Automatic summarization is the process of shortening a set of data computationally, to create a subset (a summary) that represents the most important or relevant information within the original content. Artificial intelligence (AI) algorithms are commonly developed and employed to achieve this, specialized for different types of data. Text summarization is usually implemented by natural language processing methods, designed to locate the most informative sentences in a given document. On the other hand, visual content can be summarized using computer vision algorithms. Image summarization is the subject of ongoing research; existing approaches typically attempt to display the most representative images from a given image collection, or generate a video that only includes the most important content from the entire collection. Video summarization algorithms identify and extract from the original video content the most important frames (key-frames), and/or the most important video segments (key-shots), normally in a temporally ordered fashion. Video summaries simply retain a carefully selected subset of the original video frames and, therefore, are not identical to the output of video synopsis algorithms, where new video frames are being synthesized based on the original video content. == Commercial products == In 2022 Google Docs released an automatic summarization feature. == Approaches == There are two general approaches to automatic summarization: extraction and abstraction. === Extraction-based summarization === Here, content is extracted from the original data, but the extracted content is not modified in any way. Examples of extracted content include key-phrases that can be used to "tag" or index a text document, or key sentences (including headings) that collectively comprise an abstract, and representative images or video segments, as stated above. For text, extraction is analogous to the process of skimming, where the summary (if available), headings and subheadings, figures, the first and last paragraphs of a section, and optionally the first and last sentences in a paragraph are read before one chooses to read the entire document in detail. Other examples of extraction that include key sequences of text in terms of clinical relevance (including patient/problem, intervention, and outcome). === Abstractive-based summarization === Abstractive summarization methods generate new text that did not exist in the original text. This has been applied mainly for text. Abstractive methods build an internal semantic representation of the original content (often called a language model), and then use this representation to create a summary that is closer to what a human might express. Abstraction may transform the extracted content by paraphrasing sections of the source document, to condense a text more strongly than extraction. Such transformation, however, is computationally much more challenging than extraction, involving both natural language processing and often a deep understanding of the domain of the original text in cases where the original document relates to a special field of knowledge. "Paraphrasing" is even more difficult to apply to images and videos, which is why most summarization systems are extractive. === Aided summarization === Approaches aimed at higher summarization quality rely on combined software and human effort. In Machine Aided Human Summarization, extractive techniques highlight candidate passages for inclusion (to which the human adds or removes text). In Human Aided Machine Summarization, a human post-processes software output, in the same way that one edits the output of automatic translation by Google Translate. == Applications and systems for summarization == There are broadly two types of extractive summarization tasks depending on what the summarization program focuses on. The first is generic summarization, which focuses on obtaining a generic summary or abstract of the collection (whether documents, or sets of images, or videos, news stories etc.). The second is query relevant summarization, sometimes called query-based summarization, which summarizes objects specific to a query. Summarization systems are able to create both query relevant text summaries and generic machine-generated summaries depending on what the user needs. An example of a summarization problem is document summarization, which attempts to automatically produce an abstract from a given document. Sometimes one might be interested in generating a summary from a single source document, while others can use multiple source documents (for example, a cluster of articles on the same topic). This problem is called multi-document summarization. A related application is summarizing news articles. Imagine a system, which automatically pulls together news articles on a given topic (from the web), and concisely represents the latest news as a summary. Image collection summarization is another application example of automatic summarization. It consists in selecting a representative set of images from a larger set of images. A summary in this context is useful to show the most representative images of results in an image collection exploration system. Video