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  • Corel VideoStudio

    Corel VideoStudio

    Corel VideoStudio (formerly Ulead VideoStudio) is a video editing software package for Microsoft Windows. == Features == === Basic editing === The software allows storyboard and timeline-oriented editing. Various formats are supported for source clips, and the resulting video can be exported to a video file. DVD and AVCHD DVD authoring capabilities are included, and Blu-ray authoring is available via a plug-in. VideoStudio supports direct DV and HDV capture and burning. === Overlay === Users can overlay videos, images, and text. Using the overlay track, up to 50 clips can be displayed simultaneously. It can handle videos in MOV and AVI formats, including alpha channel, and images in PSP, PSD, PNG, and GIF formats. Clips that do not contain an alpha channel can have specific colours removed from the overlay video so that the required background or image is displayed in the foreground. === Proxy video files === VideoStudio supports high-definition video. Proxy files are smaller versions of the video source that stand in for the full-resolution source during editing to improve performance. === Plug-ins/bundles === VideoStudio supports VFX-type plug-ins from providers, including NewBlue and proDAD. proDAD plug-ins Roto-Pen, Script, Vitascene, and Mercalli-Stabilizer are bundled with X4 and later Ultimate Editions. == Version history == Ulead VideoStudio 4 (1999) Ulead VideoStudio 5 (2001) Ulead VideoStudio 6 (2002) Ulead VideoStudio 7 (2003) Ulead VideoStudio 8 (2004) Ulead VideoStudio 9 (2005) Ulead VideoStudio 10 plus. (2006) Corel Ulead VideoStudio 11 plus. (2007) Corel VideoStudio Pro X2 (v12, 2008) Corel VideoStudio Pro X3 (v13, 2010) 2011: Corel VideoStudio Pro X4 (v14, 2011) Adds support for stop motion animation, time-lapse mode photography, 3D movies, and 2nd generation Intel Core. Corel VideoStudio Pro X5 (v15, March 9, 2012): Adds HTML5 export (Comparison of HTML5 and Flash). Corel VideoStudio Pro X6 (v16, April 25, 2013): Windows 8 compatible. Adds UHD 4K support. Corel VideoStudio Pro X7 (v17, March 5, 2014): Software becomes 64-bit. Corel VideoStudio Pro X8 (v18, May 8, 2015): Several improvements. Corel VideoStudio Pro X9 (v19, February 16, 2016): Windows 10 compatible. Adds H.265 support, Multi-Camera Editor, and Match moving. Corel VideoStudio Pro X10 (v20, February 15, 2017): Adds Mask Creator, Track Transparency, and 360-degree video support. Corel VideoStudio Pro 2018 (v21, February 13, 2018): Adds split screen Video, Lens Correction, and 3D Title Editor. Corel VideoStudio Pro 2019 (v22, February 12, 2019): Adds Color Grading, Morph Transitions, and MultiCam Capture Lite. Corel VideoStudio Pro 2020 (v23, February 25, 2020). Corel VideoStudio Pro 2021 (v24, March 26, 2021): Adds Instant Project Templates, AR Stickers, and performance improvements (particularly regarding hardware acceleration). Corel VideoStudio Pro 2022 (v25, March 6, 2022): Adds face effects, GIF Creator, transitions for Camera Movements, a speech to text converter, and ProRes Smart Proxy.

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  • Ontology engineering

    Ontology engineering

    In computer science, information science and systems engineering, ontology engineering is a field which studies the methods and methodologies for building ontologies, which encompasses a representation, formal naming and definition of the categories, properties and relations between the concepts, data and entities of a given domain of interest. In a broader sense, this field also includes a knowledge construction of the domain using formal ontology representations such as OWL/RDF. A large-scale representation of abstract concepts such as actions, time, physical objects and beliefs would be an example of ontological engineering. Ontology engineering is one of the areas of applied ontology, and can be seen as an application of philosophical ontology. Core ideas and objectives of ontology engineering are also central in conceptual modeling. Ontology engineering aims at making explicit the knowledge contained within software applications, and within enterprises and business procedures for a particular domain. Ontology engineering offers a direction towards solving the inter-operability problems brought about by semantic obstacles, i.e. the obstacles related to the definitions of business terms and software classes. Ontology engineering is a set of tasks related to the development of ontologies for a particular domain. Automated processing of information not interpretable by software agents can be improved by adding rich semantics to the corresponding resources, such as video files. One of the approaches for the formal conceptualization of represented knowledge domains is the use of machine-interpretable ontologies, which provide structured data in, or based on, RDF, RDFS, and OWL. Ontology engineering is the design and creation of such ontologies, which can contain more than just the list of terms (controlled vocabulary); they contain terminological, assertional, and relational axioms to define concepts (classes), individuals, and roles (properties) (TBox, ABox, and RBox, respectively). Ontology engineering is a relatively new field of study concerning the ontology development process, the ontology life cycle, the methods and methodologies for building ontologies, and the tool suites and languages that support them. A common way to provide the logical underpinning of ontologies is to formalize the axioms with description logics, which can then be translated to any serialization of RDF, such as RDF/XML or Turtle. Beyond the description logic axioms, ontologies might also contain SWRL rules. The concept definitions can be mapped to any kind of resource or resource segment in RDF, such as images, videos, and regions of interest, to annotate objects, persons, etc., and interlink them with related resources across knowledge bases, ontologies, and LOD datasets. This information, based on human experience and knowledge, is valuable for reasoners for the automated interpretation of sophisticated and ambiguous contents, such as the visual content of multimedia resources. Application areas of ontology-based reasoning include, but are not limited to, information retrieval, automated scene interpretation, and knowledge discovery. == Languages == An ontology language is a formal language used to encode the ontology. There are a number of such languages for ontologies, both proprietary and standards-based: Common logic is ISO standard 24707, a specification for a family of ontology languages that can be accurately translated into each other. The Cyc project has its own ontology language called CycL, based on first-order predicate calculus with some higher-order extensions. The Gellish language includes rules for its own extension and thus integrates an ontology with an ontology language. IDEF5 is a software engineering method to develop and maintain usable, accurate, domain ontologies. KIF is a syntax for first-order logic that is based on S-expressions. Rule Interchange Format (RIF), F-Logic and its successor ObjectLogic combine ontologies and rules. OWL is a language for making ontological statements, developed as a follow-on from RDF and RDFS, as well as earlier ontology language projects including OIL, DAML and DAML+OIL. OWL is intended to be used over the World Wide Web, and all its elements (classes, properties and individuals) are defined as RDF resources, and identified by URIs. OntoUML is a well-founded language for specifying reference ontologies. SHACL (RDF SHapes Constraints Language) is a language for describing structure of RDF data. It can be used together with RDFS and OWL or it can be used independently from them. XBRL (Extensible Business Reporting Language) is a syntax for expressing business semantics. == Methodologies and tools == DOGMA KAON OntoClean HOZO Protégé (software) Large language models == In life sciences == Life sciences is flourishing with ontologies that biologists use to make sense of their experiments. For inferring correct conclusions from experiments, ontologies have to be structured optimally against the knowledge base they represent. The structure of an ontology needs to be changed continuously so that it is an accurate representation of the underlying domain. Recently, an automated method was introduced for engineering ontologies in life sciences such as Gene Ontology (GO), one of the most successful and widely used biomedical ontology. Based on information theory, it restructures ontologies so that the levels represent the desired specificity of the concepts. Similar information theoretic approaches have also been used for optimal partition of Gene Ontology. Given the mathematical nature of such engineering algorithms, these optimizations can be automated to produce a principled and scalable architecture to restructure ontologies such as GO. Open Biomedical Ontologies (OBO), a 2006 initiative of the U.S. National Center for Biomedical Ontology, provides a common 'foundry' for various ontology initiatives, amongst which are: The Generic Model Organism Project (GMOD) Gene Ontology Consortium Sequence Ontology Ontology Lookup Service The Plant Ontology Consortium Standards and Ontologies for Functional Genomics and more

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  • Automatic image annotation

    Automatic image annotation

    Automatic image annotation (also known as automatic image tagging or linguistic indexing) is the process by which a computer system automatically assigns metadata in the form of captioning or keywords to a digital image. This application of computer vision techniques is used in image retrieval systems to organize and locate images of interest from a database. This method can be regarded as a type of multi-class image classification with a very large number of classes - as large as the vocabulary size. Typically, image analysis in the form of extracted feature vectors and the training annotation words are used by machine learning techniques to attempt to automatically apply annotations to new images. The first methods learned the correlations between image features and training annotations. Subsequently, techniques were developed using machine translation to attempt to translate the textual vocabulary into the 'visual vocabulary,' represented by clustered regions known as blobs. Subsequent work has included classification approaches, relevance models, and other related methods. The advantages of automatic image annotation versus content-based image retrieval (CBIR) are that queries can be more naturally specified by the user. At present, Content-Based Image Retrieval (CBIR) generally requires users to search by image concepts such as color and texture or by finding example queries. However, certain image features in example images may override the concept that the user is truly focusing on. Traditional methods of image retrieval, such as those used by libraries, have relied on manually annotated images, which is expensive and time-consuming, especially given the large and constantly growing image databases in existence.

