AI Data Center Zoning

AI Data Center Zoning — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

• 

  Collabora Online

  Collabora Online (often abbreviated as COOL) is an open-source online office suite developed by Collabora, based on LibreOffice Online, the web-based edition of the LibreOffice office suite. It enables real-time collaborative editing of documents, spreadsheets, presentations, and vector graphics in a web browser. Optional applications are available for offline use on Android, ChromeOS, iOS, iPadOS, Linux distributions, macOS, and Windows. It supports the OpenDocument format and is compatible with other major formats, including those used by Microsoft Office. The Document Foundation (TDF), the nonprofit organization behind LibreOffice, states that a majority of the LibreOffice software development is done by its partners like Collabora. Collabora Online is an open-source alternative to proprietary cloud office platforms such as Google Workspace and Microsoft 365. Unlike these services, it can be self-hosted or hosted by third-party providers. The platform is marketed particularly toward enterprises and public institutions seeking greater digital sovereignty and independence from U.S.-based "big tech" companies. Collabora also develops Collabora Office, a standalone desktop and mobile app suite based on LibreOffice. Although Collabora Online has increasingly taken on a central role, both products may be used in parallel, similar to Microsoft Office and Microsoft 365. In November 2025, Collabora released Collabora Office Desktop and renamed the previous product Collabora Office Classic. The new product shares code with Collabora Online and brings the same user interface to the desktop on Linux, Windows and MacOS. A separate version, the Collabora Online Development Edition (CODE), is offered free of charge and is recommended for individuals, small teams, and developers. CODE provides early access to new features and serves as a testing and development platform for open-source community contributors. As TDF does not offer a free version of LibreOffice Online, CODE represents the primary freely available option for organizations and individuals interested in deploying LibreOffice in a web-based, collaborative setting. == Applications == Collabora Online includes several applications for document editing, available through the web-based interface and optional desktop and mobile apps: Collabora Writer – A word processor based on LibreOffice Writer, comparable to Microsoft Word and Google Docs. It supports WYSIWYG editing, styles, formatting tools, comment threads, and change tracking. Collabora Calc – A spreadsheet editor based on LibreOffice Calc, similar to Microsoft Excel and Google Sheets. Features include pivot tables, formulas, data validation, conditional formatting, advanced sorting and filtering, charts, and support for up to 16,000 columns. Compatible with some macros written in VBA. Collabora Impress – A presentation program based on LibreOffice Impress, comparable to Microsoft PowerPoint and Google Slides. It supports master slides, transitions, speaker notes, and multimedia elements. Collabora Draw is not a separate application, most of the functionality of the Draw application is now integrated in Writer and Impress – vector graphics editor based on LibreOffice Draw, comparable to Microsoft Visio and Google Drawings. == Features == Collabora Online can be accessed from modern web browsers without the need for plug-ins or add-ons. It supports real-time collaborative editing of word processing documents, spreadsheets, presentations, and vector graphics. Collaboration features include commenting, version tracking with document comparison and restoration, and integration with communication tools such as chat or video calls. These functions are often enabled through integration with enterprise open-source cloud platforms like Nextcloud, ownCloud, Seafile, EGroupware, GroupOffice and others. Collabora Online can also be embedded or integrated into a variety of third-party applications. Although client apps are not required to use the web-based suite, optional applications are available for offline use on Android, ChromeOS, iOS, iPadOS, Linux distributions, macOS, and Windows. These apps share the same LibreOffice-based core as the server version, ensuring document compatibility across platforms. Development of the LibreOffice core benefits both the online server and the client applications simultaneously. The mobile apps offer touch-optimized interfaces that adapt to different screen sizes and can be used offline, with optional integration into cloud storage services. Collabora Online supports OpenDocument formats (ODF; .odt, .odp, .ods, .odg) in accordance with ISO/IEC 26300. It is also compatible with Microsoft Office formats, including Office Open XML (.docx, .pptx, .xlsx) and legacy binary formats (.doc, .ppt, .xls). Additional supported formats include PDF, PNG, CSV, TSV, RTF, EPUB, and others. The suite can import a range of formats supported by LibreOffice, including Microsoft Visio and Publisher files, Apple Keynote, Numbers, and Pages files, as well as legacy formats used by Lotus 1-2-3, Microsoft Works, and Quattro Pro. The core of Collabora Online is written in C++ and utilizes LibreOfficeKit, a programming interface that enables reuse of much of LibreOffice's existing code for document saving, loading, and rendering. Collabora Online operates on the principle that documents remain on the server, with users viewing tile-rendered images of the document and sending their edits back to the server. The user interface is implemented in JavaScript. For file access and authentication with file hosting services, Collabora Online uses Microsoft's WOPI protocol, allowing compatibility with any service supporting Microsoft 365 integration. == Server == The server component can be self-hosted or deployed through third-party enterprise open-source cloud platforms, allowing organizations to maintain control over data and infrastructure. It is available for various Linux distributions and as a Docker image. The server enables features such as in-browser document editing, file synchronization, and real-time communication. These third-party cloud platforms typically offer additional functionality comparable to services such as Dropbox, Google Workspace, Microsoft 365, or Zoom, including file sharing, calendars, email, contacts, chat, and video conferencing. Collabora Online can be integrated into these applications, as well as with other services such as learning management systems and enterprise content platforms, through open APIs and an SDK. == Reception == Various online and print publications have discussed Collabora Online. In December 2016 the technology website Softpedia mentioned the availability of collaborative editing in version 2.0 and the integration with ownCloud, Nextcloud, and other file synchronization and sharing solutions. In June 2020, ZDNET reported that Collabora Online would be included as the standard office suite in Nextcloud version 19, noting that direct document editing was added to the native video conferencing software Talk. The technology blog OMG! Ubuntu! covered the release of Collabora's Android and iOS apps, emphasizing their offline functionality. In September 2020, Linux Magazine compared Collabora Online with OnlyOffice, noting the flexibility and platform independence of both tools and highlighting Collabora's extensive feature set derived from LibreOffice. === Digital sovereignty === Collabora Online's open-source design and support for self-hosting have made it notable in discussions about digital sovereignty—the ability of users and organizations to control their own data. This is particularly relevant in Europe, where concerns about dependence on U.S.-based "big tech" companies and data privacy have grown in recent years. On 10th June 2025, Microsoft executives under oath in the French Senate admitted that they cannot guarantee data sovereignty and would be compelled to pass French (and by implication the wider European Union) information to the US administration if requested via a warrant or subpoena. The Cloud Act is a law that gives the US government authority to obtain digital data held by US-based tech corporations, irrespective of whether that data is stored on servers at home or on foreign soil. A 2020 briefing by the European Parliament highlighted risks associated with reliance on major technology companies that collect and exploit user data. Legal decisions such as the Schrems II ruling have further underscored these concerns. Several European government agencies have adopted private cloud solutions using Collabora Online and related platforms to enhance data security and maintain control over sensitive information. == History == The former LibreOffice development team from SUSE joined Collabora in September 2013, forming the subsidiary Collabora Productivity. In 2015 Collabora and IceWarp announced the development of an enterprise-ready version of LibreOffice Online to compete wi

