980s. The first version, developed for Microsoft Windows was initially called Xara Studio. It was licensed to Corel Corporation before wide-scale public availability, and from 1995 to 2000 was released as CorelXARA. Corel ceded the licensing rights back to Xara in 2000. The first Xara X version released in 2000 by its original owner. The next version, Xara X¹, was released in 2004. Xara Xtreme was released in 2005. In November 2006, Xara Xtreme PRO (an enhanced version of Xara Xtreme) was released. Xara Xtreme 3.2 and Xtreme Pro 3.2 were released in May 2007. 3.2 Pro included Xara3D, and both versions had more robust typography. In April 2008, Xara Xtreme 4.0 was released. Xara Xtreme and Xara Xtreme Pro 5.1 were released in June 2009. Features included more text-area enhancements, content-aware scaling of bitmap images, improved file import and export, master-page (repeated) objects, an object gallery (replacing the layer gallery), website-creation tools, and multi-stage graduated transparency. In June 2010, Xara Photo & Graphic Designer 6 and Xara Designer Pro 6 were released. Xtreme was renamed Photo & Graphic Designer, and Xtreme Pro was renamed Designer Pro. In May 2011, Xara Photo & Graphic Designer 7 and Xara Designer Pro 7 were released. Features included "magic" photo erase, user interface improvements to docking galleries and snapping alignment, and (in Pro) new webpage and website-design features. In May 2012, Xara Photo & Graphic Designer 2013 and Xara Designer Pro X (v8) were released. Xara Photo & Graphic Designer 9 was released in May 2013. In July of that year, Xara Designer Pro X9 was released. Xara Photo & Graphic Designer 10 was released on 16 July 2014, and Xara Designer Pro X10 on 23 July. Xara Photo & Graphic Designer 11 was released on 29 June 2015, and Xara Designer Pro X11 was released the following month. In 2016, the delivery model was changed to an update service which can be renewed annually. Users are entitled to any updates released while the update service is active. The first update-service updates were in May 2016 for Xara Photo & Graphic Designer, and July 2016 for Xara Designer Pro X. == Features == Xara Photo & Graphic Designer is known for its usability and fast renderer. It provides a fully anti-aliased display, advanced gradient fill, and transparency tools. Among vector editors, Xara Photo & Graphic Designer is considered to be fairly easy to learn, with similarities to CorelDRAW and Inkscape in terms of interface. Alongside the vector illustration tools, Xara Photo & Graphic Designer also includes an integrated photo tool offering manual and automatic photo enhance, cropping, adjustment of brightness levels, red-eye fix, 'magic' erase, photo healing, color and background erase, panoramas and content aware resizing. Designer Pro includes a wider range of tools for other design tasks including the creation of web pages and websites, and text and page layout tools for DTP with the aim of providing a single solution for all graphic and web design tasks.
AI Image Generators Reviews: What Actually Works in 2026
Trying to pick the best AI image generator? An AI image 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 image generator slots into your workflow and pays for itself fast. Read on for hands-on impressions, pricing tiers, and the standout features that matter.
The Best Free AI Headshot Generator for Beginners
Shopping for the best AI headshot generator? An AI headshot generator is software that uses machine learning to help you get more done — it keeps getting smarter as the underlying models improve. Pricing, accuracy, and the size of the model behind the tool are the three factors that most affect daily usefulness. Whether you are a beginner or a pro, the right AI headshot generator slots into your workflow and pays for itself fast. Below we compare features, pricing, and real output so you can choose with confidence.