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  • Information

    Information

    Information is an abstract concept that refers to something which has the power to inform. At the most fundamental level, it pertains to the interpretation (perhaps formally) of that which may be sensed, or their abstractions. Any natural process that is not completely random and any observable pattern in any medium can be said to convey some amount of information. Whereas digital signals and other data use discrete signs to convey information, other phenomena and artifacts such as analogue signals, poems, pictures, music or other sounds, and currents convey information in a more continuous form. Information is not knowledge itself, but the meaning that may be derived from a representation through interpretation. The concept of information is relevant to and connected with various concepts, including constraint, communication, control, data, form, education, knowledge, meaning, understanding, mental stimuli, pattern, perception, proposition, representation, and entropy. Information is often processed iteratively: Data available at one step are processed into information to be interpreted and processed at the next step. For example, in written text each symbol or letter conveys information relevant to the word it is part of, each word conveys information relevant to the phrase it is part of, each phrase conveys information relevant to the sentence it is part of, and so on until at the final step information is interpreted and becomes knowledge in a given domain. In a digital signal, bits may be interpreted into the symbols, letters, numbers, or structures that convey the information available at the next level up. The key characteristic of information is that it is subject to interpretation and processing. The derivation of information from a signal or message may be thought of as the resolution of ambiguity or uncertainty that arises during the interpretation of patterns within the signal or message. Information may be structured as data. Redundant data can be compressed up to an optimal size, which is the theoretical limit of compression. The information available through a collection of data may be derived by analysis. For example, a restaurant collects data from every customer order. That information may be analyzed to produce knowledge that is put to use when the business subsequently wants to identify the most popular or least popular dish. Information can be transmitted in time, via data storage, and space, via communication and telecommunication. Information is expressed either as the content of a message or through direct or indirect observation. That which is perceived can be construed as a message in its own right, and in that sense, all information is always conveyed as the content of a message. Information can be encoded into various forms for transmission and interpretation (for example, information may be encoded into a sequence of signs, or transmitted via a signal). It can also be encrypted for safe storage and communication. The uncertainty of an event is measured by its probability of occurrence. Uncertainty is proportional to the negative logarithm of the probability of occurrence. Information theory takes advantage of this by concluding that more uncertain events require more information to resolve their uncertainty. The bit is the standard unit of information. It is 'that which reduces uncertainty by half'. Other units such as the nat may be used. For example, the information encoded in one "fair" coin flip is log2(2/1) = 1 bit, and in two fair coin flips is log2(4/1) = 2 bits. A 2011 Science article estimates that 97% of technologically stored information was already in digital bits in 2007 and that the year 2002 was the beginning of the digital age for information storage (with digital storage capacity bypassing analogue for the first time). == Etymology and history of the concept == The English word "information" comes from Middle French enformacion/informacion/information 'a criminal investigation' and its etymon, Latin informatiō(n) 'conception, teaching, creation'. In English, "information" is an uncountable mass noun. References on "formation or molding of the mind or character, training, instruction, teaching" date from the 14th century in both English (according to Oxford English Dictionary) and other European languages. In the transition from Middle Ages to Modernity the use of the concept of information reflected a fundamental turn in epistemological basis – from "giving a (substantial) form to matter" to "communicating something to someone". Peters (1988, pp. 12–13) concludes: Information was readily deployed in empiricist psychology (though it played a less important role than other words such as impression or idea) because it seemed to describe the mechanics of sensation: objects in the world inform the senses. But sensation is entirely different from "form" – the one is sensual, the other intellectual; the one is subjective, the other objective. My sensation of things is fleeting, elusive, and idiosyncratic. For Hume, especially, sensory experience is a swirl of impressions cut off from any sure link to the real world... In any case, the empiricist problematic was how the mind is informed by sensations of the world. At first informed meant shaped by; later it came to mean received reports from. As its site of action drifted from cosmos to consciousness, the term's sense shifted from unities (Aristotle's forms) to units (of sensation). Information came less and less to refer to internal ordering or formation, since empiricism allowed for no preexisting intellectual forms outside of sensation itself. Instead, information came to refer to the fragmentary, fluctuating, haphazard stuff of sense. Information, like the early modern worldview in general, shifted from a divinely ordered cosmos to a system governed by the motion of corpuscles. Under the tutelage of empiricism, information gradually moved from structure to stuff, from form to substance, from intellectual order to sensory impulses. In the modern era, the most important influence on the concept of information is derived from the Information theory developed by Claude Shannon and others. This theory, however, reflects a fundamental contradiction. Northrup (1993) wrote: Thus, actually two conflicting metaphors are being used: The well-known metaphor of information as a quantity, like water in the water-pipe, is at work, but so is a second metaphor, that of information as a choice, a choice made by :an information provider, and a forced choice made by an :information receiver. Actually, the second metaphor implies that the information sent isn't necessarily equal to the information received, because any choice implies a comparison with a list of possibilities, i.e., a list of possible meanings. Here, meaning is involved, thus spoiling the idea of information as a pure "Ding an sich." Thus, much of the confusion regarding the concept of information seems to be related to the basic confusion of metaphors in Shannon's theory: is information an autonomous quantity, or is information always per SE information to an observer? Actually, I don't think that Shannon himself chose one of the two definitions. Logically speaking, his theory implied information as a subjective phenomenon. But this had so wide-ranging epistemological impacts that Shannon didn't seem to fully realize this logical fact. Consequently, he continued to use metaphors about information as if it were an objective substance. This is the basic, inherent contradiction in Shannon's information theory." (Northrup, 1993, p. 5). In their seminal book The Study of Information: Interdisciplinary Messages, Almach and Mansfield (1983) collected key views on the interdisciplinary controversy in computer science, artificial intelligence, library and information science, linguistics, psychology, and physics, as well as in the social sciences. Almach (1983, p. 660) himself disagrees with the use of the concept of information in the context of signal transmission, the basic senses of information in his view all referring "to telling something or to the something that is being told. Information is addressed to human minds and is received by human minds." All other senses, including its use with regard to nonhuman organisms as well to society as a whole, are, according to Machlup, metaphoric and, as in the case of cybernetics, anthropomorphic. Hjørland (2007) describes the fundamental difference between objective and subjective views of information and argues that the subjective view has been supported by, among others, Bateson, Yovits, Span-Hansen, Brier, Buckland, Goguen, and Hjørland. Hjørland provided the following example: A stone on a field could contain different information for different people (or from one situation to another). It is not possible for information systems to map all the stone's possible information for every individual. Nor is any one mapping the one "true" mapping. But peop

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  • Integrated test facility

    Integrated test facility

    An integrated test facility (ITF) creates a fictitious entity in a database to process test transactions simultaneously with live input. ITF can be used to incorporate test transactions into a normal production run of a system. Its advantage is that periodic testing does not require separate test processes. However, careful planning is necessary, and test data must be isolated from production data. Moreover, ITF validates the correct operation of a transaction in an application, but it does not ensure that a system is being operated correctly. Integrated test facility is considered a useful audit tool during an IT audit because it uses the same programs to compare processing using independently calculated data. This involves setting up dummy entities on an application system and processing test or production data against the entity as a means of verifying processing accuracy.

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  • Behavior selection algorithm

    Behavior selection algorithm

    In artificial intelligence, a behavior selection algorithm, or action selection algorithm, is an algorithm that selects appropriate behaviors or actions for one or more intelligent agents. In game artificial intelligence, it selects behaviors or actions for one or more non-player characters. Common behavior selection algorithms include: Finite-state machines Hierarchical finite-state machines Decision trees Behavior trees Hierarchical task networks Hierarchical control systems Utility systems Dialogue tree (for selecting what to say) == Related concepts == In application programming, run-time selection of the behavior of a specific method is referred to as the strategy design pattern.