  Read more →
• 

  Knowledge graph

  In knowledge representation and reasoning, a knowledge graph is a knowledge base that uses a graph-structured data model or topology to represent and operate on data. Knowledge graphs are often used to store interlinked descriptions of entities – objects, events, situations or abstract concepts – while also encoding the free-form semantics or relationships underlying these entities. Since the development of the Semantic Web, knowledge graphs have often been associated with linked open data projects, focusing on the connections between concepts and entities. They are also historically associated with and used by search engines such as Google, Bing, and Yahoo; knowledge engines and question-answering services such as WolframAlpha, Apple's Siri, and Amazon Alexa; and social networks such as LinkedIn and Facebook. Recent developments in data science and machine learning, particularly in graph neural networks, representation learning, and machine learning, have broadened the scope of knowledge graphs beyond their traditional use in search engines and recommender systems. They are increasingly used in scientific research, with notable applications in fields such as genomics, proteomics, and systems biology. == History == The term was coined as early as 1972 by the Austrian linguist Edgar W. Schneider, in a discussion of how to build modular instructional systems for courses. In the late 1980s, the University of Groningen and University of Twente jointly began a project called Knowledge Graphs, focusing on the design of semantic networks with edges restricted to a limited set of relations, to facilitate algebras on the graph. In subsequent decades, the distinction between semantic networks and knowledge graphs was blurred. Some early knowledge graphs were topic-specific. In 1985, Wordnet was founded, capturing semantic relationships between words and meanings – an application of this idea to language itself. In 2005, Marc Wirk founded Geonames to capture relationships between different geographic names and locales and associated entities. In 1998, Andrew Edmonds of Science in Finance Ltd in the UK created a system called ThinkBase that offered fuzzy-logic based reasoning in a graphical context. In 2007, both DBpedia and Freebase were founded as graph-based knowledge repositories for general-purpose knowledge. DBpedia focused exclusively on data extracted from Wikipedia, while Freebase also included a range of public datasets. Neither described themselves as a 'knowledge graph' but developed and described related concepts. In 2012, Google introduced their Knowledge Graph, building on DBpedia and Freebase among other sources. They later incorporated RDFa, Microdata, JSON-LD content extracted from indexed web pages, including the CIA World Factbook, Wikidata, and Wikipedia. Entity and relationship types associated with this knowledge graph have been further organized using terms from the schema.org vocabulary. The Google Knowledge Graph became a complement to string-based search within Google, and its popularity online brought the term into more common use. Since then, several large multinationals have advertised their use of knowledge graphs, further popularising the term. These include Facebook, LinkedIn, Airbnb, Microsoft, Amazon, Uber and eBay. In 2019, IEEE combined its annual international conferences on "Big Knowledge" and "Data Mining and Intelligent Computing" into the International Conference on Knowledge Graph. The development of large language models expanded interest in knowledge graphs as a way to structure information from unstructured text, with advances in language processing enabling their automatic or semi-automatic generation and expansion. The term knowledge graph has since broadened to include the dynamically constructed and adaptive graph structures, which support retrieval, reasoning, and summarization in generative systems. Microsoft Research's GraphRAG (2024) exemplified this development by integrating LLM-generated graphs into retrieval-augmented generation. == Definitions == There is no single commonly accepted definition of a knowledge graph. Most definitions view the topic through a Semantic Web lens and include these features: Flexible relations among knowledge in topical domains: A knowledge graph (i) defines abstract classes and relations of entities in a schema, (ii) mainly describes real world entities and their interrelations, organized in a graph, (iii) allows for potentially interrelating arbitrary entities with each other, and (iv) covers various topical domains. General structure: A network of entities, their semantic types, properties, and relationships. To represent properties, categorical or numerical values are often used. Supporting reasoning over inferred ontologies: A knowledge graph acquires and integrates information into an ontology and applies a reasoner to derive new knowledge. There are, however, many knowledge graph representations for which some of these features are not relevant. For those knowledge graphs, this simpler definition may be more useful: A digital structure that represents knowledge as concepts and the relationships between them (facts). A knowledge graph can include an ontology that allows both humans and machines to understand and reason about its contents. === Implementations === In addition to the above examples, the term has been used to describe open knowledge projects such as YAGO and Wikidata; federations like the Linked Open Data cloud; a range of commercial search tools, including Yahoo's semantic search assistant Spark, Google's Knowledge Graph, and Microsoft's Satori; and the LinkedIn and Facebook entity graphs. The term is also used in the context of note-taking software applications that allow a user to build a personal knowledge graph. The popularization of knowledge graphs and their accompanying methods have led to the development of graph databases such as Neo4j, GraphDB and AgensGraph. These graph databases allow users to easily store data as entities and their interrelationships, and facilitate operations such as data reasoning, node embedding, and ontology development on knowledge bases. In contrast, virtual knowledge graphs do not store information in specialized databases. They rely on an underlying relational database or data lake to answer queries on the graph. Such a virtual knowledge graph system must be properly configured in order to answer the queries correctly. This specific configuration is done through a set of mappings that define the relationship between the elements of the data source and the structure and ontology of the virtual knowledge graph. == Using a knowledge graph for reasoning over data == A knowledge graph formally represents semantics by describing entities and their relationships. Knowledge graphs may make use of ontologies as a schema layer. By doing this, they allow logical inference for retrieving implicit knowledge rather than only allowing queries requesting explicit knowledge. In order to allow the use of knowledge graphs in various machine learning tasks, several methods for deriving latent feature representations of entities and relations have been devised. These knowledge graph embeddings allow them to be connected to machine learning methods that require feature vectors like word embeddings. This can complement other estimates of conceptual similarity. Models for generating useful knowledge graph embeddings are commonly the domain of graph neural networks (GNNs). GNNs are deep learning architectures that comprise edges and nodes, which correspond well to the entities and relationships of knowledge graphs. The topology and data structures afforded by GNNs provide a convenient domain for semi-supervised learning, wherein the network is trained to predict the value of a node embedding (provided a group of adjacent nodes and their edges) or edge (provided a pair of nodes). These tasks serve as fundamental abstractions for more complex tasks such as knowledge graph reasoning and alignment. === Entity alignment === As new knowledge graphs are produced across a variety of fields and contexts, the same entity will inevitably be represented in multiple graphs. However, because no single standard for the construction or representation of knowledge graph exists, resolving which entities from disparate graphs correspond to the same real world subject is a non-trivial task. This task is known as knowledge graph entity alignment, and is an active area of research. Strategies for entity alignment generally seek to identify similar substructures, semantic relationships, shared attributes, or combinations of all three between two distinct knowledge graphs. Entity alignment methods use these structural similarities between generally non-isomorphic graphs to predict which nodes correspond to the same entity. In 2023, researchers found success in using large language models (LLMs) in the task of entity alignment. This was in particul

  Read more →
• 

  Algorithmic logic

  Algorithmic logic is a calculus of programs that allows the expression of semantic properties of programs by appropriate logical formulas. It provides a framework that enables proving the formulas from the axioms of program constructs such as assignment, iteration and composition instructions and from the axioms of the data structures in question see Mirkowska & Salwicki (1987), Banachowski et al. (1977). The following diagram helps to locate algorithmic logic among other logics. [ P r o p o s i t i o n a l l o g i c o r S e n t e n t i a l c a l c u l u s ] ⊂ [ P r e d i c a t e c a l c u l u s o r F i r s t o r d e r l o g i c ] ⊂ [ C a l c u l u s o f p r o g r a m s o r Algorithmic logic ] {\displaystyle \qquad \left[{\begin{array}{l}\mathrm {Propositional\ logic} \\or\\\mathrm {Sentential\ calculus} \end{array}}\right]\subset \left[{\begin{array}{l}\mathrm {Predicate\ calculus} \\or\\\mathrm {First\ order\ logic} \end{array}}\right]\subset \left[{\begin{array}{l}\mathrm {Calculus\ of\ programs} \\or\\{\mbox{Algorithmic logic}}\end{array}}\right]} The formalized language of algorithmic logic (and of algorithmic theories of various data structures) contains three types of well formed expressions: Terms - i.e. expressions denoting operations on elements of data structures, formulas - i.e. expressions denoting the relations among elements of data structures, programs - i.e. algorithms - these expressions describe the computations. For semantics of terms and formulas consult pages on first-order logic and Tarski's semantics. The meaning of a program K {\displaystyle K} is the set of possible computations of the program. Algorithmic logic is one of many logics of programs. Another logic of programs is dynamic logic, see dynamic logic, Harel, Kozen & Tiuryn (2000).

