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  • CineAsset

    CineAsset

    CineAsset was a complete mastering software suite by Doremi Labs that could create and playback encrypted (Pro version) and unencrypted DCI compliant packages from virtually any source. CineAsset included a separate "Editor" application for generating Digital Cinema Packages (DCPs). CineAsset Pro added the ability to generate encrypted DCPs and Key Delivery Messages (KDMs) for any encrypted content in the database. It has since been discontinued, along with CineAsset Player. == Features == == Supported formats == === Input === Source: ==== Containers ==== AVI MOV MXF MPG TS WMV M2TS MTS MP4 MKV ==== Video Codecs ==== JPEG2000 ProRes 422 DNxHD® YUV Uncompressed 8-10 bits DIVX® XVID® MPEG4 AVC / H-264 VC-1 MPEG2 ==== Image Sequences ==== BMP TIFF TGA DPX JPG J2C ==== Audio Files ==== WAV MP3 WMA MP2 === Output === Source: ==== JPEG2000 ==== 2D and 3D at up to 4K resolution Bit Rate: 50–250 Mbit/s (500 Mbit/s for frame rates above 30 fps) Speed: Faster than real-time processing when using optional render nodes ==== MPEG2 ==== I-Only or Long GOP 1080p up to 80 Mbit/s ==== H264 ==== 1080p up to 50 Mbit/s ==== VC1 ==== DCP wrapping only (no transcode)

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  • Gen (software)

    Gen (software)

    Gen is a Computer Aided Software Engineering (CASE) application development environment marketed by Broadcom Inc. Gen was previously known as CA Gen, IEF (Information Engineering Facility), Composer by IEF, Composer, COOL:Gen, Advantage:Gen and AllFusion Gen. The toolset originally supported the information technology engineering methodology developed by Clive Finkelstein, James Martin and others in the early 1980s. Early versions supported IBM's DB2 database, 3270 'block mode' screens and generated COBOL code. In the intervening years the toolset has been expanded to support additional development techniques such as component-based development; creation of client/server and web applications and generation of C, Java and C#. In addition, other platforms are now supported such as many variants of Unix-like Operating Systems (AIX, HP-UX, Solaris, Linux) as well as Windows. Its range of supported database technologies have widened to include ORACLE, Microsoft SQL Server, ODBC, JDBC as well as the original DB2. The toolset is fully integrated - objects identified during analysis carry forward into design without redefinition. All information is stored in a repository (central encyclopedia). The encyclopedia allows for large team development - controlling access so that multiple developers may not change the same object simultaneously. == History == === 1985-1997: Texas Instruments === It was initially produced by Texas Instruments, with input from James Martin and his consultancy firm James Martin Associates, and was based on the Information Engineering Methodology (IEM). The first version was launched in 1985. IEF (Information Engineering Facility) became popular among large government departments and public utilities. It initially supported a CICS/COBOL/DB2 target environment. However, it now supports a wider range of relational databases and operating systems. IEF was intended to shield the developer from the complexities of building complete multi-tier cross-platform applications. In 1995, Texas Instruments decided to change their marketing focus for the product. Part of this change included a new name - "Composer". By 1996, IEF had become a popular tool. However, it was criticized by some IT professionals for being too restrictive, as well as for having a high per-workstation cost ($15K USD). But it is claimed that IEF reduces development time and costs by removing complexity and allowing rapid development of large scale enterprise transaction processing systems. === 1997-2000: Sterling Software === In 1997, Composer had another change of branding, Texas Instruments sold the Texas Instruments Software division, including the Composer rights, to Sterling Software. Sterling software changed the well known name "Information Engineering Facility" to "COOL:Gen". COOL was an acronym for "Common Object Oriented Language" - despite the fact that there was little object orientation in the product. === 2000-2018: Computer Associates === In 2000, Sterling Software was acquired by Computer Associates (now CA). CA has rebranded the product three times to date and the product is still used widely today. Under CA, recent releases of the tool added support for the CA-Datacom DBMS, the Linux operating system, C# code generation and ASP.NET web clients. The current version is known as CA Gen - version 8 being released in May 2010, with support for customised web services, and more of the toolset being based around the Eclipse framework. === 2018-current: Broadcom === As of 2020, CA Gen is owned and marketed by Broadcom Inc., which rebranded the product to Gen to avoid confusion with the former owner of the product. There are a variety of "add-on" tools available for Gen, including GuardIEn - a Configuration Management and Developer Productivity Suite, QAT Wizard, an interview style wizard that takes advantage of the meta model in Gen, products for multi-platform application reporting and XML/SOAP enabling of Gen applications., and developer productivity tools such as Access Gen, APMConnect, QA Console and Upgrade Console from Response Systems Version 8.6 of CA Gen came to market in June 2016. Version 8.6.3 of CA Gen was released in 2021. Following this release, Broadcom have switched to a continuous delivery model with new features to be delivered as patches.