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  • Information explosion

    Information explosion

    Information explosion is the rapid increase in the amount of published information or data and the effects of this abundance. As the amount of available data grows, the problem of managing the information becomes more difficult, which can lead to information overload. The Online Oxford English Dictionary indicates use of the phrase in a March 1964 New Statesman article. The New York Times first used the phrase in its editorial content in an article by Walter Sullivan on June 7, 1964, in which he described the phrase as "much discussed". The earliest known use of the phrase was in a speech about television by NBC president Pat Weaver at the Institute of Practitioners of Advertising in London on September 27, 1955. The speech was rebroadcast on radio station WSUI in Iowa City and excerpted in the Daily Iowan newspaper two months later. Many sectors are seeing this rapid increase in the amount of information available such as healthcare, supermarkets, and governments. Another sector that is being affected by this phenomenon is journalism. Such a profession, which in the past was responsible for the dissemination of information, may be suppressed by the overabundance of information today. Techniques to gather knowledge from an overabundance of electronic information (e.g., data fusion may help in data mining) have existed since the 1970s. Another common technique to deal with such amount of information is qualitative research. Such approaches aim to organize the information, synthesizing, categorizing and systematizing in order to be more usable and easier to search. == Growth patterns == The world's technological capacity to store information grew from, optimally compressed, 2.6 exabytes in 1986 to 15.7 in 1993, over 54.5 in 2000, and to 295 exabytes in 2007. The world's technological capacity to receive information through one-way broadcast networks was 432 exabytes of (optimally compressed) information in 1986, 715 (optimally compressed) exabytes in 1993, 1,200 (optimally compressed) exabytes in 2000, and 1,900 in 2007. The world's effective capacity to exchange information through two-way telecommunications networks was 0.281 exabytes of (optimally compressed) information in 1986, 0.471 in 1993, 2.2 in 2000, and 65 (optimally compressed) exabytes in 2007. A new metric that is being used in an attempt to characterize the growth in person-specific information, is the disk storage per person (DSP), which is measured in megabytes/person (where megabytes is 106 bytes and is abbreviated MB). Global DSP (GDSP) is the total rigid disk drive space (in MB) of new units sold in a year divided by the world population in that year. The GDSP metric is a crude measure of how much disk storage could possibly be used to collect person-specific data on the world population. In 1983, one million fixed drives with an estimated total of 90 terabytes were sold worldwide; 30MB drives had the largest market segment. In 1996, 105 million drives, totaling 160,623 terabytes were sold with 1 and 2 gigabyte drives leading the industry. By the year 2000, with 20GB drive leading the industry, rigid drives sold for the year are projected to total 2,829,288 terabytes Rigid disk drive sales to top $34 billion in 1997. According to Latanya Sweeney, there are three trends in data gathering today: Type 1. Expansion of the number of fields being collected, known as the “collect more” trend. Type 2. Replace an existing aggregate data collection with a person-specific one, known as the “collect specifically” trend. Type 3. Gather information by starting a new person-specific data collection, known as the “collect it if you can” trend. == Related terms == Since "information" in electronic media is often used synonymously with "data", the term information explosion is closely related to the concept of data flood (also dubbed data deluge). Sometimes the term information flood is used as well. All of those basically boil down to the ever-increasing amount of electronic data exchanged per time unit. A term that covers the potential negative effects of information explosion is information inflation. The awareness about non-manageable amounts of data grew along with the advent of ever more powerful data processing since the mid-1960s. == Challenges == Even though the abundance of information can be beneficial in several levels, some problems may be of concern such as privacy, legal and ethical guidelines, filtering and data accuracy. Filtering refers to finding useful information in the middle of so much data, which relates to the job of data scientists. A typical example of a necessity of data filtering (data mining) is in healthcare since in the next years is due to have EHRs (Electronic Health Records) of patients available. With so much information available, the doctors will need to be able to identify patterns and select important data for the diagnosis of the patient. On the other hand, according to some experts, having so much public data available makes it difficult to provide data that is actually anonymous. Another point to take into account is the legal and ethical guidelines, which relates to who will be the owner of the data and how frequently he/she is obliged to the release this and for how long. With so many sources of data, another problem will be accuracy of such. An untrusted source may be challenged by others, by ordering a new set of data, causing a repetition in the information. According to Edward Huth, another concern is the accessibility and cost of such information. The accessibility rate could be improved by either reducing the costs or increasing the utility of the information. The reduction of costs according to the author, could be done by associations, which should assess which information was relevant and gather it in a more organized fashion. == Web servers == As of August 2005, there were over 70 million web servers. As of September 2007 there were over 135 million web servers. == Blogs == According to Technorati, the number of blogs doubles about every 6 months with a total of 35.3 million blogs as of April 2006. This is an example of the early stages of logistic growth, where growth is approximately exponential, since blogs are a recent innovation. As the number of blogs approaches the number of possible producers (humans), saturation occurs, growth declines, and the number of blogs eventually stabilizes.

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  • Reservoir sampling

    Reservoir sampling

    Reservoir sampling is a family of randomized algorithms for choosing a simple random sample, without replacement, of k items from a population of unknown size n in a single pass over the items. The size of the population n is not known to the algorithm and is typically too large for all n items to fit into main memory. The population is revealed to the algorithm over time, and the algorithm cannot look back at previous items. At any point, the current state of the algorithm must permit extraction of a simple random sample without replacement of size k over the part of the population seen so far. == Motivation == Suppose we see a sequence of items, one at a time. We want to keep 10 items in memory, and we want them to be selected at random from the sequence. If we know the total number of items n and can access the items arbitrarily, then the solution is easy: select 10 distinct indices i between 1 and n with equal probability, and keep the i-th elements. The problem is that we do not always know the exact n in advance. == Simple: Algorithm R == A simple and popular but slow algorithm, Algorithm R, was created by Jeffrey Vitter. Initialize an array R {\displaystyle R} indexed from 1 {\displaystyle 1} to k {\displaystyle k} , containing the first k items of the input x 1 , . . . , x k {\displaystyle x_{1},...,x_{k}} . This is the reservoir. For each new input x i {\displaystyle x_{i}} , generate a random number j uniformly in { 1 , . . . , i } {\displaystyle \{1,...,i\}} . If j ∈ { 1 , . . . , k } {\displaystyle j\in \{1,...,k\}} , then set R [ j ] := x i . {\displaystyle R[j]:=x_{i}.} Otherwise, discard x i {\displaystyle x_{i}} . Return R {\displaystyle R} after all inputs are processed. This algorithm works by induction on i ≥ k {\displaystyle i\geq k} . While conceptually simple and easy to understand, this algorithm needs to generate a random number for each item of the input, including the items that are discarded. The algorithm's asymptotic running time is thus O ( n ) {\displaystyle O(n)} . Generating this amount of randomness and the linear run time causes the algorithm to be unnecessarily slow if the input population is large. This is Algorithm R, implemented as follows: == Optimal: Algorithm L == If we generate n {\displaystyle n} random numbers u 1 , . . . , u n ∼ U [ 0 , 1 ] {\displaystyle u_{1},...,u_{n}\sim U[0,1]} independently, then the indices of the smallest k {\displaystyle k} of them is a uniform sample of the k {\displaystyle k} -subsets of { 1 , . . . , n } {\displaystyle \{1,...,n\}} . The process can be done without knowing n {\displaystyle n} : Keep the smallest k {\displaystyle k} of u 1 , . . . , u i {\displaystyle u_{1},...,u_{i}} that has been seen so far, as well as w i {\displaystyle w_{i}} , the index of the largest among them. For each new u i + 1 {\displaystyle u_{i+1}} , compare it with u w i {\displaystyle u_{w_{i}}} . If u i + 1 < u w i {\displaystyle u_{i+1} Read more →