summarization is a related domain, where the system automatically creates a trailer of a long video. This also has applications in consumer or personal videos, where one might want to skip the boring or repetitive actions. Similarly, in surveillance videos, one would want to extract important and suspicious activity, while ignoring all the boring and redundant frames captured. At a very high level, summarization algorithms try to find subsets of objects (like set of sentences, or a set of images), which cover information of the entire set. This is also called the core-set. These algorithms model notions like diversity, coverage, information and representativeness of the summary. Query based summarization techniques, additionally model for relevance of the summary with the query. Some techniques and algorithms which naturally model summarization problems are TextRank and PageRank, Submodular set function, Determinantal point process, maximal marginal relevance (MMR) etc. === Keyphrase extraction === The task is the following. You are given a piece of text, such as a journal article, and you must produce a list of keywords or key[phrase]s that capture the primary topics discussed in the text. In the case of research articles, many authors provide manually assigned keywords, but most text lacks pre-existing keyphrases. For example, news articles rarely have keyphrases attached, but it would be useful to be able to automatically do so for a number of applications discussed below. Consider the example text from a news article: "The Army Corps of Engineers, rushing to meet President Bush's promise to protect New Orleans by the start of the 2006 hurricane season, installed defective flood-control pumps last year despite warnings from its own expert that the equipment would fail during a storm, according to documents obtained by The Associated Press". A keyphrase extractor might select "Army Corps of Engineers", "President Bush", "New Orleans", and "defective flood-control pumps" as keyphrases. These are pulled directly from the text. In contrast, an abstractive keyphrase system would somehow internalize the content and generate keyphrases that do not appear in the text, but more closely resemble what a human might produce, such as "political negligence" or "inadequate protection from floods". Abstraction requires a deep understanding of the text, which makes it difficult for a computer system. Keyphrases have many applications. They can enable document browsing by providing a short summary, improve information retrieval (if documents have keyphrases assigned, a user could search by keyphrase to produce more reliable hits than a full-text search), and be employed in generating index entries for a large text corpus. Depending on the different literature and the definition of key terms, words or phrases, keyword extraction is a highly related theme. ==== Supervised learning approaches ==== Beginning with the work of Turney, many researchers have approached keyphrase extraction as a supervised machine learning problem. Given a document, we construct an example for each unigram, bigram, and trigram found in the text (though other text units are also possible, as discussed below). We then compute various features describing each example (e.g., does the phrase begin with an upper-case letter?). We assume there are known keyphrases available for a set of training documents. Using the known keyphrases, we can assign positive or negative labels to the examples. Then we learn a classifier that can discriminate between positive and negative examples as a function of the features. Some classifiers make a binary classification for a test example, while others assign a probability of being a keyphrase. For ins
Elastix (image registration)
Elastix is an image registration toolbox built upon the Insight Segmentation and Registration Toolkit (ITK). It is entirely open-source and provides a wide range of algorithms employed in image registration problems. Its components are designed to be modular to ease a fast and reliable creation of various registration pipelines tailored for case-specific applications. It was first developed by Stefan Klein and Marius Staring under the supervision of Josien P.W. Pluim at Image Sciences Institute (ISI). Its first version was command-line based, allowing the final user to employ scripts to automatically process big data-sets and deploy multiple registration pipelines with few lines of code. Nowadays, to further widen its audience, a version called SimpleElastix is also available, developed by Kasper Marstal, which allows the integration of elastix with high level languages, such as Python, Java, and R. == Image registration fundamentals == Image registration is a well-known technique in digital image processing that searches for the geometric transformation that, applied to a moving image, obtains a one-to-one map with a target image. Generally, the images acquired from different sensors (multimodal), time instants (multitemporal), and points of view (multiview) should be correctly aligned to proceed with further processing and feature extraction. Even though there are a plethora of different approaches to image registration, the majority is composed