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  • Sieve of Pritchard

    Sieve of Pritchard

    In mathematics, the sieve of Pritchard is an algorithm for finding all prime numbers up to a specified bound. Like the ancient sieve of Eratosthenes, it has a simple conceptual basis in number theory. It is especially suited to quick hand computation for small bounds. Whereas the sieve of Eratosthenes marks off each non-prime for each of its prime factors, the sieve of Pritchard avoids considering almost all non-prime numbers by building progressively larger wheels, which represent the pattern of numbers not divisible by any of the primes processed thus far. It thereby achieves a better asymptotic complexity, and was the first sieve with a running time sublinear in the specified bound. Its asymptotic running-time has not been improved on, and it deletes fewer composites than any other known sieve. It was created in 1979 by Paul Pritchard. Since Pritchard has created a number of other sieve algorithms for finding prime numbers, the sieve of Pritchard is sometimes singled out by being called the wheel sieve (by Pritchard himself) or the dynamic wheel sieve. == Overview == A prime number is a natural number that has no natural number divisors other than the number 1 and itself. To find all the prime numbers less than or equal to a given integer N, a sieve algorithm examines a set of candidates in the range 2, 3, …, N, and eliminates those that are not prime, leaving the primes at the end. The sieve of Eratosthenes examines all of the range, first removing all multiples of the first prime 2, then of the next prime 3, and so on. The sieve of Pritchard instead examines a subset of the range consisting of numbers that occur on successive wheels, which represent the pattern of numbers left after each successive prime is processed by the sieve of Eratosthenes. For i > 0, the ith wheel Wi represents this pattern. It is the set of numbers between 1 and the product Pi = p1 · p2 ⋯ pi of the first i prime numbers that are not divisible by any of these prime numbers (and is said to have an associated length Pi). This is because adding Pi to a number does not change whether it is divisible by one of the first i prime numbers, since the remainder on division by any one of these primes is unchanged. So W1 = {1} with length P1 = 2 represents the pattern of odd numbers; W2 = {1,5} with length P2 = 6 represents the pattern of numbers not divisible by 2 or 3; etc. Wheels are so-called because Wi can be usefully visualized as a circle of circumference Pi with its members marked at their corresponding distances from an origin. Then rolling the wheel along the number line marks points corresponding to successive numbers not divisible by one of the first i prime numbers. The animation shows W2 being rolled up to 30. It is useful to define Wi → n for n > 0 to be the result of rolling Wi up to n. Then the animation generates W2 → 30 = {1,5,7,11,13,17,19,23,25,29}. Note that up to 52 − 1 = 24, this consists only of 1 and the primes between 5 and 25. The sieve of Pritchard is derived from the observation that this holds generally: for all i > 0, the values in Wi → (p2i+1 − 1) are 1 and the primes between pi+1 and p2i+1. It even holds for i = 0, where the wheel has length 1 and contains just 1 (representing all the natural numbers). So the sieve of Pritchard starts with the trivial wheel W0 and builds successive wheels until the square of the wheel's first member after 1 is at least N. Wheels grow very quickly, but only their values up to N are needed and generated. It remains to find a method for generating the next wheel. Note in the animation that W3 = {1,5,7,11,13,17,19,23,25,29} − {5 · 1 , 5 · 5} can be obtained by rolling W2 up to 30 and then removing 5 times each member of W2.This also holds generally: for all i ≥ 0, Wi+1 = (Wi → Pi+1) − {pi+1 · w | w ∈ Wi}. Rolling Wi past Pi just adds values to Wi, so the current wheel is first extended by getting each successive member starting with w = 1, adding Pi to it, and inserting the result in the set. Then the multiples of pi+1 are deleted. Care must be taken to avoid a number being deleted that itself needs to be multiplied by pi+1. The sieve of Pritchard as originally presented does so by first skipping past successive members until finding the maximum one needed, and then doing the deletions in reverse order by working back through the set. This is the method used in the first animation above. A simpler approach is just to gather the multiples