  Read more →
• 

  Informetrics

  Informetrics is the study of quantitative aspects of information, it is an extension and evolution of traditional bibliometrics and scientometrics. Informetrics uses bibliometrics and scientometrics methods to study mainly the problems of literature information management and evaluation of science and technology. Informetrics is an independent discipline that uses quantitative methods from mathematics and statistics to study the process, phenomena, and law of informetrics. Informetrics has gained more attention as it is a common scientific method for academic evaluation, research hotspots in discipline, and trend analysis. Informetrics includes the production, dissemination, and use of all forms of information, regardless of its form or origin. Informetrics encompasses the following fields: Scientometrics, which studies quantitative aspects of science Webometrics, which studies quantitative aspects of the World Wide Web Bibliometrics, which studies quantitative aspects of recorded information Cybermetrics, which is similar to webometrics, but broadens its definition to include electronic resources == Origin and Development == The term informetrics (French: informétrie) was coined by German scholar Otto Nacke in 1979, and came from the German word 'informetrie’. The corresponding English terminology soon appeared in the subsequent literature. In September 1980, Professor Otto Nacke introduced the term 'informetrics' at the first seminar on Informetrics in Frankfurt, Germany. Later, Committee on Informetrics has established through The International Federation for Information and Documentation (FID). In 1987, informetrics started to be officially recognized by the international information community and several foreign information scientists. In 1988, at First International Conference on Bibliometrics and Theoretical Aspects of Information Retrieval Archived 2022-05-23 at the Wayback Machine, Brooks suggested bibliometrics and scientometrics can be included in the field of informetrics. In 1990, Leo Egghe and Ronald Rousseau proposed the formation of the discipline of informetrics: statistical bibliography (1923) to bibliometrics and scientometrics (1969) and then to informetrics (1979). In 1993, the International Society for Scientometrics and Informetrics (ISSI) Archived 2023-11-05 at the Wayback Machine was founded at the International Conference on Bibliometrics, Informetrics and Scientometrics in Berlin, and the first one was held in Belgium and organized by Leo Egghe and Ronald Rousseau. The society was formally incorporated in 1994 in the Netherlands and plays a significant role in the development of informetrics. The ISSI aims to promote the "exchange and communication of professional information in the fields of scientometrics and informetrics, including improve standards, theory and practice, as well as promote research, education and training". In addition, to "engage in relevant public conversation and policy discussions". In the western world, 20th century's Informetrics is mostly based on Lotka's law, named after Alfred J. Lotka, Zipf's law, named after George Kingsley Zipf, Bradford's law named after Samuel C. Bradford and on the work of Derek J. de Solla Price, Gerard Salton, Leo Egghe, Ronald Rousseau, Tibor Braun, Olle Persson, Peter Ingwersen, Manfred Bonitz, and Eugene Garfield. == Difference Between Informetrics, Bibliometrics and Scientometrics == Since the 1960s, three similar terms have emerged in the fields of library science, philology and science of science, they are bibliometrics, scientometrics and informetrics, representing three very similar quantitative sub-disciplines. The three metrics terms can be confusing and often misused. Informetrics and bibliometrics interpenetrate each other but have different aspects in research object, research scope, and measuring unit. Informetrics and scientometrics are very different in their research purpose and research object, as well as the research scope and application. Bibliometrics is categorised under the field of library science, it uses mathematical and statistical methods to describe, evaluate, and predict the current status and trends of science and technology. Also to study the "distribution structure, quantitative relationship, change law and quantitative management of literature information, quantitative relationships, patterns and quantitative management of literature and information". The term was first used by Alan Pritchard in 1969 in his paper Statistical Bibliography or Bibliometrics?. Scientometrics is a branch of science that quantitatively evaluates and predicts the process and management of scientific activities in order to reveal their development patterns and trends. The definition of scientometrics was described by Derek De Solla Price in his book Science to Science as the “quantitative study of science, communication in science, and science policy”. === Links between the three metrics terms === The most prominent connection between the three metrics terms is in their research objects. Since all three disciplines use literature information as their research object, therefore, they have some similarities and overlaps in their research methods and fields. Moreover, they all use mathematical methods as the basic research methods and they all apply the three basic laws, Bradford's law, Lotka's law and Zipf's law. === Distinctions between the three metrics terms === The distinction between the three metrics terms can tell from their research object and research purpose. The research of bibliometrics focuses on the analysis of "scientific output in the form of articles, publications, citations, and others". Scientometrics is to measure the basic characteristics and laws of scientific activities. Where informetrics is to investigate information sources and information distribution process. == Concept and System Structure == === Purpose of Informetrics Research === The main purpose of informetrics is to use its theocratical research to solve the methodological issues in the research process, and to discover and reveal the basic laws of information distribution through the study of information process and phenomenon. In this way, makes information management more scientific and provides a quantitative basis for information services and information management decisions. For informetrics, it is necessary to bring quantitative analysis methods to further reveal the structure of information units and the "quantitative change law of literature information”. Further to this, to improve the scientific accuracy of information science from a theoretical point of view. At the same time, to better solve the basic contradictions in the information service, overcome the information crisis, and make the information management work more effective to serve science and technology, economic and social development. Quantitative analysis of bibliographic data was pioneered by Robert K. Merton in an article called Science, Technology, and Society in Seventeenth Century England and originally published by Merton in 1938. === The Significance of Informetrics Research === The significance of informetrics research is to summarize various empirical laws from the theoretical point of view, at the same time test and modify the various empirical laws in the new information unit conditions, and explore its new applicability, therefore, the scientific nature of information science can be improved, but also to provide theoretical guidance for practical work. === The Objects of Informetrics Research === The object of informetrics is broader than the field of bibliometrics and scientometrics, including "messages, data, events, objects, text, and documents”. Informetrics is often used to inform policies and decisions across a broad range of fields, such as economy, politics, technology and social spheres that "influence the flow and use patterns of information". Tague-Sutcliffe describes the following uses of informetrics: Citation analysis; Characteristics of authors; Use of recorded information; Obsolescence of the literature; Concomitant growth of new concepts; Characteristics of publication sources; Definition and measurement o information; Growth of subject literature, databases, libraries; Types and characteristics of retrieval performance measures; Statistical aspects of language, word, and phrase frequencies. == Basic Laws == In the field of informetrics research, there are many outstanding contributors in the discipline with a solid knowledge of quantitative research methods. In the early 20th century, several scientists contributed empirical applications that have become the three basic laws of informetrics, Bradford's law, Lotka's law, and Zipf's law, which promote the development of informetrics. === Bradford's Law === The British documentalist and librarian Samuel C. Bradford first discovered the law of concentration and scattering of literature, and in 1934, it has be

  Read more →
• 

  Resel

  In image analysis, a resel (from resolution element) represents the actual spatial resolution in an image or a volumetric dataset. The number of resels in the image may be lower or equal to the number of pixel/voxels in the image. In an actual image the resels can vary across the image and indeed the local resolution can be expressed as "resels per pixel" (or "resels per voxel"). In functional neuroimaging analysis, an estimate of the number of resels together with random field theory is used in statistical inference. Keith Worsley has proposed an estimate for the number of resels/roughness. The word "resel" is related to the words "pixel", "texel", and "voxel". Waldo R. Tobler is probably among the first to use the word.

  Read more →
• 

  Learning augmented algorithm

  A learning augmented algorithm (also called algorithm with predictions) is an algorithm that can make use of a prediction to improve its performance. Whereas in regular algorithms just the problem instance is inputted, learning augmented algorithms accept an extra parameter. This extra parameter often is a prediction of some property of the solution. This prediction is then used by the algorithm to improve its running time or the quality of its output. The most common application are online algorithms, where a prediction on the uncertain instance is provided. == Description == A learning augmented algorithm typically takes an input ( I , A ) {\displaystyle ({\mathcal {I}},{\mathcal {A}})} . Here I {\displaystyle {\mathcal {I}}} is a problem instance and A {\displaystyle {\mathcal {A}}} is the prediction. A prediction can be any object. Common are the following types: Prediction of an optimal solution. The prediction gives a solution to the problem or characterizes an optimal solution. Prediction of the input. This is mainly used for online problems. Prediction of algorithmic actions. A prediction tailored to a specific algorithm that suggests a specific algorithm execution. Learning augmented algorithms usually satisfy the following three properties: Consistency. A learning augmented algorithm is said to be consistent if the algorithm can be proven to have a good performance when it is provided with an accurate prediction. Smoothness. A learning augmented algorithm is called smooth if its performance can be bounded by a function of the quality of the prediction. Here, the quality can be measured in a problem specific way. This is also called the prediction error. Robustness. A learning augmented algorithm is called robust if its worst-case performance can be bounded even if the given prediction is inaccurate. Learning augmented algorithms generally do not prescribe how the prediction should be done. For this purpose machine learning can be used. == Applications == A few examples of problems where learning augmented algorithms have been applied are the following. === Online algorithms === The ski rental problem The weighted paging problem The set cover problem Nonclairvoyant scheduling The online bipartite matching problem === Warm starting === ==== Data structures ==== The binary search algorithm is an algorithm for finding elements of a sorted list x 1 , … , x n {\displaystyle x_{1},\ldots ,x_{n}} . It needs O ( log ⁡ ( n ) ) {\displaystyle O(\log(n))} steps to find an element with some known value y {\displaystyle y} in a list of length n {\displaystyle n} . With a prediction i {\displaystyle i} for the position of y {\displaystyle y} , the following learning augmented algorithm can be used. First, look at position i {\displaystyle i} in the list. If x i = y {\displaystyle x_{i}=y} , the element has been found. If x i < y {\displaystyle x_{i} y {\displaystyle x_{i}>y} , do the same as in the previous case, but instead consider i − 1 , i − 2 , i − 4 , … {\displaystyle i-1,i-2,i-4,\ldots } . The error is defined to be η = | i − i ∗ | {\displaystyle \eta =|i-i^{}|} , where i ∗ {\displaystyle i^{}} is the real index of y {\displaystyle y} . In the learning augmented algorithm, probing the positions i + 1 , i + 2 , i + 4 , … {\displaystyle i+1,i+2,i+4,\ldots } takes log 2 ⁡ ( η ) {\displaystyle \log _{2}(\eta )} steps. Then a binary search is performed on a list of size at most 2 η {\displaystyle 2\eta } , which takes log 2 ⁡ ( η ) {\displaystyle \log _{2}(\eta )} steps. This makes the total running time of the algorithm 2 log 2 ⁡ ( η ) {\displaystyle 2\log _{2}(\eta )} . So, when the error is small, the algorithm is faster than a normal binary search. This shows that the algorithm is consistent. Even in the worst case, the error will be at most n {\displaystyle n} . Then the algorithm takes at most O ( log ⁡ ( n ) ) {\displaystyle O(\log(n))} steps, so the algorithm is robust. ==== More examples ==== The maximum weight matching problem === Approximation algorithms === The maximum cut problem The vertex cover problem === Mechanism Design === The facility location problem