of the same macro building blocks, namely the transformation, the interpolator, the metric, and the optimizer. Registering two or more images can be framed as an optimization problem that requires multiple iterations to converge to the best solution. Starting from an initial transformation computed from the image moments the optimization process searches for the best transformation parameters based on the value of the selected similarity metric. The figure on the right shows the high-level representation of the registration of two images, where the reference remains constant during the entire process, while the moving one will be transformed according to the transformation parameters. In other words, the registration ends when the similarity metric, which is a mathematical function with a certain number of parameters to be optimized, reaches the optimal value which is highly dependent on the specific application. == Main building blocks == Following the structure of the image registration workflow, the elastix toolbox proposes a modular solution that implements for each of the building blocks different algorithms, highly employed in medical image registration, and helps the final users to build their specific pipeline by selecting the most suitable algorithm for each of the main building blocks. Each block is easily configurable both by selecting pre-defined initialization values or by trying multiple sets of parameters and then choosing the most performing one. The registration is performed on images, and the elastix toolbox supports all the data formats supported by ITK, ranging from JPEG and PNG to medical standard formats such as DICOM and NIFTI. It also stores physical pixel spacing, the origin and the relative position to an external world reference system, when provided in the metadata, to facilitate the registration process, especially in medical field applications. === Transformation === The transformation is an essential building block, since it defines the allowable transformations. In image registration, the main distinction can be done between parallel-to-parallel and parallel-to-non parallel (deformable) line mapping transformations. In the elastix toolbox, the final users can select one transformation or compose more transformations either through addition or via composition. Below are reported the different transformation models in order of increasing flexibility, along with the corresponding elastix class names between brackets. Translation (TranslationTransform) allows only translations Rigid (EulerTransform) expands the translation adding rotations and the object is seen as a rigid body Similarity (SimilarityTransform) expands the rigid transformation by introducing isotropic scaling Affine (AffineTransform) expands the rigid transformation allowing both scaling and shear B-splines (BSplineTransform) is a deformable transformation usually preceded by a rigid or affine one Thin-plate splines (SplineKernelTransform) is a deformable transformation belonging to the class of kernel-based transformations that is a composition of and affine and a non-rigid part === Metric === The similarity metric is the mathematical function whose parameters should be optimized to reach the desired registration, and, during the process, it is computed multiple times. Below are reported the available metrics computed employing the reference and the transformed images and the corresponding elastix class names between brackets. Mean squared difference (AdvancedMeanSquares) to be used for mono-modal applications Normalized correlation coefficient (AdvancedNormalizedCorrelation) to be used for images that have an intensity linear relationship Mutual information (AdvancedMattesMutualInformation) to be used for both mono- and multi-modal applications and optimized to reach better performance compared to the normalized version Normalized mutual information (NormalizedMutualInformation) for both mono- and multi-modal applications Kappa statistic (AdvancedKappaStatistic) to be used only for binary images === Sampler === For the computation of the similarity metrics, it is not always necessary to consider all the voxels and, sometimes, it can be useful to use only a fraction of the voxels of the images, i.e. to reduce the execution time for big input images. Below are reported the available criteria for selecting a fraction of the voxels for the similarity metric computation and the corresponding elastix class names between brackets. Full (Full) to employ all the voxels Grid (Grid) to employ a regular grid defined by the user to downsample the image Random (Random) to randomly select a percentage of voxels defined by the users (all voxels have equal probability to be selected) Random coordinate (RandomCoordinate) like the random criterion, but in this case also off-grid positions can be selected to simplify the optimization process === Interpolator === After the application of the transformation, it may occur that the voxels used for the similarity metric computation are at non-voxel positions, so intensity interpolation should be performed to ensure the correctness of the computed values. Below are reported the implemented interpolators and the corresponding