of pi+1 in a list, and then delete them. Another approach is given by Gries and Misra. If the main loop terminates with a wheel whose length is less than N, it is extended up to N to generate the remaining primes. The algorithm, for finding all primes up to N, is therefore as follows: Start with a set W = {1} and length = 1 representing wheel 0, and prime p = 2. As long as p2 ≤ N, do the following: if length < N, then extend W by repeatedly getting successive members w of W starting with 1 and inserting length + w into W as long as it does not exceed p · length or N; increase length to the minimum of p · length and N. repeatedly delete p times each member of W by first finding the largest ≤ length and then working backwards. note the prime p, then set p to the next member of W after 1 (or 3 if p was 2). if length < N, then extend W to N by repeatedly getting successive members w of W starting with 1 and inserting length + w into W as long as it does not exceed N; On termination, the rest of the primes up to N are the members of W after 1. === Example === To find all the prime numbers less than or equal to 150, proceed as follows. Start with wheel 0 with length 1, representing all natural numbers 1, 2, 3...: 1 The first number after 1 for wheel 0 (when rolled) is 2; note it as a prime. Now form wheel 1 with length 2 × 1 = 2 by first extending wheel 0 up to 2 and then deleting 2 times each number in wheel 0, to get: 1 2 The first number after 1 for wheel 1 (when rolled) is 3; note it as a prime. Now form wheel 2 with length 3 × 2 = 6 by first extending wheel 1 up to 6 and then deleting 3 times each number in wheel 1, to get 1 2 3 5 The first number after 1 for wheel 2 is 5; note it as a prime. Now form wheel 3 with length 5 × 6 = 30 by first extending wheel 2 up to 30 and then deleting 5 times each number in wheel 2 (in reverse order), to get 1 2 3 5 7 11 13 17 19 23 25 29 The first number after 1 for wheel 3 is 7; note it as a prime. Now wheel 4 has length 7 × 30 = 210, so we only extend wheel 3 up to our limit 150. (No further extending will be done now that the limit has been reached.) We then delete 7 times each number in wheel 3 until we exceed our limit 150, to get the elements in wheel 4 up to 150: 1 2 3 5 7 11 13 17 19 23 25 29 31 37 41 43 47 49 53 59 61 67 71 73 77 79 83 89 91 97 101 103 107 109 113 119 121 127 131 133 137 139 143 149 The first number after 1 for this partial wheel 4 is 11; note it as a prime. Since we have finished with rolling, we delete 11 times each number in the partial wheel 4 until we exceed our limit 150, to get the elements in wheel 5 up to 150: 1 2 3 5 7 11 13 17 19 23 25 29 31 37 41 43 47 49 53 59 61 67 71 73 77 79 83 89 91 97 101 103 107 109 113 119 121 127 131 133 137 139 143 149 The first number after 1 for this partial wheel 5 is 13. Since 13 squared is at least our limit 150, we stop. The remaining numbers (other than 1) are the rest of the primes up to our limit 150. Just 8 composite numbers are removed, once each. The rest of the numbers considered (other than 1) are prime. In comparison, the natural version of Eratosthenes sieve (stopping at the same point) removes composite numbers 184 times. == Pseudocode == The sieve of Pritchard can be expressed in pseudocode, as follows: algorithm Sieve of Pritchard is input: an integer N >= 2. output: the set of prime numbers in {1,2,...,N}. let W and Pr be sets of integer values, and all other variables integer values. k, W, length, p, Pr := 1, {1}, 2, 3, {2}; {invariant: p = pk+1 and W = Wk ∩ {\displaystyle \cap } {1,2,...,N} and length = minimum of Pk,N and Pr = the primes up to pk} while p2 <= N do if (length < N) then Extend W,length to minimum of plength,N; Delete multiples of p from W; Insert p into Pr; k, p := k+1, next(W, 1) if (length < N) then Extend W,length to N; return Pr ∪ {\displaystyle \cup } W - {1}; where next(W, w) is the next value in the ordered set W after w. procedure Extend W,length to n is {in: W = Wk and length = Pk and n > length} {out: W = Wk → {\displaystyle \rightarrow } n and length = n} integer w, x; w, x := 1, length+1; while x <= n do Insert x into W; w := next(W,w); x := length + w; length := n; procedure Delete multiples of p from W,length is integer w; w := p; while pw <= length do w := next(W,w); while w > 1 do w := prev(W,w); Remove pw from W; where prev(W, w) is the previous value in the ordered set W before w. The algorithm can be initialized with W0 instead of W1 at the minor complication of making next(W, 1) a special case when k = 0. This a