  • CPT Corporation

    CPT Corporation

    CPT Corporation was founded in 1971 by Dean Scheff in Minneapolis, Minnesota, with co-founders James Wienhold and Richard Eichhorn. CPT first designed, manufactured, and marketed the CPT 4200, a dual-cassette-tape machine that controlled a modified IBM Selectric typewriter to support text editing and word processing. The CPT 4200 was followed in 1976 by the CPT VM (Visual Memory), a partial-page display-screen dual-cassette-tape unit, and shortly thereafter by the CPT 8000, a full-page display dual-diskette desktop microcomputer that drove stand-alone daisy wheel printers. Subsequent products included (1) variants on the 8000 series; (2) the CPT 6000 series, which had a lower capacity, smaller screen, and was less expensive; (3) the CPT 9000 series, which had a larger capacity and could run IBM personal computer software; (4) the CPT Phoenix series, which had a graphical capabilities; (5) CPT PT, a software-only reduced version that ran on IBM personal computers and clones; and (6) other related products. The CPT logo—originally three letters chosen to sound well together—began to be taken as an acronym for "cassette powered typewriting," and subsequently for "computer processed text," and numerous other variants. Major competition was IBM, Wang, Lanier, Xerox, and other word processing vendors. CPT Corporation was fifth in size among Minnesota-based top high-tech companies, after 3M, Honeywell, Control Data, and Medtronic. Corporate revenues grew to approximately a quarter-billion dollars per year in the mid-1980s, then declined with the proliferation of personal computers. CPT ultimately ceased major manufacturing late in the 20th century. == Selected products == === Cassette based === The CPT 4200 was a dual-cassette-tape unit with a small built-in keyboard that controlled a modified IBM Selectric typewriter. Keystrokes entered on the typewriter appeared on the paper as they were recorded on the output cassette, which formed a magnetic replica of the characters printed on the page. That output cassette could later be used as an input cassette, where it would be played back to the typewriter along with new keystrokes to accomplish text editing. The keyboard of the CPT 4200 had action keys for "skip", "read" and "stop", mode keys for "word", "line", "paragraph," and "page." Pressing "read" transferred a word, line, paragraph, or page (depending on which mode key had been selected) from the input tape to both the typewriter and the output tape. Line boundaries (aka printer margins) recorded on the input tape were ignored or retained depending on whether or not the "adjust" key had been selected. Alternatively, pressing "skip" moved past the corresponding amount of text on the input tape without sending it to the typewriter or to the output tape. The Selectric's keyboard was active for any new typing, which would appear on the paper and transferred to the output tape. Thus a document was edited by reading back those parts of the text to be retained and skipping those parts to be discarded, with new typing added from the Selectric's keyboard. Price: approx. $5000, 1980-era values. The CPT Communicator was an add-on to the CPT 4200 that allowed data to be transferred from one text-editing machine to another, or between a text-editing machine and a remote computer, via phone lines. Price: not available. === Microprocessor based === ==== CPT 8000 series ==== The CPT 8000 was the company's first microcomputer product, exhibited in spring of 1976. It was a self-contained desktop machine with two 8-inch floppy diskette drives, a movable keyboard, and a full-page vertically oriented CRT display simulating paper with black characters on a white background, for a wysiwyg view of text on paper. It was promoted as familiar and easy to use for those experienced with typewriters. A keyboard with a large set of extra keys made operating the 8000 quite easy even for people without any computer skills or background. IN, OUT, PRINT, OOPS OOPS was changed thinking it was insulting to the buyer to assume they would ever make an error. The CPT 8000 was designed to show a full page of text with a static line showing the margin and tab stops. An additional line would display status or error messages with a times square like display. The times square error and status messages were very well done, "The printer needs a new ribbon" rather than "ERROR 034892". The text page could both smooth pan and scroll by the hardware in the display board and nothing quite like it existed for a very long time. The 8000 ran its own multitasking hardware interrupt-driven operating system but it also ran CP/M quite well. So unlike other companies that sold Wordprocessor only systems, CPT had a system that could run any of the many popular CP/M applications. Using the CP/M OS users could develop Fortran, CBasic, Cobol and other language's programs. The 8000 used Intel's 8080 microprocessor. The display board was bleeding-edge, high-speed logic. The parts available at this time were pushed to their limits to provide the speed needed to display this much text. There were times that batches of parts from one manufacturer simply could not be clocked as fast as the 8000 display required. Memory was initially 64K, but larger boards of 128K were most common then later 256K were offered. The 8080 accessed this additional RAM by running a custom page flipping circuit. The 8000 was originally priced at $8000 and its daisy wheel printer an additional $8000. The model number having been confused with the price at its first appearance at the Hanover fair. An RS-232 serial communication option was available for the 8000 series that allowed the electronic transfer of documents. One very popular use of this was to access the Westlaw system. A tempest approved version of the 8000 was developed that was RF tight with nothing being emitted that could be monitored or spied on. === Storage Systems === ==== CPT WordPak ==== The CPT WordPak series was CPT's first external document storage system that enabled multiple 8000 series workstations to store documents in an electronic filing cabinet. Prior to WordPak, all documents were stored on removable 8-inch floppy diskettes. Sharing documents involved handing off the original disk, or copying the document to a second disk and 'sneaker-net-ing' (walking it over) to the second 8000. But this resulted in two copies of the document, one at each workstation. A circuit board with a proprietary cable connector was installed in the 8000/6000 family of "workstations" and connected to the WordPak by a multi-conductor cable. WordPak 1 consisted of a single Shugart Associates SA4000 14"-diameter hard disk with a capacity of 30 megabytes. WordPak 2 added a 2nd drive for a total of 60 megabytes. ==== CPT SRS 45 ==== The CPT SRS 45 was what would now be called a server (quite likely the first of its kind) but in practice was much more. It was maybe the worlds easiest networking shared resource system. It combined a ZIP drive for backup and hard disk(s) that would be shared simultaneously by up to eight CPT machines that had the PC AT bus. The primary person responsible for its development was Bill Davidson whose wife Cheryl was responsible for bringing up CP/M, MP/M and other Digital Research products running on the Phoenix. The brilliance of the system were the networking cards that plugged into the individual machines. These used the 55AA installable driver of the IBM BIOS to simply add the zip and hard disk drives to each computers drives list. So a system that started with floppy drives A and B and a C hard disk on the machine would have the SRS 45 drives added as drives D (E, F depending on the number of hard disk) and Z for the zip drive. Sharing (avoiding writing to the same file at the same time) was handled by simply assigning parts of the drives for individuals and other directories for shared use. No "driver" software was needed. You simply plugged in the networking card and your machine had additional drives that were internal to the SRS45. This approach was far ahead of its time and sadly never recognized for its brilliance. The SRS45 as were all CPT machines not just dedicated Word Processors. === Personal-computer based === ==== CPT PT software ==== CPT PT was a reduced a version of the software that ran under MS-DOS as an application on IBM PC compatible computers. The corporation intended it as a bridge to allow data to flow in and out of personal computer packages, as well as providing a personal-computer word processing application for those familiar with standalone CPT equipment or who preferred the CPT style of dual-window text editing. Price: approx. $200, 1980-era values. ==== CPT Genius Display ==== The Genius display was a stand-alone, vertically-oriented (portrait) configuration monochrome grey-scale CRT monitor unit and an IBM PC form factor display card to allow high-resolution, full-page text & graphics on IBM PC compatible computers.

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  • Enterprise bus matrix

    Enterprise bus matrix

    The enterprise bus matrix is a data warehouse planning tool and model created by Ralph Kimball, and is part of the data warehouse bus architecture. The matrix is the logical definition of one of the core concepts of Kimball's approach to dimensional modeling conformed dimension. The bus matrix defines part of the data warehouse bus architecture and is an output of the business requirements phase in the Kimball lifecycle. It is applied in the following phases of dimensional modeling and development of the data warehouse. The matrix can be categorized as a hybrid model, being part technical design tool, part project management tool and part communication tool == Background == The need for an enterprise bus matrix stems from the way one goes about creating the overall data warehouse environment. Historically there have been two approaches: a structured, centralized and planned approach and a more loosely defined, department specific approach, in which solutions are developed in a more independent matter. Autonomous projects can result in a range of isolated stove pipe data marts. Naturally each approach has its issues; the visionary approach often struggles with long delivery cycles and lack of reaction time as needs emerge and scope issues arise. On the other hand, the development of isolated data marts leads to stovepipe systems that lack synergy in development. Over time this approach will lead to a so-called data-mart-in-a-box architecture where interoperability and lack of cohesion is apparent, and can hinder the realization of an overall enterprise data warehouse. As an attempt to handle this issue, Ralph Kimball introduced the enterprise bus. == Description == The bus matrix purpose is one of high abstraction and visionary planning on the data warehouse architectural level. By dictating coherency in the development and implementation of an overall data warehouse the bus architecture approach enables an overall vision of the broader enterprise integration and consistency while at the same time dividing the problem into more manageable parts – all in a technology and software independent manner. The bus matrix and architecture builds upon the concept of conformed dimensions, creating a structure of common dimensions that ideally can be used across the enterprise by all business processes related to the data warehouse and the corresponding fact tables from which they derive their context. According to Kimball and Margy Ross's article “Differences of Opinion” "The Enterprise Data warehouse built on the bus architecture ”identifies and enforces the relationship between business process metrics (facts) and descriptive attributes (dimensions)”. The concept of a bus is well known in the language of information technology, and is what reflects the conformed dimension concept in the data warehouse, creating the skeletal structure where all parts of a system connect, ensuring interoperability and consistency of data, and at the same time considers future expansion. This makes the conformed dimensions act as the integration ‘glue’, creating a robust backbone of the enterprise Data Warehouse.