elastix class names between brackets. Nearest neighbor (NearestNeighborInterpolator) exploits little resources, but gives low quality results Linear (LinearInterpolator) is sufficient in general applications N-th order B-spline (BSplineInterpolator) can be used to increase the order N, increasing quality and computation time. N=0 and N=1 indicate the nearest neighbor and linear cases respectively. === Optimizer === The optimizer defines the strategy employed for searching the best transformation parameter to reach the correct registration, and it is commonly an iterative strategy. Below are reported some of the implemented optimization strategies. Gradient descent Robbins-Monro, similar to the gradient descent, but employing an approximation of the cost function derivatives A wider range of optimizers is also available, such as Quasi-Newton or evolutionary strategies. === Other features === The elastix software also offers other features that can be employed to speed up the registration procedure and to provide more advanced algorithms to the end-users. Some examples are the introduction of blur and Gaussian pyramid to reduce data complexity, and multi-image and multi-metric framework to deal with more complex applications. == Applications == Elastix has applications mainly in the medical field, where image registration is fundamental to get comprehensive information regarding the analysed anatomical region. It is widely employed in image-guided surgery, tumour monitoring, and treatment assessment. For example, in radiotherapy planning, image registration allows to correctly deliver the treatment and evaluate the obtained results. Thanks to the wide range of implemented algorithms, the use of the elastix software allows physicians and researchers to test different registration pipelines from the simplest to more complex ones, and to save the best one as a configuration file. This file and the fact that the software is completely open-source makes it easy to reproduce the work, that can help supporting the open science paradigm, and allows fast reuse on different patients data. In image-guided surgery, registration time and accuracy are critical points, considering that, during the registration, the patient is on the operating table, and the imag
OpenWSN
OpenWSN aims to build an open standard-based and open source implementation of a complete constrained network protocol stack for wireless sensor networks and Internet of Things. The project was created at the University of California Berkeley and extended at the INRIA and at the Open University of Catalonia (UOC). The root of OpenWSN is a deterministic MAC layer implementing the IEEE 802.15.4e TSCH based on the concept of Time Slotted Channel Hopping (TSCH). Above the MAC layer, the Low Power Lossy Network stack is based on IETF standards including the IETF 6TiSCH management and adaptation layer (a minimal configuration profile, 6top protocol and different scheduling functions). The stack is complemented by an implementation of 6LoWPAN, RPL in non-storing mode, UDP and CoAP, enabling access to devices running the stack from the native IPv6 through open standards. OpenWSN is related to other projects including the following: RIOT OpenMote OpenWSN is available for Linux, Windows and OS X platforms. Current release of OpenWSN is 1.14.0.
The Algorithm Auction
The Algorithm Auction is the world's first auction of computer algorithms. Created by Ruse Laboratories, the initial auction featured seven lots and was held at the Cooper Hewitt, Smithsonian Design Museum on March 27, 2015. Five lots were physical representations of famous code or algorithms, including a signed, handwritten copy of the original Hello, World! C program by its creator Brian Kernighan on dot-matrix printer paper, a printed copy of 5,000 lines of Assembly code comprising the earliest known version of Turtle Graphics, signed by its creator Hal Abelson, a necktie containing the six-line qrpff algorithm capable of decrypting content on a commercially produced DVD video disc, and a pair of drawings representing OkCupid's original Compatibility Calculation algorithm, signed by the company founders. The qrpff lot sold for $2,500. Two other lots were “living algorithms,” including a set of JavaScript tools for building applications that are accessible to the visually impaired and the other is for a program that converts lines of software code into music. Winning bidders received, along with artifacts related to the algorithms, a full intellectual property license to use, modify, or open-source the code. All lots were sold, with Hello World receiving the most bids. Exhibited alongside the auction lots were a facsimile of the Plimpton 322 tablet on loan from Columbia University, and Nigella, an art-world facing computer virus named after Nigella Lawson and created by cypherpunk and hacktivist Richard Jones. Sebastian Chan, Director of Digital & Emerging Media at the Cooper–Hewitt, attended the event remotely from Milan, Italy via a Beam Pro telepresence robot. == Effects == Following the auction, the Museum of Modern Art held a salon titled The Way of the Algorithm highlighting algorithms as "a ubiquitous and indispensable component of our lives."