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  • Interviewer effect

    Interviewer effect

    The interviewer effect (also called interviewer variance or interviewer error) is the distortion of response to an interviewer-administered data collection effort which results from differential reactions to the social style and personality of interviewers or to their presentation of particular questions. The use of fixed-wording questions is one method of reducing interviewer bias. Anthropological research and case-studies are also affected by the problem, which is exacerbated by the self-fulfilling prophecy, when the researcher is also the interviewer it is also any effect on data gathered from interviewing people that is caused by the behavior or characteristics (real or perceived) of the interviewer. Interviewer effects can also be associated with the characteristics of the interviewer, such as race. Whether black respondents are interviewed by white interviewers or black interviewers has a strong impact on their responses to both attitude questions and behavioral ones. In the latter case, for example, if black respondents are interviewed by black interviewers in pre-election surveys, they are more likely to actually vote in the upcoming election than if they are interviewed by white interviewers. Furthermore, the race of the interviewer can also affect answers to factual questions that might take the form of a test of how informed the respondent is. Black respondents in a survey of political knowledge, for example, get fewer correct answers to factual questions about politics when interviewed by white interviewers than when interviewed by black interviewers. This is consistent with the research literature on stereotype threat, which finds diminished test performance of potentially stigmatised groups when the interviewer or test supervisor is from a perceived higher status group. Interviewer effects can be mitigated somewhat by randomly assigning subjects to different interviewers, or by using tools such as computer-assisted telephone interviewing (CATI).