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  • Broadcast (parallel pattern)

    Broadcast (parallel pattern)

    Broadcast is a collective communication primitive in parallel programming to distribute programming instructions or data to nodes in a cluster. It is the reverse operation of reduction. The broadcast operation is widely used in parallel algorithms, such as matrix-vector multiplication, Gaussian elimination and shortest paths. The Message Passing Interface implements broadcast in MPI_Bcast. == Definition == A message M [ 1.. m ] {\displaystyle M[1..m]} of length m {\displaystyle m} should be distributed from one node to all other p − 1 {\displaystyle p-1} nodes. T byte {\displaystyle T_{\text{byte}}} is the time it takes to send one byte. T start {\displaystyle T_{\text{start}}} is the time it takes for a message to travel to another node, independent of its length. Therefore, the time to send a package from one node to another is t = s i z e × T byte + T start {\displaystyle t=\mathrm {size} \times T_{\text{byte}}+T_{\text{start}}} . p {\displaystyle p} is the number of nodes and the number of processors. == Binomial Tree Broadcast == With Binomial Tree Broadcast the whole message is sent at once. Each node that has already received the message sends it on further. This grows exponentially as each time step the amount of sending nodes is doubled. The algorithm is ideal for short messages but falls short with longer ones as during the time when the first transfer happens only one node is busy. Sending a message to all nodes takes log 2 ⁡ ( p ) t {\displaystyle \log _{2}(p)t} time which results in a runtime of log 2 ⁡ ( p ) ( m T byte + T start ) {\displaystyle \log _{2}(p)(mT_{\text{byte}}+T_{\text{start}})} == Linear Pipeline Broadcast == The message is split up into k {\displaystyle k} packages and sent piecewise from node n {\displaystyle n} to node n + 1 {\displaystyle n+1} . The time needed to distribute the first message piece is p t = m k T byte + T start {\textstyle pt={\frac {m}{k}}T_{\text{byte}}+T_{\text{start}}} whereby t {\displaystyle t} is the time needed to send a package from one processor to another. Sending a whole message takes ( p + k ) ( m T byte k + T start ) = ( p + k ) t = p t + k t {\displaystyle (p+k)\left({\frac {mT_{\text{byte}}}{k}}+T_{\text{start}}\right)=(p+k)t=pt+kt} . Optimal is to choose k = m ( p − 2 ) T byte T start {\displaystyle k={\sqrt {\frac {m(p-2)T_{\text{byte}}}{T_{\text{start}}}}}} resulting in a runtime of approximately m T byte + p T start + m p T start T byte {\displaystyle mT_{\text{byte}}+pT_{\text{start}}+{\sqrt {mpT_{\text{start}}T_{\text{byte}}}}} The run time is dependent on not only message length but also the number of processors that play roles. This approach shines when the length of the message is much larger than the amount of processors. == Pipelined Binary Tree Broadcast == This algorithm combines Binomial Tree Broadcast and Linear Pipeline Broadcast, which makes the algorithm work well for both short and long messages. The aim is to have as many nodes work as possible while maintaining the ability to send short messages quickly. A good approach is to use Fibonacci trees for splitting up the tree, which are a good choice as a message cannot be sent to both children at the same time. This results in a binary tree structure. We will assume in the following that communication is full-duplex. The Fibonacci tree structure has a depth of about d ≈ log Φ ⁡ ( p ) {\displaystyle d\approx \log _{\Phi }(p)} whereby Φ = 1 + 5 2 {\displaystyle \Phi ={\frac {1+{\sqrt {5}}}{2}}} the golden ratio. The resulting runtime is ( m k T byte + T start ) ( d + 2 k − 2 ) {\textstyle ({\frac {m}{k}}T_{\text{byte}}+T_{\text{start}})(d+2k-2)} . Optimal is k = n ( d − 2 ) T byte 3 T start {\displaystyle k={\sqrt {\frac {n(d-2)T_{\text{byte}}}{3T_{\text{start}}}}}} . This results in a runtime of 2 m T byte + T start log Φ ⁡ ( p ) + 2 m log Φ ⁡ ( p ) T start T byte {\displaystyle 2mT_{\text{byte}}+T_{\text{start}}\log _{\Phi }(p)+{\sqrt {2m\log _{\Phi }(p)T_{\text{start}}T_{\text{byte}}}}} . == Two Tree Broadcast (23-Broadcast) == === Definition === This algorithm aims to improve on some disadvantages of tree structure models with pipelines. Normally in tree structure models with pipelines (see above methods), leaves receive just their data and cannot contribute to send and spread data. The algorithm concurrently uses two binary trees to communicate over. Those trees will be called tree A and B. Structurally in binary trees there are relatively more leave nodes than inner nodes. Basic Idea of this algorithm is to make a leaf node of tree A be an inner node of tree B. It has also the same technical function in opposite side from B to A tree. This means, two packets are sent and received by inner nodes and leaves in different steps. === Tree construction === The number of steps needed to construct two parallel-working binary trees is dependent on the amount of processors. Like with other structures one processor can is the root node who sends messages to two trees. It is not necessary to set a root node, because it is not hard to recognize that the direction of sending messages in binary tree is normally top to bottom. There is no limitation on the number of processors to build two binary trees. Let the height of the combined tree be h = ⌈log(p + 2)⌉. Tree A and B can have a height of h − 1 {\displaystyle h-1} . Especially, if the number of processors correspond to p = 2 h − 1 {\displaystyle p=2^{h}-1} , we can make both sides trees and a root node. To construct this model efficiently and easily with a fully built tree, we can use two methods called "Shifting" and "Mirroring" to get second tree. Let assume tree A is already modeled and tree B is supposed to be constructed based on tree A. We assume that we have p {\displaystyle p} processors ordered from 0 to p − 1 {\displaystyle p-1} . ==== Shifting ==== The "Shifting" method, first copies tree A and moves every node one position to the left to get tree B. The node, which will be located on -1, becomes a child of processor p − 2 {\displaystyle p-2} . ==== Mirroring ==== "Mirroring" is ideal for an even number of processors. With this method tree B can be more easily constructed by tree A, because there are no structural transformations in order to create the new tree. In addition, a symmetric process makes this approach simple. This method can also handle an odd number of processors, in this case, we can set processor p − 1 {\displaystyle p-1} as root node for both trees. For the remaining processors "Mirroring" can be used. === Coloring === We need to find a schedule in order to make sure that no processor has to send or receive two messages from two trees in a step. The edge, is a communication connection to connect two nodes, and can be labelled as either 0 or 1 to make sure that every processor can alternate between 0 and 1-labelled edges. The edges of A and B can be colored with two colors (0 and 1) such that no processor is connected to its parent nodes in A and B using edges of the same color- no processor is connected to its children nodes in A or B using edges of the same color. In every even step the edges with 0 are activated and edges with 1 are activated in every odd step. === Time complexity === In this case the number of packet k is divided in half for each tree. Both trees are working together the total number of packets k = k / 2 + k / 2 {\displaystyle k=k/2+k/2} (upper tree + bottom tree) In each binary tree sending a message to another nodes takes 2 i {\displaystyle 2i} steps until a processor has at least a packet in step i {\displaystyle i} . Therefore, we can calculate all steps as d := log 2 ⁡ ( p + 1 ) ⇒ log 2 ⁡ ( p + 1 ) ≈ log 2 ⁡ ( p ) {\displaystyle d:=\log _{2}(p+1)\Rightarrow \log _{2}(p+1)\approx \log _{2}(p)} . The resulting run time is T ( m , p , k ) ≈ ( m k T byte + T start ) ( 2 d + k − 1 ) {\textstyle T(m,p,k)\approx ({\frac {m}{k}}T_{\text{byte}}+T_{\text{start}})(2d+k-1)} . (Optimal k = m ( 2 d − 1 ) T byte / T start {\textstyle k={\sqrt {{m(2d-1)T_{\text{byte}}}/{T_{\text{start}}}}}} ) This results in a run time of T ( m , p ) ≈ m T byte + T start ⋅ 2 log 2 ⁡ ( p ) + m ⋅ 2 log 2 ⁡ ( p ) T start T byte {\displaystyle T(m,p)\approx mT_{\text{byte}}+T_{\text{start}}\cdot 2\log _{2}(p)+{\sqrt {m\cdot 2\log _{2}(p)T_{\text{start}}T_{\text{byte}}}}} . == ESBT-Broadcasting (Edge-disjoint Spanning Binomial Trees) == In this section, another broadcasting algorithm with an underlying telephone communication model will be introduced. A Hypercube creates network system with p = 2 d ( d = 0 , 1 , 2 , 3 , . . . ) {\displaystyle p=2^{d}(d=0,1,2,3,...)} . Every node is represented by binary 0 , 1 {\displaystyle {0,1}} depending on the number of dimensions. Fundamentally ESBT(Edge-disjoint Spanning Binomial Trees) is based on hypercube graphs, pipelining( m {\displaystyle m} messages are divided by k {\displaystyle k} packets) and binomial trees. The Processor 0 d {\displaystyle 0^{d}} cyclically spreads packets to roots of ESB