  Read more →
• 

  Information quality

  Information quality (IQ) is a contextual property of or a perspective to the content within information systems. There exist two complementary yet partially conflicting definitions of high-quality: firstly, information is considered high quality if it is fit for its intended purpose ; secondly, it is deemed high quality if it conforms to specified requirements . The primary distinction between these definitions is that Juran's perspective focuses on the suitability of information for its intended purpose, which can be measured by the success of its application even without direct access to or exact knowledge of the data. For example, a black-box AI with access to English Wikipedia can work well for users' purposes but using Estonian Wikipedia fails for the same purposes. Given that the AI remains the same, it can be concluded that English version data would be of higher quality in comparison to Estonian version, even without exact comparison of data contents and their properties in each version. In contrast, Crosby emphasizes adherence to predefined specifications, assuming specific criteria rather than measuring the success of its use; for instance, information in Wikipedia could be proven to be good based on criteria such as existing peer validation and academic references, even if the AI results are poor. This approach falls into problems when data is not completely accessible or all quality properties cannot be known and measured leading to false impression of quality due to lacking and misleading metrics. Numerous IQ frameworks and methodologies provide tangible approach to assess and measure DQ/IQ in a robust and rigorous manner. == Conceptual problems == Although the foundational definitions are usable for most everyday purposes, specialists often use more complex models for information quality. It has been suggested, however, that higher the quality the greater will be the confidence in meeting more general, less specific contexts. == Dimensions and metrics of information quality == "Information quality" is a measure of its fitness for use or conformance to requirements. In this way, "quality" is considered contextual and it can then vary across users and uses of the information. The exact degree of quality is often described with dimensions such as accuracy, timeliness, completeness, and similar scales. Although a huge amount of academic research has been directed to these dimensions, there does not exist consensus on their definitions or practical usefulness . Historically, Richard Wang and Diane Strong proposed a list of dimensions or elements used in assessing Information Quality is: Intrinsic IQ: accuracy, objectivity, believability, reputation Contextual IQ: relevance, value-added, timeliness, completeness, amount of information Representational IQ: interpretability, format, coherence, compatibility Accessibility IQ: accessibility, access security Other authors propose similar but different lists of dimensions for analysis, and emphasize measurement and reporting as information quality metrics. Larry English prefers the term "characteristics" to dimensions. However, a considerable amount of information quality research involves investigating and describing various categories of desirable attributes (or dimensions) of data. Research has recently shown the huge diversity of terms and classification structures used. === Quality metrics === Source: Authority/verifiability Authority refers to the expertise or recognized official status of a source. Consider the reputation of the author and publisher. When working with legal or government information, consider whether the source is the official provider of the information. Verifiability refers to the ability of a reader to verify the validity of the information irrespective of how authoritative the source is. To verify the facts is part of the duty of care of the journalistic deontology, as well as, where possible, to provide the sources of information so that they can be verified Scope of coverage Scope of coverage refers to the extent to which a source explores a topic. Consider time periods, geography or jurisdiction and coverage of related or narrower topics. Composition and organization Composition and organization has to do with the ability of the information source to present its particular message in a coherent, logically sequential manner. Objectivity Objectivity is the bias or opinion expressed when a writer interprets or analyze facts. Consider the use of persuasive language, the source's presentation of other viewpoints, its reason for providing the information and advertising. Integrity Adherence to moral and ethical principles; soundness of moral character The state of being whole, entire, or undiminished Comprehensiveness Of large scope; covering or involving much; inclusive: a comprehensive study. Comprehending mentally; having an extensive mental grasp. Insurance. covering or providing broad protection against loss. Validity Validity of some information has to do with the degree of obvious truthfulness which the information carries Uniqueness As much as 'uniqueness' of a given piece of information is intuitive in meaning, it also significantly implies not only the originating point of the information but also the manner in which it is presented and thus the perception which it conjures. The essence of any piece of information we process consists to a large extent of those two elements. Timeliness Timeliness refers to information that is current at the time of publication. Consider publication, creation and revision dates. Beware of Web site scripting that automatically reflects the current day's date on a page. Reproducibility (utilized primarily when referring to instructive information) Means that documented methods are capable of being used on the same data set to achieve a consistent result. == Professional associations == IQ International—the International Association for Information and Data Quality IQ International is a not-for-profit, vendor neutral, professional association formed in 2004, dedicated to building the information and data quality profession. CDOIQ Society Chief Data Officers and Information Quality Society is a global professional society supporting data leaders with networking, meetings, best practices, experience, certification, and training. == Information quality conferences == A number of major conferences relevant to information quality are held annually: Annual MIT Chief Data Officer & Information Quality (CDOIQ) Symposium Annual conferences held at the Massachusetts Institute of Technology, Cambridge, MA, USA Data Governance and Information Quality Conference Commercial conferences held each year in the USA Data Quality Asia Pacific Commercial conference held annually in Sydney or Melbourne, Australia Enterprise Data and Business Intelligence Conference Europe Commercial conferences held annually in London, England. Information and Data Quality Conference Not for profit conference run annually by IQ International (the International Association for Information and Data Quality) in the USA International Conference on Information Quality Academic Conference launched through MITIQ held annually at a University Master Data Management & Data Governance Conferences Six major conferences are run annually by the MDM Institute in venues such as London, San Francisco, Sydney, Toronto, Madrid, Frankfurt, Shanghai and New York City.