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  • Desktop Window Manager

    Desktop Window Manager

    Desktop Window Manager (DWM, previously Desktop Compositing Engine or DCE in builds of pre-reset Windows Longhorn) is the compositing window manager in Microsoft Windows since Windows Vista that enables the use of hardware acceleration to render the graphical user interface of Windows. It was originally created to enable portions of the new "Windows Aero" user experience, which allowed for effects such as transparency, 3D window switching and more. It is also included with Windows Server 2008, but requires the "Desktop Experience" feature and compatible graphics drivers to be installed. == Architecture == The Desktop Window Manager is a compositing window manager, meaning that each program has a buffer that it writes data to; DWM then composites each program's buffer into a final image. By comparison, the stacking window manager in Windows XP and earlier (and also Windows Vista and Windows 7 with Windows Aero disabled) comprises a single display buffer to which all programs write. DWM works in different ways depending on the operating system (Windows 7 or Windows Vista) and on the version of the graphics drivers it uses (WDDM 1.0 or 1.1). Under Windows 7 and with WDDM 1.1 drivers, DWM only writes the program's buffer to the video RAM, even if it is a graphics device interface (GDI) program. This is because Windows 7 supports (limited) hardware acceleration for GDI and in doing so does not need to keep a copy of the buffer in system RAM so that the CPU can write to it. Because the compositor has access to the graphics of all applications, it easily allows visual effects that string together visuals from multiple applications, such as transparency. DWM uses DirectX to perform the function of compositing and rendering in the GPU, freeing the CPU of the task of managing the rendering from the off-screen buffers to the display. However, it does not affect applications painting to the off-screen buffers – depending on the technologies used for that, this might still be CPU-bound. DWM-agnostic rendering techniques like GDI are redirected to the buffers by rendering the user interface (UI) as bitmaps. DWM-aware rendering technologies like WPF directly make the internal data structures available in a DWM-compatible format. The window contents in the buffers are then converted to DirectX textures. The desktop itself is a full-screen Direct3D surface, with windows being represented as a mesh consisting of two adjacent (and mutually inverted) triangles, which are transformed to represent a 2D rectangle. The texture, representing the UI chrome, is then mapped onto these rectangles. Window transitions are implemented as transformations of the meshes, using shader programs. With Windows Vista, the transitions are limited to the set of built-in shaders that implement the transformations. Greg Schechter, a developer at Microsoft has suggested that this might be opened up for developers and users to plug in their own effects in a future release. DWM only maps the primary desktop object as a 3D surface; other desktop objects, including virtual desktops as well as the secure desktop used by User Account Control are not. Because all applications render to an off-screen buffer, they can be read off the buffer embedded in other applications as well. Since the off-screen buffer is constantly updated by the application, the embedded rendering will be a dynamic representation of the application window and not a static rendering. This is how the live thumbnail previews and Windows Flip work in Windows Vista and Windows 7. DWM exposes a public API that allows applications to access these thumbnail representations. The size of the thumbnail is not fixed; applications can request the thumbnails at any size - smaller than the original window, at the same size or even larger - and DWM will scale them properly before returning. Aero Flip does not use the public thumbnail APIs as they do not allow for directly accessing the Direct3D textures. Instead, Aero Flip is implemented directly in the DWM engine. The Desktop Window Manager uses Media Integration Layer (MIL), the unmanaged compositor which it shares with Windows Presentation Foundation, to represent the windows as composition nodes in a composition tree. The composition tree represents the desktop and all the windows hosted in it, which are then rendered by MIL from the back of the scene to the front. Since all the windows contribute to the final image, the color of a resultant pixel can be decided by more than one window. This is used to implement effects such as per-pixel transparency. DWM allows custom shaders to be invoked to control how pixels from multiple applications are used to create the displayed pixel. The DWM includes built-in Pixel Shader 2.0 programs which compute the color of a pixel in a window by averaging the color of the pixel as determined by the window behind it and its neighboring pixels. These shaders are used by DWM to achieve the blur effect in the window borders of windows managed by DWM, and optionally for the areas where it is requested by the application. Since MIL provides a retained mode graphics system by caching the composition trees, the job of repainting and refreshing the screen when windows are moved is handled by DWM and MIL, freeing the application of the responsibility. The background data is already in the composition tree and the off-screen buffers and is directly used to render the background. In pre-Vista Windows OSs, background applications had to be requested to re-render themselves by sending them the WM_PAINT message. DWM uses double-buffered graphics to prevent flickering and tearing when moving windows. The compositing engine uses optimizations such as culling to improve performance, as well as not redrawing areas that have not changed. Because the compositor is multi-monitor aware, DWM natively supports this too. During full-screen applications, such as games, DWM does not perform window compositing and therefore performance will not appreciably decrease. On Windows 8 and Windows Server 2012, DWM is used at all times and cannot be disabled, due to the new "start screen experience" implemented. Since the DWM process is usually required to run at all times on Windows 8, users experiencing an issue with the process are seeing memory usage decrease after a system reboot. This is often the first step in a long list of troubleshooting tasks that can help. It is possible to prevent DWM from restarting temporarily in Windows 8, which causes the desktop to turn black, the taskbar grey, and break the start screen/modern apps, but desktop apps will continue to function and appear just like Windows 7 and Vista's Basic theme, based on the single-buffer renderer used by XP. They also use Windows 8's centered title bar, visible within Windows PreInstallation Environment. Starting up Windows without DWM will not work because the default lock screen requires DWM unlike the fallback lockscreen that appears as a command line interface program when Windows.UI.Logon.dll isn't present on Windows versions such as 1507 and later, so it can only be done on the fly, and does not have any practical purposes. Starting with Windows 10, disabling DWM in such a way will cause the entire compositing engine to break, even traditional desktop apps, due to Universal App implementations in the taskbar and new start menu. Windows can still be partially usable without the presence of DWM but requires Sihost.exe to not be present due to it relying on DWM. Most of the applications in Windows 11 require DWM to render UI elements and transparency, Windows 11's new task manager requires dwm to render menus unlike the fallback -d version. Unlike its predecessors, Windows 8 supports basic display adapters through Windows Advanced Rasterization Platform (WARP), which uses software rendering and the CPU to render the interface rather than the graphics card. This allows DWM to function without compatible drivers, but not at the same level of performance as with a normal graphics card. DWM on Windows 8 also adds support for stereoscopic 3D. == Redirection == For rendering techniques that are not DWM-aware, output must be redirected to the DWM buffers. With Windows, either GDI or DirectX can be used for rendering. To make these two work with DWM, redirection techniques for both are provided. With GDI, which is the most used UI rendering technique in Microsoft Windows, each application window is notified when it or a part of it comes in view and it is the job of the application to render itself. Without DWM, the rendering rasterizes the UI in a buffer in video memory, from where it is rendered to the screen. Under DWM, GDI calls are redirected to use the Canonical Display Driver (cdd.dll), a software renderer. A buffer equal to the size of the window is allocated in system memory and CDD.DLL outputs to this buffer rather than the video memory. Another buffer is allocated in the video memory to represent t