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  • Transliteracy

    Transliteracy

    Transliteracy is "a fluidity of movement across a range of technologies, media and contexts". It is an ability to use diverse techniques to collaborate across different social groups. Transliteracy combines a range of capabilities required to move across a range of contexts, media, technologies and genres. Conceptually, transliteracy is situated across five capabilities: information capabilities (see information literacy), ICT (information and communication technologies), communication and collaboration, creativity and critical thinking. It is underpinned by literacy and numeracy. (See figure below) The concept of transliteracy is impacting the system of education and libraries. == History == While the term appears to come from the prefix trans- ('across') and the word literacy, the scholars who coined it say they developed it from the practice of transliteration, which means to use the letters of one language to write down a different language. The study of transliteracy was first developed in 2005 by the Transliteracies Research Project, directed by University of California at Santa Barbara Professor Alan Liu. The concept of 'transliteracies' was developed as part of research into online reading. It was shared and refined at the Transliteracies conference, held at UC Santa Barbara in 2005. The conference inspired the at the time De Montfort University Professor, Sue Thomas, to create the Production in Research and Transliteracy (PART) group, which evolved into the Transliteracy Research Group. The current meaning of transliteracy was defined in the group's seminal paper Transliteracy: crossing divides as "the ability to read, write, and interact across a range of platforms, tools, and media from signing and orality through handwriting, print, TV, radio, and film, to digital social networks." The concept was enthusiastically adopted by a number of professional groups, notably in the library and information field. Transliteracy Research Group Archive 2006–2013 curates numerous resources from this period. For a number of years, there was a gap between significant interest in transliteracy among professional groups and the scarcity of research. A group of academics from the University of Bordeaux considered transliteracy mainly in the school context. Freelance writer and consultant, Sue Thomas, studied transliteracy and creativity, while Suzana Sukovic, executive director of educational research and evidence-based practice at HETI, researched transliteracy in relation to digital storytelling. The first book on the topic, Transliteracy in complex information environment by Sukovic, is based on research and experience with practice-based projects. == Transliteracy in education == Transliteracy is making an impact on the classroom setting because of how technologically advanced younger generations are today. In 2012, Adam Marcus, a teacher and librarian at the New York City Department of Education (NYCDOE), decided to incorporate transliteracy into his school's public library summer reading program. He had a desire to enhance the experience of reading for his students by allowing them to connect to the text differently by using social media. He used a tool called VoiceThread in order to have his students "take part in conversations, formulate ideas, and share higher-order thinking through a variety of media channels: video, audio, text, images, and music". Students were also enabled to communicate with the book's author through blogs and websites, and were given multiple modes of media to comprehend and engage with the text on a deeper level. Some of these examples include an audio-video glossary and web links that aimed to bring the details of the text to life. The results of his experiment were deemed to have a positive effect on the program as students responded well to this interactive experience they were given. Marcus believes that it is important for educators and librarians to enhance storytelling for children by providing them with a modern and transliterate experience that one could not receive back then. The Agence nationale de la recherche funded a program at a French high school from 2013 to 2015, where the transliteracy skills of students were tested and observed. Students were placed in groups of three or four members and were required to use all sorts of media and tools in order to collect data for their projects. They were not allowed to only use digital sources, and were advised to use a diversity of sources. The focus of this experiment was to observe "the possible diversity of media and tools employed, on the ways of and reasons for switching from one to another, on how these different media and tools are distributed within contexts, according to the academic requirements and tasks individually and collectively performed by the students." The conclusions of the experiment dealt with physical space and organization being an issue for students and teachers to deal with. Spatially, it was challenging for students to navigate through different mediums when their space inside the classroom was limited. It was noticed that students were prone to use something that took up less space, rather than focusing on expanding their diversity of sources. Organizationally, it was challenging for students to organize all of the information they collected since everything was not being search and collected for digitally. In addition, students were not allotted a lot of time to complete their projects which also impacted their final product. == Transliteracy in libraries == In 2009, Dr. Susie Andretta, senior lecturer in Information Management at London Metropolitan University, conducted interviews with four different information professionals including an academic librarian, an outreach librarian, a content manager, and a scholar within the library science and information discipline. She was aiming to explore how transliteracy was colliding and combining with the print-world of libraries. Dr. Andretta defines transliteracy as "an umbrella term encompassing different literacies and multiple communication channels that require active participation with and across a range of platforms, and embracing both linear and non-linear messages (3)." The goals of these interviews ranged from the following: to test the information professional's awareness of transliteracy, to have them identify transliteracy and how it is integrated into their work, and to explain the impact transliteracy has had on they library they work at. Andretta found that out of all the information professionals interviewed, it was only the academic librarian who was vaguely familiar with the concept of transliteracy. Bernadette Daly Swanson, an Academic Librarian at UC Davis, expresses in her interview with Dr. Andretta how she would "like to think that the transliterate library is more of an environment where we do different things [...] I would take maybe about a third of the first floor of our library and transform it into a lab [...] where we can start to evolve [..] explore, and experiment in media development, content development, and do it not just with librarians; so open up the space for other people [...] so you don't get people working in isolation." Although the other three candidates that Dr. Andretta interviewed had not heard of the term transliteracy, they responded well to the concept once it was explained to them and agreed with its impact on the workplace. Dr. Michael Stephens, an assistant professor in the Graduate School of Library and Information Science at Dominican University, explains in his interview how the term transliteracy describes the courses he teaches on libraries and Web 2.0 technologies. Dr. Stephens states that students being educated in Web 2.0 technologies gives them "the opportunity to experience what the channel can be and the potential for that sharing learning, for asking questions, just for out loud thinking – I think it's incredibly valuable. [..] this is where this wonderful concept comes in, it was teaching them transliteracy and the fact that they can move across channels without getting worried about it." Dr. Andretta concluded from her interviews how although transliteracy may not be a very well-known term yet, it has nonetheless established itself into the intuition of libraries while also transforming the traditional library to a world of enhanced and expanded services. "Inherent in this transition are the challenges of having to adapt to a constantly changing technological landscape, the multiple literacies that this generates, and the need to establish a multifaceted library profession that can speak the multiple-media languages of its diverse users." Thomas Ipri, a librarian at the University of Nevada, advocates for libraries needing to make a change in their literary functions. He argues that the divide between digital and print makes it harder for libraries to accommodate their patrons and to share information. He f

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  • Microsoft Support Diagnostic Tool