MedSLT
MedSLT is a medium-ranged open source spoken language translator developed by the University of Geneva. It is funded by the Swiss National Science Foundation. The system has been designed for the medical domain. It currently covers the doctor-patient diagnosis dialogues for the domains of headache, chest and abdominal pain in English, French, Japanese, Spanish, Catalan and Arabic. The vocabulary used ranges from 350 to 1000 words depending on the domain and language pair. == Motivation for creating MedSLT == With more than 6000 languages worldwide, language barriers become an increasing problem for healthcare. The lack of medical interpreters can lead to disastrous consequences. These range from prolonged hospital stays to wrong diagnosis and medication. A study found that only about half of the 23 million people with limited proficiency in English in the United States had been provided with a medical interpreter. Millions of refugees and immigrants worldwide face similar problems, although not always as severe. The gap between need and availability of language services might be closed with speech translation systems. == Challenges == The biggest challenge is and was to develop an ideal system, though it is not possible to do so at this moment. This system would fit the needs of doctors and the patients alike, and would provide accurate and flexible translation. A realisation of an ideal translation tool is impossible without the use of unrestricted language and a large vocabulary. Medical professionals demand high reliability from translation. This favours rule-based architectures over data-driven. The latter are more suitable for inexperienced users. Rule-based architectures achieve higher accuracy especially if used by experts. Though it is highly desirable to build a bidirectional system supporting a two-way dialogue, which concentrates on patient-centered communication, the patients will have difficult access to the system. Most patients have no experience with such systems. Less reliable results for translation from the patient-to-doctor direction are the outcome. To overcome this the system needs to provide either easy access or an integrated help tool to guide the users through the process. Although controlled rule-based systems achieve good results, they are brittle. To receive good translations the user needs to be familiar with the system and has to know what is covered by the grammar. Covering different sub-domains (headache, chest and abdominal pain) and language pairs presents additional problems. A shared structure and grammar for all subdomains and language pairs minimises development and maintenance costs. The integration of new doctor and patient languages is also a key challenge. Adding new languages should be quick and rather simple, because he system has to be used in many countries to cover multiple language pairs. Direct translation from source to target language proves to be rather difficult. Using interlingua for unidirectional translation instead of a bidirectional approach helps to simplify the translation process. On top of this, the system has to run on different platforms, because mobility is a key issue for many attending physicians. A portable version addresses these issues, but has to deal with the heavy load of the translation process. == The MedSLT system == The system's speech recognition is based on the Nuance 8.5 platform that supports grammar-based language models. All grammars used for recognition, analysis and generation are compiled from a small set of unification grammars. These core grammars are created by the open-source Regulus Grammar Compiler and are automatically specialised using corpus-driven methods. The specialisation considers both the task (recognition, analysis and generation) and the sub-domain (headache, chest and abdominal pain). The specialisation uses the explanation-based learning algorithm to create a treebank from the training corpus. These examples are divided into sets of subtrees by using domain- and grammar-specific rules (also known as "operationality criteria" in machine translation). The subtree rules are combined into a single rule, creating a specialised unification grammar. The grammar is compiled to an executable form, for analysis and generation by a parser or generator, and for recognition of a CFG grammar. A CFG grammar is required for the Nuance engine. Compilation by Nuance-specific criteria turns the grammar into speech recognition packages. The final step uses the training corpus again for statistical tuning of the language model. MedSLT translation processes are based on a rule-based interlingua. The interlingua is treated as an actual language (it is a very simple version of English) and is specified by a Regulus grammar. This grammar does not take account of complex surface syntax phenomena of real languages like movement or agreement. A set of rules is the base for translating the source language semantic representation to interlingua. Another set of rules covers the translation from interlingua to the target language. The semantic representations are converted to surface words using a target language grammar. Defining semantics for a specific domain enables the developers to specify interlingua with a small, tightly constraint semantic grammar. The translations based on interlingua match direct translations almost perfectly, because the development shifts to a decoupled monolingual architecture. A set of combined interlingua corpora, with one corpus per sub-domain, is the core of this architecture. All source language development corpora are translated to interlingua. These are sorted and grouped together with the corresponding source language examples. The interlingua forms are then translated into each target language, and the results are attached together. This organisation improves the translation process. There is no duplicated effort for multilingual regression testing, because each parsing and generation step is performed once. This allows more frequent testing. The representation language used for all forms is Almost Flat Functional semantics. AFF is derived from the Spoken Language Translator, the precursor of MEdSLT. SLT uses Quasi Logical Form, a logical based representation language. QLF is an expressive yet very complex language, causing high development and maintenance costs. A minimal solution was planned for the medical translator. Early versions of the system utilised a language using simple feature-value lists. These lists were supplemented with an optional level of nesting to represent subordinate clauses (i.e. embedded clauses). Determiners were not included, because they are hard to translate and it is difficult to reliably distinguish and recognise them. This way, translation rules became a lot simpler, because only a list of feature-value pairs had to be