Ontology alignment
Ontology alignment, or ontology matching, is the process of determining correspondences between concepts in ontologies. A set of correspondences is also called an alignment. The phrase takes on a slightly different meaning, in computer science, cognitive science or philosophy. == Computer science == For computer scientists, concepts are expressed as labels for data. Historically, the need for ontology alignment arose out of the need to integrate heterogeneous databases, ones developed independently and thus each having their own data vocabulary. In the Semantic Web context involving many actors providing their own ontologies, ontology matching has taken a critical place for helping heterogeneous resources to interoperate. Ontology alignment tools find classes of data that are semantically equivalent, for example, "truck" and "lorry". The classes are not necessarily logically identical. According to Euzenat and Shvaiko (2007), there are three major dimensions for similarity: syntactic, external, and semantic. Coincidentally, they roughly correspond to the dimensions identified by Cognitive Scientists below. A number of tools and frameworks have been developed for aligning ontologies, some with inspiration from Cognitive Science and some independently. Ontology alignment tools have generally been developed to operate on database schemas, XML schemas, taxonomies, formal languages, entity-relationship models, dictionaries, and other label frameworks. They are usually converted to a graph representation before being matched. Since the emergence of the Semantic Web, such graphs can be represented in the Resource Description Framework line of languages by triples of the form
Robot Monk Xian'er
Robot Monk Xian'er (Chinese: 贤二机器僧) is a humanoid robot based on the cartoon character Xian'er. It was developed by a team of monks, volunteers and AI experts from Beijing Longquan Monastery in Beijing, China. He can follow human instructions to make body movements, read scriptures and play Buddhist music. He can chat and respond to people's emotional and spiritual questions with Buddhist wisdom. As a chatbot, Robot Monk Xian'er is available on certain public platforms including WeChat and Facebook. Over the years, master Xuecheng, the abbot of Beijing Longquan Monastery, replied to thousands of questions on Sina Weibo. These questions and their answers become the data source of the chatbot.
Library history
Library history is a subdiscipline within library science and library and information science focusing on the history of libraries and their role in societies and cultures. Some see the field as a subset of information history. Library history is an academic discipline and should not be confused with its object of study (history of libraries): the discipline is much younger than the libraries it studies. Library history begins in ancient societies through contemporary issues facing libraries today. Topics include recording mediums, cataloguing systems, scholars, scribes, library supporters and librarians. == Earliest libraries == The earliest records of a library institution as it is presently understood can be dated back to around 5,000 years ago in the Southwest Asian regions of the world. One of the oldest libraries found is that of the ancient library at Ebla (circa 2500 BCE) in present-day Syria. In the 1970s, the excavation at Ebla's library unearthed over 20,000 clay tablets written in cuneiform script. === Library in Mesopotamia === The Assyrian King Assurbanipal created one of the greatest libraries in Nineveh in the seventh century BCE. The collection consisted of over 30,000 tablets written in a variety of languages. The collection was cataloged both by the shape of the tablet and by the subject of the content. The library had separate rooms for the different topics: government, history, law, astronomy, geography, and so on. The tablets also contained myths, hymns, and even jokes. Assurbanipal would send scribes to visit every corner of his kingdom to copy the content of other libraries. His library contained many of the most important literary works of the day, including the epic of Gilgamesh. Assurbanipal's Royal Library also had one of the first library catalogs. Unfortunately, Nineveh was eventually destroyed, and the library was lost in a fire. === Libraries in Ancient Greece === The Greek government was the first to sponsor public libraries. By 500 BCE both Athens and Samos had begun creating libraries for the public, though as most of the population was illiterate these spaces were serving a small, educated portion of the community. Athens developed a city archive at the Metroon in 405 BCE, where documents were stored in sealed jars. These would have saved the documents, but they would have been difficult to consult regularly. In Paros, around the same time, contracts were placed in the temple for safe keeping, and a book curse was placed for extra protection. === Library of Alexandria === The Library at Alexandria, Egypt, was renowned in the third century BCE while kings Ptolemy I Soter and Ptolemy II Philadelphus reigned. The library included a museum, garden, meeting areas and of course reading rooms. The Great Library, as it is known, was one of many in Alexandria. From its inception around the second century BCE, Alexandria was a well-known center for learning. It earned renown as the intellectual capital of the Western world up through the third century CE. The librarians at Alexandria collected, copied, and organized scrolls from across the known world. According to a primary source, every ship that came to Alexandria was required to hand over their books to be copied, and the copies would