  Read more →
• 

  Voiceverse NFT plagiarism scandal

  In January 2022, 15—the pseudonymous Massachusetts Institute of Technology (MIT) artificial intelligence researcher and creator of the non-commercial generative artificial intelligence voice synthesis research project 15.ai—discovered that the blockchain-based technology company Voiceverse had plagiarized from their platform. Voiceverse marketed itself as a service that offered AI voice cloning technology that could be purchased and traded as non-fungible tokens (NFTs). Amid heightened controversy over NFTs in the gaming industry, voice actor Troy Baker (who has been described as one of the most famous voice actors in video games) announced his partnership with Voiceverse on January 14, 2022, triggering immediate backlash over concerns about the environmental impact of NFTs, potential for fraud, predatory monetization in video games, and the potential of AI displacing jobs for human voice actors. Later that same day, 15 revealed through server logs that Voiceverse had generated voice lines using 15's free text-to-speech platform, pitch-shifted the audio to make them unrecognizable, and falsely marketed the samples as their own technology before selling them as NFTs. Within an hour of being confronted with evidence, Voiceverse confessed and stated that their marketing team had used 15.ai without proper attribution while rushing to create a technology demo to coincide with Baker's partnership announcement, further exacerbating the already negative reception to the original announcement. In response, 15 replied "Go fuck yourself"; the interaction went viral and garnered a large amount of support for the developer. News publications universally characterized this incident as Voiceverse having "stolen" from 15.ai. The next day, Baker appeared on a podcast and stated that his motivation had been to help independent creators who were unable to afford professional voice actors. Following continued backlash and the plagiarism revelation, Baker ended his partnership with Voiceverse on January 31, 2022. Subsequently, the incident was documented in multiple AI ethics databases, criticisms of predatory monetization in video games, and retrospectives as one of the earliest instances of plagiarism and theft stemming from artificial intelligence during the AI boom. == Background == === Troy Baker === Troy Baker is a prominent voice actor in the video game industry best known for his performances as Joel Miller in The Last of Us franchise. Baker has been described as "ubiquitous" by Polygon, "one of the most high-profile and prolific voice actors in video games" by Eurogamer, and "arguably the most famous voice actor in the gaming industry" by GameGuru. His other prominent roles include voicing Agent John "Jonesy" Jones in Fortnite, Booker DeWitt in BioShock Infinite, and both Batman and Joker in multiple Batman video games. As of October 2025, Baker holds the record for the most acting nominations at the BAFTA Games Awards, with five between 2013 and 2021. === Voiceverse === Voiceverse is a blockchain-based startup founded by the Bored Ape Yacht Club that marketed itself as offering AI voice cloning technology in the form of NFTs. Prior to the announcement of their partnership with Baker, Voiceverse had partnered with LOVO, Inc., an AI voice platform that, according to LOVO, could generate human-like voices. Voiceverse stated that any user who purchases a voice NFT would have unlimited and perpetual access to the voice model, which could be used to create content such as audiobooks, YouTube videos, podcasts, e-learning materials, in-game voice chat, and Zoom calls. Voiceverse promised that buyers would "OWN [sic] all of the IP" of content they created using these voices. Voiceverse's roadmap included plans to release 8,888 initial voice NFTs, a feature to add emotions to existing voices, and the ability for users to mint their own voices as NFTs. Prior to Baker's partnership, Voiceverse had also partnered with voice actors Charlet Chung, who voices D.Va in Overwatch, and Andy Milonakis of The Andy Milonakis Show. === 15.ai === 15.ai is a free web application launched in 2020 that uses artificial intelligence to generate text-to-speech voices of fictional characters from popular media. Created by a pseudonymous artificial intelligence researcher known as 15, who began developing the technology as a freshman during their undergraduate research at MIT, it was an early example of an application of generative artificial intelligence during the initial stages of the AI boom. The platform showed that deep neural networks could generate emotionally expressive speech with only 15 seconds of speech; the name "15.ai" references the creator's statement that a voice can be convincingly cloned with just 15 seconds of audio, as opposed to the tens of hours of data previously required. 15.ai became an Internet phenomenon in early 2021 when content utilizing it went viral on social media and quickly gained widespread use among various Internet fandoms. 15 has emphasized that it remain free and non-commercial; it only requires users to give proper credit when using the service for content creation. === NFTs in the video game industry === By early 2022, NFTs had become highly controversial within the gaming industry. Critics raised concerns about their environmental impact due to the significant energy consumption of blockchain technology. In addition, the prevalence of scams, fraud, and potential money laundering associated with NFT sales, as well as fears that NFTs were a new form of predatory monetization following the increasing frequency of loot boxes, caused vocal pushback from the gaming community. Several major gaming companies had begun exploring NFT integration into their products, though fan backlash had already forced some projects to be cancelled. On December 16, 2021, the developers of S.T.A.L.K.E.R. 2: Heart of Chernobyl announced that they would be including NFTs in the game, but cancelled within an hour of the announcement due to immediate universal backlash. Simultaneously, the rise of AI voice technology raised concerns among voice actors about potential job displacement and the devaluation of their work amidst the voice acting industry's ongoing struggles for better compensation and working conditions. == Partnership announcement and backlash == On January 14, 2022, 1:02 a.m. EST, Baker announced on Twitter that he was partnering with Voiceverse "to explore ways where together we might bring new tools to new creators to make new things, and allow everyone a chance to own & invest in the IP's they create." The announcement concluded with the statement "You can hate. Or you can create." Baker's specific role with Voiceverse remained unclear at the time of the announcement. Along with Baker's announcement, Voiceverse promoted their supposed voice AI technology on Twitter by posting animated videos that featured a cat character created by NFT firm Chubbiverse. The videos concluded with text that read "The Voice Powered By Voiceverse"; Voiceverse stated on Twitter that the voices in the animations had been generated using their own AI voice synthesis technology and presented the videos as a technology demonstration of their voice NFT capabilities. The announcement provoked immediate and widespread backlash from the gaming community. Baker's tweet received thousands of replies and quote retweets (the vast majority of which were negative), far more than the number of likes; Michael McWhertor of Polygon described it as a "textbook example of being ratioed" and commented that reactions had been amplified by the final part of Baker's announcement. Michael Beckwith of Metro called Baker's approach "bizarrely aggressive". Later that day, Baker responded to the backlash by apologizing for his choice of words. He said he appreciated people's thoughts and acknowledged that the "hate/create part might have been a bit antagonistic," calling it a "bad attempt to bring levity". Despite the apology, Baker and his fellow voice actors did not distance themselves from Voiceverse at this point. At the same time, Voiceverse attempted to address the criticisms, stating that they were working to move to more environmentally friendly blockchain technology and that voice actors would receive royalties from NFT sales, with actors benefiting from any increase in NFT value. == Plagiarism revelation == On December 13, 2021, amidst the increasingly negative reactions toward NFTs among the general public, the creator of 15.ai (known pseudonymously as 15) announced that they had "no interest in incorporating NFTs into any aspect of [their] work." On January 14, 2022, 11:17 a.m. EST (10 hours after Baker's initial announcement), 15 commented on the Voiceverse venture, stating that it "sounds like a scam". Two hours later, at 1:20 p.m., 15 explicitly accused Voiceverse of "actively attempting to appropriate [15's] work for [Voiceverse's] own benefit." 15 provided evidence through