mapped to another list of pairs. The language turned out to be underconstrained. Adding natural sortal constraints to the grammar solved this problem, but also returned the language to a more expressive formalism. The newly created AFF combines elements of QLF and the feature-value list semantics. This version of flat semantics is enhanced with additional functional markings. This together with a relatively small vocabulary solved the ambiguity problem of the original flat representation language without creating overly complex rules. In addition, the syntactic structures are treated carefully by a compromise of linguistic and engineering traditions. The grammars are in fact retrieved from linguistically motivated resource, using corpus-based methods. They are driven by small sets of examples. This results in simpler and flatter domain-specific grammars. The semantics are less sophisticated and represent a minimal approach in the engineering tradition. Each lexical item contributes a set of feature-value pairs. This leads to simple-to-write translation rules. There are only lists of features-value pairs to map to other feature-value pairs. However, as a result the machine translation channel model becomes underspecified and is weakened, whereas the target language model is strengthened. An intelligent help module is integrated into the system to support users in utilising the full coverage of the grammars. This tool provides the user with examples as close as possible to the users original utterance. The output is based on a library. Each sub-domain and language pair has its own library. The contents are extracted from the combined interlingua corpora. The help module scans the corpus for the tagged source language form mapped with the corresponding target language form. Additionally a second statistical recogniser is used as backup. The results are used to select similar examples from the library. According to the generation preferences, one of the derived strings is picked and the target language string is realised as spoken language. Some statistical corpus based meth
Behavior informatics
Behavior informatics (BI) is the informatics of behaviors so as to obtain behavior intelligence and behavior insights. BI is a research method combining science and technology, specifically in the area of engineering. The purpose of BI includes analysis of current behaviors as well as the inference of future possible behaviors. This occurs through pattern recognition. Different from applied behavior analysis from the psychological perspective, BI builds computational theories, systems and tools to qualitatively and quantitatively model, represent, analyze, and manage behaviors of individuals, groups and/or organizations. BI is built on classic study of behavioral science, including behavior modeling, applied behavior analysis, behavior analysis, behavioral economics, and organizational behavior. Typical BI tasks consist of individual and group behavior formation, representation, computational modeling, analysis, learning, simulation, and understanding of behavior impact, utility, non-occurring behaviors, etc. for behavior intervention and management. The Behavior Informatics approach to data utilizes cognitive as well as behavioral data. By combining the data, BI has the potential to effectively illustrate the big picture when it comes to behavioral decisions and patterns. One of the goals of BI is also to be able to study human behavior while eliminating issues like self-report bias. This creates more reliable and valid information for research studies. == Behavior == From an Informatics perspective, a behavior consists of three key elements: actors (behavioral subjects and objects), operations (actions, activities) and interactions (relationships), and their properties. A behavior can be represented as a behavior vector, all behaviors of an actor or an actor group can be represented as behavior sequences and multi-dimensional behavior matrix. The following table explains some of the elements of behavior. Behavior Informatics takes into account behavior when analyzing business patterns and intelligence. The inclusion of behavior in these analyses provides prominent information on social and driving factors of patterns. == Applications == Behavior Informatics is being used in a variety of settings, including but not limited to health care management, telecommunications, marketing, and security. Behavior Informatics provides a manner in which to analyze and organize the many aspects that go into a person's health care needs and decisions. When it comes to business models, behavior informatics may be utilized for a similar role. Organizations implement behavior informatics to enhance business structure and regime, where it helps moderate ideal business decisions and situations.
Interlingual machine translation
Interlingual machine translation is one of the classic approaches to machine translation. In this approach, the source language, i.e. the text to be translated is transformed into an interlingua, i.e., an abstract language-independent representation. The target language is then generated from the interlingua. Within the rule-based machine translation paradigm, the interlingual approach is an alternative to the direct approach and the transfer approach. In the direct approach, words are translated directly without passing through an additional representation. In the transfer approach the source language is transformed into an abstract, less language-specific representation. Linguistic rules which are specific to the language pair then transform the source language representation into an abstract target language representation and from this the target sentence is generated. The interlingual approach to machine translation has advantages and disadvantages. The advantages are that it requires fewer components in order to relate each source language to each target language, it takes fewer components to add a new language, it supports paraphrases of the input in the original language, it allows both the analysers and generators to be written by monolingual system developers, and it handles languages that are very different from each other (e.g. English and Arabic). The obvious disadvantage is that the definition of an interlingua is difficult and maybe even impossible for a wider domain. The ideal context for interlingual machine translation is thus multilingual machine translation in a very specific domain. For example, Interlingua has been used as a pivot language in international conferences and has been proposed as a pivot language for the European Union. == History == The first ideas about interlingual machine translation appeared in the 17th century with Descartes and Leibniz, who came up with theories of how to create dictionaries using universal numerical codes, not unlike