be returned to the owner, the library keeping the original. The Library of Alexandria was damaged by various disasters over time, including fire, invasion, and earthquake. Scholars believe the collection slowly diminished over time due to theft and efforts to remove it ahead of invading armies. While there are popular stories about how the library was ultimately destroyed, most of these are more myth than fact. === Libraries in Rome === Julius Caesar and his successor Augustus were the first to establish public libraries in ancient Rome, including the library of Apollo on the Palatine Hill. Several emperors followed suit over the next four centuries, including Hadrian, Tiberius, and Vespasian. Roman aristocrats also had personal libraries, which usually contained works in both Greek and Latin. A valuable example of this has been found at Herculaneum near Pompeii. Papyrus manuscripts in Herculaneum's Villa of the Papyri were encased in ash after the eruption of Vesuvius in 79 CE. Modern archaeology is now able to scan these artifacts and discern their contents, including many writings from Philodemus. The average Roman would not have been familiar with books beyond what they might hear read aloud in the forum. Public figures would pay for particular passages to be read aloud to the public from the steps of a public library. === Libraries in the Middle Ages === In the European Middle Ages, libraries began to become more prevalent, despite a widespread reduction in new writing beyond religious themes. Most libraries were initially connected to monasteries or religious institutions. Scriptoriums copied Christian religious texts to share with other religious centers or to be read aloud to their own parishioners. The Holy Roman Emperor Charlemagne (r. 786-814) had a large impact on the advancement of written culture in the Medieval Christian world, acquiring as many written works as he could, and employing many scribes to copy and recirculate vernacular versions of religious works. Most of the text held in small personal libraries was still religious in nature. == Early modern libraries == === Libraries of the Renaissance === During the Renaissance era the merchant middle class grew, and more people found benefits in education. They relied on libraries as a place to study and gain knowledge. Libraries provided a valuable resource, enriching the culture of those who were educated. Universities that had been started in the Middle Ages, founded their own libraries. Books in these libraries could not be borrowed from these libraries and were generally chained to the shelves to prevent theft. As more of the population became literate, new ideas like Humanism and Natural Law spawned an increase personal libraries, although they remained small. Gutenberg's invention of the printing press in 1456 opened the door to the modern era for libraries. == Oldest working libraries == According to the German librarian Michael Knoche, it is not possible to determine which library is the “oldest”: "Precise year dates are a construct, especially in the case of very old libraries. When a collection of books deserves to be called a library depends very much on the point of view of the observer." Various libraries are referred to as the “oldest”: The library founded in the 6th century of the Saint Catherine's Monastery in Sinai is "reputedly the oldest continuously run library in existence today", according to the Library of Congress. Its collection of religious and secular manuscripts is ranging from Bibles, liturgies and prayer books to legal documents such as deeds, court cases and fatwahs (legal opinions). The Al Qarawiyyin Library was founded in 859 by Fatima al-Fihri and is often regarded as the oldest working library in the world. It is in Fez, Morocco and is part of the oldest continually operating university in the world, the University of al-Qarawiyyin. The library houses approximately 4,000 ancient Islamic manuscripts. These manuscripts include 9th century Qurans and the oldest known accounts of the Islamic prophet Muhammed. The Malatestiana Library (Italian: Biblioteca Malatestiana) is a public library in the city of Cesena in northern Italy. Opened in 1454 it is significant for being the first civic library in Europe open to the general public. == Library history reports and writings of the early 19th and 20th century == In the early 19th and 20th century, representative titles were created reporting library history in the United States and the United Kingdom. American titles include Public Libraries in the United States of America, Their History, Condition, and Management (1876), Memorial History of Boston (1881) by Justin Winsor, Public Libraries in America (1894) by William I. Fletcher, and History of the New York Public Library (1923) by Henry M. Lydenberg. British titles include Old English Libraries (1911) by Earnest A. Savage and The Chained Library: A Survey of Four Centuries in the Evolution of the English Library by Burnett Hillman Streeter. In the beginning of the 20th century, library historians began applying scientific research methodologies to examine the library as a social agency. Two works that demonstrate this argument are Geschichte der Bibliotheken (1925) by Alfred Hessel and the Library Quarterly article from 1931, “The Sociological Beginnings of the Library Movement in America” by Arnold Borden. With the establishment of library schools, master's theses and doctoral dissertations represented the shift in serious research regarding libraries and library history. Two published doctoral dissertations that mark this trend are Foundations of the Public Library: The Origins of the American Public Library Movement in Ne