  Read more →
• 

  Pwnie Awards

  The Pwnie Awards are an annual awards ceremony that recognizes both excellence and incompetence in the field of information security, described by SecurityWeek as an event that "recognizes excellence and mocks incompetence in cybersecurity." Winners are selected by a committee of security industry professionals from nominations collected from the information security community. Nominees are announced yearly at Summercon, and the awards themselves are presented at the Black Hat Security Conference. == Origins == The name Pwnie Award is based on the word "pwn", which is hacker slang meaning to "compromise" or "control" based on the previous usage of the word "own" (and it is pronounced similarly). The name "The Pwnie Awards," pronounced as "Pony," is meant to sound like the Tony Awards, an awards ceremony for Broadway theater in New York City. == History == The Pwnie Awards were founded in 2007 by Alexander Sotirov and Dino Dai Zovi following discussions regarding Dino's discovery of a cross-platform QuickTime vulnerability (CVE-2007-2175) and Alexander's discovery of an ANI file processing vulnerability (CVE-2007-0038) in Internet Explorer. == Winners == === 2024 === Most Epic Fail: Crowdstrike for 2024 CrowdStrike incident Best Mobile Bug: Operation Triangulation Lamest Vendor Response: Xiaomi for obstructing Pwn2Own researchers from using their services Best Cryptographic Attack: GoFetch Best Desktop Bug: forcing realtime WebAudio playback in Chrome (CVE-2023-5996) Best Song: Touch Some Grass by UwU Underground Best Privilege Escalation: Windows Streaming Service UAF (CVE-2024-30089) by Valentina Palmiotti (chompie) Best Remote Code Execution: Microsoft Message Queuing (MSMQ) Remote Code Execution Vulnerability (CVE-2024-30080) Most Epic Achievement: Discovery and reverse engineering of the XZ Utils backdoor Most Innovative Research: Let the Cache Cache and Let the WebAssembly Assemble: Knocking’ on Chrome’s Shell by Edouard Bochin, Tao Yan, and Bo Qu Most Underhyped Research: See No Eval: Runtime Dynamic Code Execution in Objective-C === 2023 === Best Desktop Bug: CountExposure! by RyeLv(@b2ahex) Best Cryptographic Attack: Video-based cryptanalysis: Extracting Cryptographic Keys from Video Footage of a Device’s Power LED by Ben Nassi, Etay Iluz, Or Cohen, Ofek Vayner, Dudi Nassi, Boris Zadov, Yuval Elovici Best Song: Clickin’ Most Innovative Research: Inside Apple’s Lightning: Jtagging the iPhone for Fuzzing and Profit Most Under-Hyped Research: Activation Context Cache Poisoning Best Privilege Escalation Bug: URB Excalibur: Slicing Through the Gordian Knot of VMware VM Escapes Best Remote Code Execution Bug: ClamAV RCE Lamest Vendor Response: Three Lessons From Threema: Analysis of a Secure Messenger Most Epic Fail: “Holy fucking bingle, we have the no fly list,” Epic Achievement: Clement Lecigne: 0-days hunter world champion Lifetime Achievement Award: Mudge === 2022 === Lamest Vendor Response: Google's "TAG" response team for "unilaterally shutting down a counterterrorism operation." Epic Achievement: Yuki Chen’s Windows Server-Side RCE Bugs Most Epic Fail: HackerOne Employee Caught Stealing Vulnerability Reports for Personal Gains Best Desktop Bug: Pietro Borrello, Andreas Kogler, Martin Schwarzl, Moritz Lipp, Daniel Gruss, Michael Schwarz for Architecturally Leaking Data from the Microarchitecture Most Innovative Research: Pietro Borrello, Martin Schwarzl, Moritz Lipp, Daniel Gruss, Michael Schwarz for Custom Processing Unit: Tracing and Patching Intel Atom Microcode Best Cryptographic Attack: Hertzbleed: Turning Power Side-Channel Attacks Into Remote Timing Attacks on x86 by Yingchen Wang, Riccardo Paccagnella, Elizabeth Tang He, Hovav Shacham, Christopher Fletcher, David Kohlbrenner Best Remote Code Execution Bug: KunlunLab for Windows RPC Runtime Remote Code Execution (CVE-2022-26809) Best Privilege Escalation Bug: Qidan He of Dawnslab, for Mystique in the House: The Droid Vulnerability Chain That Owns All Your Userspace Best Mobile Bug: FORCEDENTRY Most Under-Hyped Research: Yannay Livneh for Spoofing IP with IPIP Best Song: Dialed Up by Project Mammoth === 2021 === Lamest Vendor Response: Cellebrite, for their response to Moxie, the creator of Signal, reverse-engineering their UFED and accompanying software and reporting a discovered exploit. Epic Achievement: Ilfak Guilfanov, in honor of IDA's 30th Anniversary. Best Privilege Escalation Bug: Baron Samedit of Qualys, for the discovery of a 10-year-old exploit in sudo. Best Song: The Ransomware Song by Forrest Brazeal Best Server-Side Bug: Orange Tsai, for his Microsoft Exchange Server ProxyLogon attack surface discoveries. Best Cryptographic Attack: The NSA for its disclosure of a bug in the verification of signatures in Windows which breaks the certificate trust chain. Most Innovative Research: Enes Göktaş, Kaveh Razavi, Georgios Portokalidis, Herbert Bos, and Cristiano Giuffrida at VUSec for their research on the "BlindSide" Attack. Most Epic Fail: Microsoft, for their failure to fix PrintNightmare. Best Client-Side Bug: Gunnar Alendal's discovery of a buffer overflow on the Samsung Galaxy S20's secure chip. Most Under-Hyped Research: The Qualys Research Team for 21Nails, 21 vulnerabilities in Exim, the Internet's most popular mail server. === 2020 === Best Server-Side Bug: BraveStarr (CVE-2020-10188) – A Fedora 31 netkit telnetd remote exploit (Ronald Huizer') Best Privilege Escalation Bug: checkm8 – A permanent unpatchable USB bootrom exploit for a billion iOS devices. (axi0mX) Epic Achievement: "Remotely Rooting Modern Android Devices" (Guang Gong) Best Cryptographic Attack: Zerologon vulnerability (Tom Tervoort, CVE-2020-1472) Best Client-Side Bug: RCE on Samsung Phones via MMS (CVE-2020-8899 and -16747), a zero click remote execution attack. (Mateusz Jurczyk) Most Under-Hyped Research: Vulnerabilities in System Management Mode (SMM) and Trusted Execution Technology (TXT) (CVE-2019-0151 and -0152) (Gabriel Negreira Barbosa, Rodrigo Rubira Branco, Joe Cihula) Most Innovative Research: TRRespass: When Memory Vendors Tell You Their Chips Are Rowhammer-free, They Are Not. (Pietro Frigo, Emanuele Vannacci, Hasan Hassan, Victor van der Veen, Onur Mutlu, Cristiano Giuffrida, Herbert Bos, Kaveh Razavi) Most Epic Fail: Microsoft; for the implementation of Elliptic-curve signatures which allowed attackers to generate private pairs for public keys of any signer, allowing HTTPS and signed binary spoofing. (CVE-2020-0601) Best Song: Powertrace by Rebekka Aigner, Daniel Gruss, Manuel Weber, Moritz Lipp, Patrick Radkohl, Andreas Kogler, Maria Eichlseder, ElTonno, tunefish, Yuki and Kater Lamest Vendor Response: Daniel J. Bernstein (CVE-2005-1513) === 2019 === Best Server-Side Bug: Orange Tsai and Meh Chang, for their SSL VPN research. Most Innovative Research: Vectorized Emulation Brandon Falk Best Cryptographic Attack: \m/ Dr4g0nbl00d \m/ Mathy Vanhoef, Eyal Ronen Lamest Vendor Response: Bitfi Most Over-hyped Bug: Allegations of Supermicro hardware backdoors, Bloomberg Most Under-hyped Bug: Thrangrycat, (Jatin Kataria, Red Balloon Security) === 2018 === Most Innovative Research: Spectre/Meltdown (Paul Kocher, Jann Horn, Anders Fogh, Daniel Genkin, Daniel Gruss, Werner Haas, Mike Hamburg, Moritz Lipp, Stefan Mangard, Thomas Prescher, Michael Schwarz, Yuval Yarom) Best Privilege Escalation Bug: Spectre/Meltdown (Paul Kocher, Jann Horn, Anders Fogh, Daniel Genkin, Daniel Gruss, Werner Haas, Mike Hamburg, Moritz Lipp, Stefan Mangard, Thomas Prescher, Michael Schwarz, Yuval Yarom) Lifetime Achievement: Michał Zalewski Best Cryptographic Attack: ROBOT - Return Of Bleichenbacher’s Oracle Threat Hanno Böck, Juraj Somorovsky, Craig Young Lamest Vendor Response: Bitfi hardware crypto-wallet, after the "unhackable" device was hacked to extract the keys required to steal coins and rooted to play Doom. === 2017 === Epic Achievement: Federico Bento for Finally getting TIOCSTI ioctl attack fixed Most Innovative Research: ASLR on the line Ben Gras, Kaveh Razavi, Erik Bosman, Herbert Bos, Cristiano Giuffrida Best Privilege Escalation Bug: DRAMMER Victor van der Veen, Yanick Fratantonio, Martina Lindorfer, Daniel Gruss, Clementine Maurice, Giovanni Vigna, Herbert Bos, Kaveh Razavi, Cristiano Giuffrida Best Cryptographic Attack: The first collision for full SHA-1 Marc Stevens, Elie Bursztein, Pierre Karpman, Ange Albertini, Yarik Markov Lamest Vendor Response: Lennart Poettering - for mishandling security vulnerabilities most spectacularly for multiple critical Systemd bugs Best Song: Hello (From the Other Side) - Manuel Weber, Michael Schwarz, Daniel Gruss, Moritz Lipp, Rebekka Aigner === 2016 === Most Innovative Research: Dedup Est Machina: Memory Deduplication as an Advanced Exploitation Vector Erik Bosman, Kaveh Razavi, Herbert Bos, Cristiano Giuffrida Lifetime Achievement: Peiter Zatko aka Mudge Best Cryptographic Attack: DROWN attack Nimrod Aviram et al. Best Song: Cyberlier - Katie Mous

  Read more →
• 

  SIGMOD Edgar F. Codd Innovations Award

  The ACM SIGMOD Edgar F. Codd Innovations Award is a lifetime research achievement award given by the ACM Special Interest Group on Management of Data, at its yearly flagship conference (also called SIGMOD). According to its homepage, it is given "for innovative and highly significant contributions of enduring value to the development, understanding, or use of database systems and databases". The award has been given since 1992. Until 2003, this award was known as the “SIGMOD Innovations Award.” In 2004, SIGMOD, with the unanimous approval of ACM Council, decided to rename the award to honor Dr. E.F. (Ted) Codd (1923 – 2003) who invented the relational data model and was responsible for the significant development of the database field as a scientific discipline. == Recipients ==

  Read more →
• 

  Certifying algorithm

  In theoretical computer science, a certifying algorithm is an algorithm that outputs, together with a solution to the problem it solves, a proof that the solution is correct. A certifying algorithm is said to be efficient if the combined runtime of the algorithm and a proof checker is slower by at most a constant factor than the best known non-certifying algorithm for the same problem. The proof produced by a certifying algorithm should be in some sense simpler than the algorithm itself, for otherwise any algorithm could be considered certifying (with its output verified by running the same algorithm again). Sometimes this is formalized by requiring that a verification of the proof take less time than the original algorithm, while for other problems (in particular those for which the solution can be found in linear time) simplicity of the output proof is considered in a less formal sense. For instance, the validity of the output proof may be more apparent to human users than the correctness of the algorithm, or a checker for the proof may be more amenable to formal verification. Implementations of certifying algorithms that also include a checker for the proof generated by the algorithm may be considered to be more reliable than non-certifying algorithms. For, whenever the algorithm is run, one of three things happens: it produces a correct output (the desired case), it detects a bug in the algorithm or its implication (undesired, but generally preferable to continuing without detecting the bug), or both the algorithm and the checker are faulty in a way that masks the bug and prevents it from being detected (undesired, but unlikely as it depends on the existence of two independent bugs). == Examples == Many examples of problems with checkable algorithms come from graph theory. For instance, a classical algorithm for testing whether a graph is bipartite would simply output a Boolean value: true if the graph is bipartite, false otherwise. In contrast, a certifying algorithm might output a 2-coloring of the graph in the case that it is bipartite, or a cycle of odd length if it is not. Any graph is bipartite if and only if it can be 2-colored, and non-bipartite if and only if it contains an odd cycle. Both checking whether a 2-coloring is valid and checking whether a given odd-length sequence of vertices is a cycle may be performed more simply than testing bipartiteness. Analogously, it is possible to test whether a given directed graph is acyclic by a certifying algorithm that outputs either a topological order or a directed cycle. It is possible to test whether an undirected graph is a chordal graph by a certifying algorithm that outputs either an elimination ordering (an ordering of all vertices such that, for every vertex, the neighbors that are later in the ordering form a clique) or a chordless cycle. And it is possible to test whether a graph is planar by a certifying algorithm that outputs either a planar embedding or a Kuratowski subgraph. The extended Euclidean algorithm for the greatest common divisor of two integers x and y is certifying: it outputs three integers g (the divisor), a, and b, such that ax + by = g. This equation can only be true of multiples of the greatest common divisor, so testing that g is the greatest common divisor may be performed by checking that g divides both x and y and that this equation is correct.