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  • Adaptive algorithm

    Adaptive algorithm

    An adaptive algorithm is an algorithm that changes its behavior at the time it is run, based on information available and on a priori defined reward mechanism (or criterion). Such information could be the story of recently received data, information on the available computational resources, or other run-time acquired (or a priori known) information related to the environment in which it operates. Among the most used adaptive algorithms is the Widrow-Hoff’s least mean squares (LMS), which represents a class of stochastic gradient-descent algorithms used in adaptive filtering and machine learning. In adaptive filtering the LMS is used to mimic a desired filter by finding the filter coefficients that relate to producing the least mean square of the error signal (difference between the desired and the actual signal). For example, stable partition, using no additional memory is O(n lg n) but given O(n) memory, it can be O(n) in time. As implemented by the C++ Standard Library, stable_partition is adaptive and so it acquires as much memory as it can get (up to what it would need at most) and applies the algorithm using that available memory. Another example is adaptive sort, whose behavior changes upon the presortedness of its input. An example of an adaptive algorithm in radar systems is the constant false alarm rate (CFAR) detector. In machine learning and optimization, many algorithms are adaptive or have adaptive variants, which usually means that the algorithm parameters such as learning rate are automatically adjusted according to statistics about the optimisation thus far (e.g. the rate of convergence). Examples include adaptive simulated annealing, adaptive coordinate descent, adaptive quadrature, AdaBoost, Adagrad, Adadelta, RMSprop, and Adam. In data compression, adaptive coding algorithms such as Adaptive Huffman coding or Prediction by partial matching can take a stream of data as input, and adapt their compression technique based on the symbols that they have already encountered. In signal processing, the Adaptive Transform Acoustic Coding (ATRAC) codec used in MiniDisc recorders is called "adaptive" because the window length (the size of an audio "chunk") can change according to the nature of the sound being compressed, to try to achieve the best-sounding compression strategy.