    Microsoft Support Diagnostic Tool

    The Microsoft Support Diagnostic Tool (MSDT) is a legacy service in Microsoft Windows that allows Microsoft technical support agents to analyze diagnostic data remotely for troubleshooting purposes. In April 2022 it was observed to have a security vulnerability that allowed remote code execution which was being exploited to attack computers in Russia and Belarus, and later against the Tibetan government in exile. Microsoft advised a temporary workaround of disabling the MSDT by editing the Windows registry. == Use == When contacting support the user is told to run MSDT and given a unique "passkey" which they enter. They are also given an "incident number" to uniquely identify their case. The MSDT can also be run offline which will generate a .CAB file which can be uploaded from a computer with an internet connection. == Security vulnerabilities == === Follina === Follina is the name given to a remote code execution (RCE) vulnerability, a type of arbitrary code execution (ACE) exploit, in the Microsoft Support Diagnostic Tool (MSDT) which was first widely publicized on May 27, 2022, by a security research group called Nao Sec. This exploit allows a remote attacker to use a Microsoft Office document template to execute code via MSDT. This works by exploiting the ability of Microsoft Office document templates to download additional content from a remote server. If the size of the downloaded content is large enough it causes a buffer overflow allowing a payload of Powershell code to be executed without explicit notification to the user. On May 30 Microsoft issued CVE-2022-30190 with guidance that users should disable MSDT. Malicious actors have been observed exploiting the bug to attack computers in Russia and Belarus since April, and it is believed Chinese state actors had been exploiting it to attack the Tibetan government in exile based in India. Microsoft patched this vulnerability in its June 2022 patches. === DogWalk === The DogWalk vulnerability is a remote code execution (RCE) vulnerability in the Microsoft Support Diagnostic Tool (MSDT). It was first reported in January 2020, but Microsoft initially did not consider it to be a security issue. However, the vulnerability was later exploited in the wild, and Microsoft released a patch for it in August 2022. The vulnerability is caused by a path traversal vulnerability in the sdiageng.dll library. This vulnerability allows an attacker to trick a victim into opening a malicious diagcab file, which is a type of Windows cabinet file that is used to store support files. When the diagcab file is opened, it triggers the MSDT tool, which then executes the malicious code. Originally discovered by Mitja Kolsek, the DogWalk vulnerability is caused by a path traversal vulnerability in the sdiageng.dll library. This vulnerability allows an attacker to trick a victim into opening a malicious diagcab file, which is a type of Windows cabinet file that is used to store support files. When the diagcab file is opened, it triggers the MSDT tool, which then executes the malicious code. The vulnerability is exploited by creating a malicious diagcab file that contains a specially crafted path. This path contains a sequence of characters that is designed to exploit the path traversal vulnerability in the sdiageng.dll library. When the diagcab file is opened, the MSDT tool will attempt to follow the path. However, the path will contain characters that are not valid for a Windows path. This will cause the MSDT tool to crash. When the MSDT tool crashes, it will generate a memory dump. This memory dump will contain the malicious code that was executed by the MSDT tool. The attacker can then use this memory dump to extract the malicious code and execute it on their own computer. == Retirement == Microsoft will no longer be supporting the Windows legacy inbox Troubleshooters. In 2025, Microsoft will remove the MSDT platform entirely. Get Help is the replacement tool. == Windows versions == Windows 7 Windows 8.1 Windows 10 Windows 11 (up to 22H2) Future versions and feature upgrades will deprecate the MSDT after May 23, 2023.

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  • Ballin' (Mustard and Roddy Ricch song)

    Ballin' (Mustard and Roddy Ricch song)

    "Ballin'" is a song by American record producer Mustard featuring American rapper Roddy Ricch. The track was released as the third single from Mustard's third studio album, Perfect Ten, on August 20, 2019, though it was available as early as the end of June 2019. The song and its accompanying video received acclaim from music critics, with Complex magazine naming it the Best Song of 2019. It peaked at number 11 on the Billboard Hot 100, marking Mustard's highest charting song in the US. The song received a nomination for Best Rap/Sung Performance at the 2020 Grammy Awards, making it the first time Ricch has been nominated for a Grammy and Mustard's first nomination as an artist. Later in 2019, the two released another collaboration, "High Fashion". == Background == Roddy Ricch revealed in an interview that the song was composed in late 2018, but Mustard wanted to keep it for his album, Perfect Ten, which he was still working on. The song was later included on the album, released in June 2019. Ricch said he knew the song was "hard enough" the first time he heard it, while Mustard proclaimed "this is going to be the one". == Composition and lyrics == "Ballin'" has a "rags to riches" theme. In its intro, the song samples girl group 702's 1997 top ten hit "Get It Together". The song features a "smooth, bouncy beat", with Roddy Ricch rapping about his come-up and ascent in the music industry. In the first verse, Ricch salutes fellow Los Angeles rapper, the late Nipsey Hussle and his girlfriend Lauren London: "I run these racks up with my queen like London and Nip". The line simultaneously references Ricch and Hussle's collaboration "Racks in the Middle", released earlier in 2019 as Hussle's last single before his death. Billboard's Heran Mamo noted that "in typical Hussle fashion", Roddy Ricch "narrates his life's hardships before delving into his newfound treasures". == Critical reception == The song was widely acclaimed by music critics. Charles Holmes of Rolling Stone magazine called it "a song of the year contender", while Complex and Billboard both named it as a "standout track" on the album. Pitchfork magazine included "Ballin'" in its list of The Best Rap Songs of 2019 and called it "the centerpiece of Mustard's underappreciated album Perfect Ten". Complex later named it the Best Song of 2019, calling it "a feel-good anthem so infectious you'll need antibiotics just to stop running it back". == Chart performance == "Ballin'" was at the time Mustard's highest charting song in the US, peaking at number 11 on the Billboard Hot 100. It was also Roddy Ricch's highest charting song, until he surpassed it a week later, with the release of his album track "The Box", which eventually reached number 1 on the chart. It reached number one on Billboard's Rhythmic Songs chart, becoming Mustard's second number one following "Pure Water" and Ricch's first number one. The song also topped the Rap Airplay chart. == Music video == The music video for the track was teased by Mustard on his Instagram page on September 29, 2019. The music video for the track was eventually released on October 2, 2019 to critical acclaim. The video features Mustard and Roddy Ricch driving a Lamborghini Aventador in Los Angeles, where they both are from, playing poker in a casino, and going to a strip club. This is contrasted with scenes in which Mustard and Roddy Ricch as children play cards with Monopoly money and playing with miniature toy Lamborghinis together, aspiring for wealth and luxury, representing how they went from "rags to riches". The video also pays tribute to rapper Nipsey Hussle, who had been killed a few months ago. == Live performances == On December 16, 2019, Roddy Ricch performed the song live, alongside an 8-piece orchestra, at Peppermint Club in Los Angeles for Audiomack's Trap Symphony series. Along with Mustard, he performed it at The Pop Out: Ken & Friends on June 19, 2024. == Other uses == The song can be heard on "Elyse's Skit", track 10 off Roddy Ricch's debut album Please Excuse Me for Being Antisocial. In the skit, which is an actual voicenote recording, the mother of a woman named Elyse sends her daughter a voicenote, with "Ballin'" playing in the background, while the mother proceeds to say "I can't get that damn song out my head", jokingly calling it "inappropriate music". Ricch called the skit "something natural". In 2023, AI covers of the song using models based on pop culture characters and real-world celebrities gained viral popularity. == Awards and nominations == 62nd Annual Grammy Awards == Charts == == Certifications ==

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  • Broadcast (parallel pattern)

    Broadcast (parallel pattern)