  Read more →
• 

  Emotion Markup Language

  An Emotion Markup Language (EML or EmotionML) has first been defined by the W3C Emotion Incubator Group (EmoXG) as a general-purpose emotion annotation and representation language, which should be usable in a large variety of technological contexts where emotions need to be represented. Emotion-oriented computing (or "affective computing") is gaining importance as interactive technological systems become more sophisticated. Representing the emotional states of a user or the emotional states to be simulated by a user interface requires a suitable representation format; in this case a markup language is used. EmotionML version 1.0 was published by the group in May 2014. == Example == Here is an example of an EmotionML document describing emotions expressed in a video recording of the interaction between a teacher, Alice, and a student, Bob. == History == In 2006, a first W3C Incubator Group, the Emotion Incubator Group (EmoXG), was set up "to investigate a language to represent the emotional states of users and the emotional states simulated by user interfaces" with the final Report published on 10 July 2007. In 2007, the Emotion Markup Language Incubator Group (EmotionML XG) was set up as a follow-up to the Emotion Incubator Group, "to propose a specification draft for an Emotion Markup Language, to document it in a way accessible to non-experts, and to illustrate its use in conjunction with a number of existing markups." The final report of the Emotion Markup Language Incubator Group, Elements of an EmotionML 1.0, was published on 20 November 2008. The work then was continued in 2009 in the frame of the W3C's Multimodal Interaction Activity, with the First Public Working Draft of "Emotion Markup Language (EmotionML) 1.0" being published on 29 October 2009. The Last Call Working Draft of "Emotion Markup Language 1.0", was published on 7 April 2011. The Last Call Working Draft addressed all open issues that arose from feedback of the community on the First Call Working Draft as well as results of a workshop held in Paris in October 2010. Along with the Last Call Working Draft, a list of vocabularies for EmotionML has been published to aid developers using common vocabularies for annotating or representing emotions. Annual draft updates were published until the 1.0 version was finished in 2014. == Reasons for defining an emotion markup language == A standard for an emotion markup language would be useful for the following purposes: To enhance computer-mediated human-human or human-machine communication. Emotions are a basic part of human communication and should therefore be taken into account, e.g. in emotional Chat systems or emphatic voice boxes. This involves specification, analysis and display of emotion related states. To enhance systems' processing efficiency. Emotion and intelligence are strongly interconnected. The modeling of human emotions in computer processing can help to build more efficient systems, e.g. using emotional models for time-critical decision enforcement. To allow the analysis of non-verbal behavior, emotion, mental states that can be provided using web services to enable data collection, analysis, and reporting. Concrete examples of existing technology that could apply EmotionML include: Opinion mining / sentiment analysis in Web 2.0, to automatically track customer's attitude regarding a product across blogs; Affective monitoring, such as ambient assisted living applications, fear detection for surveillance purposes, or using wearable sensors to test customer satisfaction; Wellness technologies that provide assistance according to a person's emotional state with the goal to improve the person's well-being; Character design and control for games and virtual worlds; Building web services to capture, analysis, and report data of non-verbal behavior, emotion and mental states of an individual or group across the internet using standard web technologies such as HTML5 and JSON. Social robots, such as guide robots engaging with visitors; Expressive speech synthesis, generating synthetic speech with different emotions, such as happy or sad, friendly or apologetic; expressive synthetic speech would for example make more information available to blind and partially sighted people, and enrich their experience of the content; Emotion recognition (e.g., for spotting angry customers in speech dialog systems, to improve computer games or e-Learning applications); Support for people with disabilities, such as educational programs for people with autism. EmotionML can be used to make the emotional intent of content explicit. This would enable people with learning disabilities (such as Asperger syndrome) to realise the emotional context of the content; EmotionML can be used for media transcripts and captions. Where emotions are marked up to help deaf or hearing impaired people who cannot hear the soundtrack, more information is made available to enrich their experience of the content. The Emotion Incubator Group has listed 39 individual use cases for an Emotion markup language. A standardised way to mark up the data needed by such "emotion-oriented systems" has the potential to boost development primarily because data that was annotated in a standardised way can be interchanged between systems more easily, thereby simplifying a market for emotional databases, and the standard can be used to ease a market of providers for sub-modules of emotion processing systems, e.g. a web service for the recognition of emotion from text, speech or multi-modal input. == The challenge of defining a generally usable emotion markup language == Any attempt to standardize the description of emotions using a finite set of fixed descriptors is doomed to failure, as there is no consensus on the number of relevant emotions, on the names that should be given to them or how else best to describe them. For example, the difference between ":)" and "(:" is small, but using a standardized markup it would make one invalid. Even more basically, the list of emotion-related states that should be distinguished varies depending on the application domain and the aspect of emotions to be focused. Basically, the vocabulary needed depends on the context of use. On the other hand, the basic structure of concepts is less controversial: it is generally agreed that emotions involve triggers, appraisals, feelings, expressive behavior including physiological changes, and action tendencies; emotions in their entirety can be described in terms of categories or a small number of dimensions; emotions have an intensity, and so on. For details, see the Scientific Descriptions of Emotions in the Final Report of the Emotion Incubator Group. Given this lack of agreement on descriptors in the field, the only practical way of defining an emotion markup language is the definition of possible structural elements and to allow users to "plug in" vocabularies that they consider appropriate for their work. An additional challenge lies in the aim to provide a markup language that is generally usable. The requirements that arise from different use cases are rather different. Whereas manual annotation tends to require all the fine-grained distinctions considered in the scientific literature, automatic recognition systems can usually distinguish only a very small number of different states and affective avatars need yet another level of detail for expressing emotions in an appropriate way. For the reasons outlined here, it is clear that there is an inevitable tension between flexibility and interoperability, which need to be weighed in the formulation of an EmotionML. The guiding principle in the following specification has been to provide a choice only where it is needed, and to propose reasonable default options for every choice. == Applications and web services benefiting from an emotion markup language == There are a range of existing projects and applications to which an emotion markup language will enable the building of webservices to measure capture data of individuals non-verbal behavior, mental states, and emotions and allowing results to be reported and rendered in a standardized format using standard web technologies such as JSON and HTML5. One such project is measuring affect data across the Internet using EyesWeb.

  Read more →
• 

  Light scanning photomacrography

  Light Scanning Photomacrography (LSP), also known as Scanning Light Photomacrography (SLP) or Deep-Field Photomacrography, is a photographic film technique that allows for high magnification light imaging with exceptional depth of field (DOF). This method overcomes the limitations of conventional macro photography, which typically only keeps a portion of the subject in acceptable focus at high magnifications. == Historical background == The principles of LSP were first documented in the early 1960s by Dan McLachlan Jr., who highlighted its capability for extreme focal depth in microscopy and in 1968 patented the process. The technique was revived and further developed in the 1980s by photographers such as Darwin Dale and Nile Root, a faculty member at the Rochester Institute of Technology. In the early 1990s, William Sharp and Charles Kazilek, both researchers at Arizona State University, also published articles describing their technique and system setup for capturing SLP images. == Predecessor to stack image photography == Light Scanning Photomacrography offered a powerful analog tool for high-detail imaging in the age of film photography. It provided a comprehensive depth of field, making it invaluable in scientific and biomedical photography. As technology and techniques continue to evolve, LSP has been replaced by digital image focus stacking. This technique uses a collection of images captured in series at different focal depths, which are then processed using computer software to create a single image with a greater focus depth than any single image. == LSP technique and results == LSP involves the use of a thin plane of light that scans across the subject, which is mounted on a stage moving perpendicular to the film plane. The technique utilizes traditional optics and is governed by the physical laws of depth of field. By moving the subject through a narrow band of illumination, the entire subject can be recorded in sharp focus from the nearest details to the farthest ones. This analog process produces sharp and detailed images by slowly recording the image on film as the specimen passes through the sheet of light that is thinner than the effective DOF. Because the image is captured at the same relative distance from the camera lens, the resulting images are axonometric rather than perspective projection, which is what the human eye sees and is typically captured by a film camera. Because all parts of an LSP image are captured at the same distance from the lens, relative measurements can be taken from an LSP photograph and can be used for comparison. == Equipment and setup == A typical LSP setup includes: A stage that can move the subject perpendicular to the film plane. Light sources, in some cases modified projectors, are used to project a thin plane of light. A camera mounted on a stable stand such as a tabletop copy stand. In 1991, Sharp and Kazilek described their SLP system that used three Kodak Ektagraphic slide projectors with zoom lenses to create a thin plane of light. The projectors each had a slide mount with two razor blades placed edge-to-edge to create a thin slit for the light to pass through. The image was captured using a Nikon FE-2 SLR camera mounted above the specimen. Kodachrome 25 slide film was used to record the image and to minimize film grain size and maximize image sharpness == Commercial systems == A commercial SLP instrument was produced by the Irvine Optical Corp. Their DYNAPHOT system was based on a photomacroscope and could capture images on 4x5 film. The instrument came with two or three illumination sources and a motorized specimen stage. The system advertised a 2X – 40X magnification range and the ability to capture images in black and white and color. Other systems have been developed by Nile Root and Theodore Clarke and reported higher magnification (up to 100X). == LSP process == Alignment and Focusing: The light sources are aligned and focused to project a thin, consistent plane of light across the subject. Stage Movement: The subject stage moves at a controlled speed, scanning through the plane of light. Image Capture: The camera shutter is set to a long exposure or can be opened and closed manually. As the subject moves through the illuminated plane, it is recorded on the film. This process is very much like painting an image onto the film using photons instead of paint. == Applications == LSP was particularly useful in biomedical photography, where it was used to document magnified subjects with increased depth of field over traditional macro and micro photography. It has been employed to capture detailed images of biological specimens, such as imaging small insects and their parts. SLP has been used to document shell collections for scientific documentation and research. Other applications include forensic science, mineralogy, and the imaging of fractured surfaces and parts == Advantages and challenges of LSP imaging == === Advantages === Exceptional depth of field: Subjects are rendered in sharp focus throughout. High magnification: Detailed images at significant magnification without sacrificing DOF. Analog precision: Provides a non-digital solution with accurate image representation. Versatility: Can be used for a range of subject sizes, from macro to non-macro scales. === Challenges === Technical complexity: Requires precise setup and alignment. Exposure time: Typically requires long exposure times due to the scanning process. Contrast control: The highly directional lighting can create harsh shadows and high contrast, which may need to be managed. Digital competition: Focus stacking has largely replaced LSP in the digital era due to convenience and flexibility. == DIY contributions == Enthusiasts and researchers have contributed to the development and accessibility of LSP by creating and sharing DIY guides. These contributions have enabled others to build their own LSP systems using readily available materials and components. Nile Root's publications provide detailed instructions and recommendations for constructing an LSP setup. These DIY systems have allowed a wider audience to explore and utilize the benefits of LSP imaging in various fields.