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  • Project workforce management

    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

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  • ArchiMate

    ArchiMate

    ArchiMate ( AR-ki-mayt) is an open and independent enterprise architecture modeling language to support the description, analysis and visualization of architecture within and across business domains in an unambiguous way. ArchiMate is a technical standard from The Open Group and is based on concepts from the now superseded IEEE 1471 standard. It is supported by various tool vendors and consulting firms. ArchiMate is also a registered trademark of The Open Group. The Open Group has a certification program for ArchiMate users, software tools and courses. ArchiMate distinguishes itself from other languages such as Unified Modeling Language (UML) and Business Process Modeling and Notation (BPMN) by its enterprise modelling scope. Also, UML and BPMN are meant for a specific use and they are quite heavy – containing about 150 (UML) and 250 (BPMN) modeling concepts whereas ArchiMate works with just about 50 (in version 2.0). The goal of ArchiMate is to be ”as small as possible”, not to cover every edge scenario imaginable. To be easy to learn and apply, ArchiMate was intentionally restricted “to the concepts that suffice for modeling the proverbial 80% of practical cases". == Overview == ArchiMate offers a common language for describing the construction and operation of business processes, organizational structures, information flows, IT systems, and technical infrastructure. This insight helps the different stakeholders to design, assess, and communicate the consequences of decisions and changes within and between these business domains. The main concepts and relationships of the ArchiMate language can be seen as a framework, the so-called Archimate Framework: It divides the enterprise architecture into a business, application and technology layer. In each layer, three aspects are considered: active elements, an internal structure and elements that define use or communicate information. One of the objectives of the ArchiMate language is to define the relationships between concepts in different architecture domains. The concepts of this language therefore hold the middle between the detailed concepts, which are used for modeling individual domains (for example, the Unified Modeling Language (UML) for modeling software products), and Business Process Model and Notation (BPMN), which is used for business process modeling. == History == ArchiMate is partly based on the now superseded IEEE 1471 standard. It was developed in the Netherlands by a project team from the Telematica Instituut in cooperation with several Dutch partners from government, industry and academia. Among the partners were Ordina NV, Radboud Universiteit Nijmegen, the Leiden Institute for Advanced Computer Science (LIACS) and the Centrum Wiskunde & Informatica (CWI). Later, tests were performed in organizations such as ABN AMRO, the Dutch Tax and Customs Administration and the ABP. The development process lasted from July 2002 to December 2004, and took about 35 person years and approximately 4 million euros. The development was funded by the Dutch government (Dutch Tax and Customs Administration), and business partners, including ABN AMRO and the ABP Pension Fund. In 2008 the ownership and stewardship of ArchiMate was transferred to The Open Group. It is now managed by the ArchiMate Forum within The Open Group. In February 2009 The Open Group published the ArchiMate 1.0 standard as a formal technical standard. In January 2012 the ArchiMate 2.0 standard, and in 2013 the ArchiMate 2.1 standard was released. In June 2016, the Open Group released version 3.0 of the ArchiMate Specification. An update to Archimate 3.0.1 came out in August 2017. Archimate 3.1 was published 5 November 2019. The latest version of the ArchiMate Specification is version 3.2 released October 2022. Version 3.0 adds enhanced support for capability-oriented strategic modelling, new entities representing physical resources (for modelling the ingredients, equipment and transport resources used in the physical world) and a generic metamodel showing the entity types and the relationships between them. == ArchiMate framework == === Core framework === The main concepts and elements of the ArchiMate language are being presented as ArchiMate core framework. It consists of three layers and three aspects. This creates a matrix of combinations. Every layer has its passive structure, behavior and active structure aspects. ==== Layers ==== ArchiMate has a layered and service-oriented look on architectural models. The higher layers make use of services that are provided by the lower layers. Although, at an abstract level, the concepts that are used within each layer are similar, we define more concrete concepts that are specific for a certain layer. In this context, we distinguish three main layers: The business layer is about business processes, services, functions and events of business units. This layer "offers products and services to external customers, which are realized in the organization by business processes performed by business actors and roles". The application layer is about software applications that "support the components in the business with application services". The technology layer deals "with the hardware and communication infrastructure to support the application layer. This layer offers infrastructural services needed to run applications, realized by computer and communication hardware and system software". Each of these main layers can be further divided in sub-layers. For example, in the business layer, the primary business processes realising the products of a company may make use of a layer of secondary (supporting) business processes; in the application layer, the end-user applications may make use of generic services offered by supporting applications. On top of the business layer, a separate environment layer may be added, modelling the external customers that make use of the services of the organisation (although these may also be considered part of the business layer). In line with service orientation, the most important relation between layers is formed by use relations, which show how the higher layers make use of the services of lower layers. However, a second type of link is formed by realisation relations: elements in lower layers may realise comparable elements in higher layers; e.g., a ‘data object’ (application layer) may realise a ‘business object’ (business layer); or an ‘artifact’ (technology layer) may realise either a ‘data object’ or an ‘application component’ (application layer). ==== Aspects ==== Passive structure is the set of entities on which actions are conducted. In the business layer the example would be information objects, in the application layer data objects and in the technology layer, they could include physical objects. Behavior refers to the processes and functions performed by the actors. "Structural elements are assigned to behavioral elements, to show who or what displays the behavior". Active structure is the set of entities that display some behavior, e.g. business actors, devices, or application components. === Full framework === The Full ArchiMate framework is enriched by the physical layer, which was added to allow modeling of “physical equipment, materials, and distribution networks” and was not present in the previous version. The implementation and migration layer adds elements that allow architects to model a state of transition, to mark parts of the architecture that are temporary for the purpose, as the name says, of implementation and migration. Strategy layer adds three elements: resource, capability and course of action. These elements help to incorporate strategic dimension to the ArchiMate language by allowing it to depict the usage of resources and capabilities in order to achieve some strategic goals. Finally, there is a motivation aspect that allows different stakeholders to describe the motivation of specific actors or domains, which can be quite important when looking at one thing from several different angles. It adds several elements like stakeholder, value, driver, goal, meaning etc. == ArchiMate language == The ArchiMate language is formed as a top-level and is hierarchical. On the top, there is a model. A model is a collection of concepts. A concept can be either an element or a relationship. An element can be either of behavior type, structure, motivation or a so-called composite element (which means that it does not fit just one aspect of the framework, but two or more). The functionality of all concepts without a dependency on a specific layer is described by the generic metamodel. This layer-independent description of concepts is useful when trying to understand the mechanics of the Archimate language. === Concepts === ==== Elements ==== The generic elements are distributed into the same categories as the layers: Active structure elements Behavior elements Passive structure elements Motivation elements Active structure e