numerical tokens used by large language models nowadays. Others, such as Cave Beck, Athanasius Kircher and Johann Joachim Becher worked on developing an unambiguous universal language based on the principles of logic and iconographs. In 1668, John Wilkins described his interlingua in his "Essay towards a Real Character and a Philosophical Language". In the 18th and 19th centuries many proposals for "universal" international languages were developed, the most well known being Esperanto. That said, applying the idea of a universal language to machine translation did not appear in any of the first significant approaches. Instead, work started on pairs of languages. However, during the 1950s and 60s, researchers in Cambridge headed by Margaret Masterman, in Leningrad headed by Nikolai Andreev and in Milan by Silvio Ceccato started work in this area. The idea was discussed extensively by the Israeli philosopher Yehoshua Bar-Hillel in 1969. During the 1970s, noteworthy research was done in Grenoble by researchers attempting to translate physics and mathematical texts from Russian to French, and in Texas a similar project (METAL) was ongoing for Russian to English. Early interlingual MT systems were also built at Stanford in the 1970s by Roger Schank and Yorick Wilks; the former became the basis of a commercial system for the transfer of funds, and the latter's code is preserved at The Computer Museum at Boston as the first interlingual machine translation system. In the 1980s, renewed relevance was given to interlingua-based, and knowledge-based approaches to machine translation in general, with much research going on in the field. The uniting factor in this research was that high-quality translation required abandoning the idea of requiring total comprehension of the text. Instead, the translation should be based on linguistic knowledge and the specific domain in which the system would be used. The most important research of this era was done in distributed language translation (DLT) in Utrecht, which worked with a modified version of Esperanto, and the Fujitsu system in Japan. In 2016, Google Neural Machine Translation achieved "zero-shot translation", that is it directly translates one language into another. For example, it might be trained just for Japanese-English and Korean-English translation, but can perform Japanese-Korean translation. The system appears to have learned to produce a language-independent intermediate representation of language (an "interlingua"), which allows it to perform zero-shot translation by converting from and to the interlingua. == Outline == In this method of translation, the interlingua can be thought of as a way of describing the analysis of a text written in a source language such that it is possible to convert its morphological, syntactic, semantic (and even pragmatic) characteristics, that is "meaning" into a target language. This interlingua is able to describe all of the characteristics of all of the languages which are to be translated, instead of simply translating from one language to another. Sometimes two interlinguas are used in translation. It is possible that one of the two covers more of the characteristics of the source language, and the other possess more of the characteristics of the target language. The translation then proceeds by converting sentences from the first language into sentences closer to the target language through two stages. The system may also be set up such that the second interlingua uses a more specific vocabulary that is closer, or more aligned with the target language, and this could improve the translation quality. The above-mentioned system is based on the idea of using linguistic proximity to improve the translation quality from a text in one original language to many other structurally similar languages from only one original analysis. This principle is also used in pivot machine translation, where a natural language is used as a "bridge" between two more distant languages. For example, in the case of translating to English from Ukrainian using Russian as an intermediate language. == Translation process == In interlingual machine translation systems, there are two monolingual components: the analysis of the source language and the interlingual, and the generation of the interlingua and the target language. It is however necessary to distinguish between interlingual systems using only syntactic methods (for example the systems developed in the 1970s at the universities of Grenoble and Texas) and those based on artificial intelligence (from 1987 in Japan and the research at the universities of Southern California and Carnegie Mellon). The first type of system corresponds to that outlined in Figure 1. while the other types would be approximated by the diagram in Figure 4. The following resources are necessary to an interlingual machine translation system: Dictionaries (or lexicons) for analysis and generation (specific to the domain and the languages involved). A conceptual lexicon (specific to the domain), which is the knowledge base about events and entities known in the domain. A set of projection rules (specific to the domain and the languages). Grammars for the analysis and generation of the languages involved. One of the problems of knowledge-based machine translation systems is that it becomes impossible to create databases for domains larger than very specific areas. Another is that processing these databases is very computationally expensive. == Efficacy == One of the main advantages of this strategy is that it provides an economical way to make multilingual translation systems. With an interlingua it becomes unnecessary to make a translation pair between each pair of languages in the system. So instead of creating n ( n − 1 ) {\displaystyle n(n-1)} language pairs, where n {\displaystyle n} is the number of languages in the system, it is only necessary to make 2 n {\displaystyle 2n} pairs between the n {\displaystyle n} languages and the interlingua. The main disadvantage of this strategy is the difficulty of creating an adequate interlingua. It should be both abstract and independent of the source and target languages. The more languages added to the translation system, and the more different they are, the more potent the interlingua must be to express all possible translation directions. Another problem is that it is difficult to extract meaning from texts in the original languages to create the intermediate representation. == Existing interlingual machine translation systems == Calliope-Aero Carabao Linguistic Virtual Machine Grammatical Framework Number Translator Google Translate use English internally as a pivot language for some language pairs such as Chinese and Japanese, and more generally those with "higher quality" neural-network translators with English but not between each other.