    Broadcast is a collective communication primitive in parallel programming to distribute programming instructions or data to nodes in a cluster. It is the reverse operation of reduction. The broadcast operation is widely used in parallel algorithms, such as matrix-vector multiplication, Gaussian elimination and shortest paths. The Message Passing Interface implements broadcast in MPI_Bcast. == Definition == A message M [ 1.. m ] {\displaystyle M[1..m]} of length m {\displaystyle m} should be distributed from one node to all other p − 1 {\displaystyle p-1} nodes. T byte {\displaystyle T_{\text{byte}}} is the time it takes to send one byte. T start {\displaystyle T_{\text{start}}} is the time it takes for a message to travel to another node, independent of its length. Therefore, the time to send a package from one node to another is t = s i z e × T byte + T start {\displaystyle t=\mathrm {size} \times T_{\text{byte}}+T_{\text{start}}} . p {\displaystyle p} is the number of nodes and the number of processors. == Binomial Tree Broadcast == With Binomial Tree Broadcast the whole message is sent at once. Each node that has already received the message sends it on further. This grows exponentially as each time step the amount of sending nodes is doubled. The algorithm is ideal for short messages but falls short with longer ones as during the time when the first transfer happens only one node is busy. Sending a message to all nodes takes log 2 ⁡ ( p ) t {\displaystyle \log _{2}(p)t} time which results in a runtime of log 2 ⁡ ( p ) ( m T byte + T start ) {\displaystyle \log _{2}(p)(mT_{\text{byte}}+T_{\text{start}})} == Linear Pipeline Broadcast == The message is split up into k {\displaystyle k} packages and sent piecewise from node n {\displaystyle n} to node n + 1 {\displaystyle n+1} . The time needed to distribute the first message piece is p t = m k T byte + T start {\textstyle pt={\frac {m}{k}}T_{\text{byte}}+T_{\text{start}}} whereby t {\displaystyle t} is the time needed to send a package from one processor to another. Sending a whole message takes ( p + k ) ( m T byte k + T start ) = ( p + k ) t = p t + k t {\displaystyle (p+k)\left({\frac {mT_{\text{byte}}}{k}}+T_{\text{start}}\right)=(p+k)t=pt+kt} . Optimal is to choose k = m ( p − 2 ) T byte T start {\displaystyle k={\sqrt {\frac {m(p-2)T_{\text{byte}}}{T_{\text{start}}}}}} resulting in a runtime of approximately m T byte + p T start + m p T start T byte {\displaystyle mT_{\text{byte}}+pT_{\text{start}}+{\sqrt {mpT_{\text{start}}T_{\text{byte}}}}} The run time is dependent on not only message length but also the number of processors that play roles. This approach shines when the length of the message is much larger than the amount of processors. == Pipelined Binary Tree Broadcast == This algorithm combines Binomial Tree Broadcast and Linear Pipeline Broadcast, which makes the algorithm work well for both short and long messages. The aim is to have as many nodes work as possible while maintaining the ability to send short messages quickly. A good approach is to use Fibonacci trees for splitting up the tree, which are a good choice as a message cannot be sent to both children at the same time. This results in a binary tree structure. We will assume in the following that communication is full-duplex. The Fibonacci tree structure has a depth of about d ≈ log Φ ⁡ ( p ) {\displaystyle d\approx \log _{\Phi }(p)} whereby Φ = 1 + 5 2 {\displaystyle \Phi ={\frac {1+{\sqrt {5}}}{2}}} the golden ratio. The resulting runtime is ( m k T byte + T start ) ( d + 2 k − 2 ) {\textstyle ({\frac {m}{k}}T_{\text{byte}}+T_{\text{start}})(d+2k-2)} . Optimal is k = n ( d − 2 ) T byte 3 T start {\displaystyle k={\sqrt {\frac {n(d-2)T_{\text{byte}}}{3T_{\text{start}}}}}} . This results in a runtime of 2 m T byte + T start log Φ ⁡ ( p ) + 2 m log Φ ⁡ ( p ) T start T byte {\displaystyle 2mT_{\text{byte}}+T_{\text{start}}\log _{\Phi }(p)+{\sqrt {2m\log _{\Phi }(p)T_{\text{start}}T_{\text{byte}}}}} . == Two Tree Broadcast (23-Broadcast) == === Definition === This algorithm aims to improve on some disadvantages of tree structure models with pipelines. Normally in tree structure models with pipelines (see above methods), leaves receive just their data and cannot contribute to send and spread data. The algorithm concurrently uses two binary trees to communicate over. Those trees will be called tree A and B. Structurally in binary trees there are relatively more leave nodes than inner nodes. Basic Idea of this algorithm is to make a leaf node of tree A be an inner node of tree B. It has also the same technical function in opposite side from B to A tree. This means, two packets are sent and received by inner nodes and leaves in different steps. === Tree construction === The number of steps needed to construct two parallel-working binary trees is dependent on the amount of processors. Like with other structures one processor can is the root node who sends messages to two trees. It is not necessary to set a root node, because it is not hard to recognize that the direction of sending messages in binary tree is normally top to bottom. There is no limitation on the number of processors to build two binary trees. Let the height of the combined tree be h = ⌈log(p + 2)⌉. Tree A and B can have a height of h − 1 {\displaystyle h-1} . Especially, if the number of processors correspond to p = 2 h − 1 {\displaystyle p=2^{h}-1} , we can make both sides trees and a root node. To construct this model efficiently and easily with a fully built tree, we can use two methods called "Shifting" and "Mirroring" to get second tree. Let assume tree A is already modeled and tree B is supposed to be constructed based on tree A. We assume that we have p {\displaystyle p} processors ordered from 0 to p − 1 {\displaystyle p-1} . ==== Shifting ==== The "Shifting" method, first copies tree A and moves every node one position to the left to get tree B. The node, which will be located on -1, becomes a child of processor p − 2 {\displaystyle p-2} . ==== Mirroring ==== "Mirroring" is ideal for an even number of processors. With this method tree B can be more easily constructed by tree A, because there are no structural transformations in order to create the new tree. In addition, a symmetric process makes this approach simple. This method can also handle an odd number of processors, in this case, we can set processor p − 1 {\displaystyle p-1} as root node for both trees. For the remaining processors "Mirroring" can be used. === Coloring === We need to find a schedule in order to make sure that no processor has to send or receive two messages from two trees in a step. The edge, is a communication connection to connect two nodes, and can be labelled as either 0 or 1 to make sure that every processor can alternate between 0 and 1-labelled edges. The edges of A and B can be colored with two colors (0 and 1) such that no processor is connected to its parent nodes in A and B using edges of the same color- no processor is connected to its children nodes in A or B using edges of the same color. In every even step the edges with 0 are activated and edges with 1 are activated in every odd step. === Time complexity === In this case the number of packet k is divided in half for each tree. Both trees are working together the total number of packets k = k / 2 + k / 2 {\displaystyle k=k/2+k/2} (upper tree + bottom tree) In each binary tree sending a message to another nodes takes 2 i {\displaystyle 2i} steps until a processor has at least a packet in step i {\displaystyle i} . Therefore, we can calculate all steps as d := log 2 ⁡ ( p + 1 ) ⇒ log 2 ⁡ ( p + 1 ) ≈ log 2 ⁡ ( p ) {\displaystyle d:=\log _{2}(p+1)\Rightarrow \log _{2}(p+1)\approx \log _{2}(p)} . The resulting run time is T ( m , p , k ) ≈ ( m k T byte + T start ) ( 2 d + k − 1 ) {\textstyle T(m,p,k)\approx ({\frac {m}{k}}T_{\text{byte}}+T_{\text{start}})(2d+k-1)} . (Optimal k = m ( 2 d − 1 ) T byte / T start {\textstyle k={\sqrt {{m(2d-1)T_{\text{byte}}}/{T_{\text{start}}}}}} ) This results in a run time of T ( m , p ) ≈ m T byte + T start ⋅ 2 log 2 ⁡ ( p ) + m ⋅ 2 log 2 ⁡ ( p ) T start T byte {\displaystyle T(m,p)\approx mT_{\text{byte}}+T_{\text{start}}\cdot 2\log _{2}(p)+{\sqrt {m\cdot 2\log _{2}(p)T_{\text{start}}T_{\text{byte}}}}} . == ESBT-Broadcasting (Edge-disjoint Spanning Binomial Trees) == In this section, another broadcasting algorithm with an underlying telephone communication model will be introduced. A Hypercube creates network system with p = 2 d ( d = 0 , 1 , 2 , 3 , . . . ) {\displaystyle p=2^{d}(d=0,1,2,3,...)} . Every node is represented by binary 0 , 1 {\displaystyle {0,1}} depending on the number of dimensions. Fundamentally ESBT(Edge-disjoint Spanning Binomial Trees) is based on hypercube graphs, pipelining( m {\displaystyle m} messages are divided by k {\displaystyle k} packets) and binomial trees. The Processor 0 d {\displaystyle 0^{d}} cyclically spreads packets to roots of ESB

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