  Read more →
• 

  Project workforce management

  Project workforce management is the practice of combining the coordination of all logistic elements of a project through a single software application (or workflow engine). This includes planning and tracking of schedules and mileposts, cost and revenue, resource allocation, as well as overall management of these project elements. Efficiency is improved by eliminating manual processes, like spreadsheet tracking to monitor project progress. It also allows for at-a-glance status updates and ideally integrates with existing legacy applications in order to unify ongoing projects, enterprise resource planning (ERP) and broader organizational goals. There are a lot of logistic elements in a project. Different team members are responsible for managing each element and often, the organisation may have a mechanism to manage some logistic areas as well. By coordinating these various components of project management, workforce management and financials through a single solution, the process of configuring and changing project and workforce details is simplified. == Introduction == A project workforce management system defines project tasks, project positions, and assigns personnel to the project positions. The project tasks and positions are correlated to assign a responsible project position or even multiple positions to complete each project task. Because each project position may be assigned to a specific person, the qualifications and availabilities of that person can be taken into account when determining the assignment. By associating project tasks and project positions, a manager can better control the assignment of the workforce and complete the project more efficiently. When it comes to project workforce management, it is all about managing all the logistic aspects of a project or an organisation through a software application. Usually, this software has a workflow engine defined. Therefore, all the logistic processes take place in the workflow engine. == About == === Technical field === This invention relates to project management systems and methods, more particularly to a software-based system and method for project and workforce management. === Software usage === Due to the software usage, all the project workflow management tasks can be fully automated without leaving many tasks for the project managers. This returns high efficiency to the project management when it comes to project tracking proposes. In addition to different tracking mechanisms, project workforce management software also offer a dashboard for the project team. Through the dashboard, the project team has a glance view of the overall progress of the project elements. Most of the times, project workforce management software can work with the existing legacy software systems such as ERP (enterprise resource planning) systems. This easy integration allows the organisation to use a combination of software systems for management purposes. === Background === Good project management is an important factor for the success of a project. A project may be thought of as a collection of activities and tasks designed to achieve a specific goal of the organisation, with specific performance or quality requirements while meeting any subject time and cost constraints. Project management refers to managing the activities that lead to the successful completion of a project. Furthermore, it focuses on finite deadlines and objectives. A number of tools may be used to assist with this as well as with assessment. Project management may be used when planning personnel resources and capabilities. The project may be linked to the objects in a professional services life cycle and may accompany the objects from the opportunity over quotation, contract, time and expense recording, billing, period-end-activities to the final reporting. Naturally the project gets even more detailed when moving through this cycle. For any given project, several project tasks should be defined. Project tasks describe the activities and phases that have to be performed in the project such as writing of layouts, customising, testing. What is needed is a system that allows project positions to be correlated with project tasks. Project positions describe project roles like project manager, consultant, tester, etc. Project-positions are typically arranged linearly within the project. By correlating project tasks with project positions, the qualifications and availability of personnel assigned to the project positions may be considered. == Benefits of project management == Good project management should: Reduce the chance of a project failing Ensure a minimum level of quality and that results meet requirements and expectations Free up other staff members to get on with their area of work and increase efficiency both on the project and within the business Make things simpler and easier for staff with a single point of contact running the overall project Encourage consistent communications amongst staff and suppliers Keep costs, timeframes and resources to budget == Workflow engine == When it comes to project workforce management, it is all about managing all the logistic aspects of a project or an organisation through a software application. Usually, this software has a workflow engine defined in them. So, all the logistic processes take place in the workflow engine. The regular and most common types of tasks handled by project workforce management software or a similar workflow engine are: === Planning and monitoring project schedules and milestones === Regularly monitoring your project's schedule performance can provide early indications of possible activity-coordination problems, resource conflicts, and possible cost overruns. To monitor schedule performance. Collecting information and evaluating it ensure a project accuracy. The project schedule outlines the intended result of the project and what's required to bring it to completion. In the schedule, we need to include all the resources involved and cost and time constraints through a work breakdown structure (WBS). The WBS outlines all the tasks and breaks them down into specific deliverables. === Tracking the cost and revenue aspects of projects === The importance of tracking actual costs and resource usage in projects depends upon the project situation. Tracking actual costs and resource usage is an essential aspect of the project control function. === Resource utilisation and monitoring === Organisational profitability is directly connected to project management efficiency and optimal resource utilisation. To sum up, organisations that struggle with either or both of these core competencies typically experience cost overruns, schedule delays and unhappy customers. The focus for project management is the analysis of project performance to determine whether a change is needed in the plan for the remaining project activities to achieve the project goals. == Other management aspects of project management == === Project risk management === Risk identification consists of determining which risks are likely to affect the project and documenting the characteristics of each. === Project communication management === Project communication management is about how communication is carried out during the course of the project === Project quality management === It is of no use completing a project within the set time and budget if the final product is of poor quality. The project manager has to ensure that the final product meets the quality expectations of the stakeholders. This is done by good: Quality planning: Identifying what quality standards are relevant to the project and determining how to meet them. Quality assurance: Evaluating overall project performance on a regular basis to provide confidence that the project will satisfy the relevant quality standards. Quality control: Monitoring specific project results to determine if they comply with relevant quality standards and identifying ways to remove causes of poor performance. == Project workforce management vs. traditional management == There are three main differences between Project Workforce Management and traditional project management and workforce management disciplines and solutions: === Workflow-driven === All project and workforce processes are designed, controlled and audited using a built-in graphical workflow engine. Users can design, control and audit the different processes involved in the project. The graphical workflow is quite attractive for the users of the system and allows the users to have a clear idea of the workflow engine. === Organisation and work breakdown structures === Project Workforce Management provides organization and work breakdown structures to create, manage and report on functional and approval hierarchies, and to track information at any level of detail. Users can create, manage, edit and report work breakdown structures. Work breakdown structures have different abstraction

  Read more →
• 

  Pointer algorithm

  In computer science, a pointer algorithm (sometimes called a pointer machine, or a reference machine; see the article Pointer machine for a close but non-identical concept) is a type of algorithm that manages a linked data structure. This concept is used as a model for lower-bound proofs and specific restrictions on the linked data structure and on the algorithm's access to the structure vary. This model has been used extensively with problems related to the disjoint-set data structure. Thus, Tarjan and La Poutré used this model to prove lower bounds on the amortized complexity of a disjoint-set data structure (La Poutré also addressed the interval split-find problem). Blum used this model to prove a lower bound on the single operation worst-case time of disjoint set data structure. Blum and Rochow proved a worst-case lower bound for the interval union-find problem. == Example == In Tarjan's lower bound for the disjoint set union problem, the assumptions on the algorithm are: The algorithm maintains a linked structure of nodes. Each element of the problem is associated with a node. Each set is represented by a node. The nodes of each set constitute a distinct connected component in the structure (this property is called separability). The find operation is performed by following links from the element node to the set node. Under these assumptions, the lower bound of Ω ( m α ( m , n ) ) {\displaystyle \Omega (m\alpha (m,n))} on the cost of a sequence of m operations is proven.

  Read more →