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  • Hierarchical navigable small world

    Hierarchical navigable small world

    Hierarchical navigable small world (HNSW) is an algorithm for approximate nearest neighbor search. It is used to find items that are similar to a query item in a large collection, without comparing the query with every item one by one. The algorithm is commonly used for searching vector data. In these systems, an item such as a document, image, song, or user profile is represented by a list of numbers called a vector. Items with similar vectors are treated as similar according to the model that produced the vectors. HNSW provides a way to search these vectors quickly, especially in large datasets. HNSW stores vectors in a graph. Each vector is a node, and links connect it to some nearby vectors. The graph has several layers: upper layers contain fewer nodes and act like a rough map, while the bottom layer contains all nodes and gives a more detailed view. A search starts in an upper layer, follows links toward nodes that are closer to the query, and then repeats the process in lower layers until it finds a set of likely nearest neighbors. == Background == The nearest neighbor search problem asks which items in a dataset are closest to a query item. A direct search can compare the query with every item in the dataset, but this becomes slow when the dataset is large. Exact search methods based on spatial trees, such as the k-d tree and R-tree, can also become less effective for high-dimensional data, a problem often associated with the curse of dimensionality. Approximate nearest neighbor methods trade some exactness for speed or lower resource use. Instead of always guaranteeing the exact closest item, they try to return close items quickly. Other approximate methods include locality-sensitive hashing and product quantization. HNSW builds on research into small-world networks and navigable graphs. In a small-world graph, most nodes can be reached from other nodes through a short chain of links. In a navigable graph, a search procedure can use local information to move toward a target. Jon Kleinberg's work on navigation in small-world networks is an important example of this research area. Later work studied ways to add links that make graphs easier to navigate greedily. The HNSW algorithm extends earlier navigable small world methods for similarity search by adding a hierarchy of graph layers. This hierarchy helps the algorithm find a good region of the graph before doing a more detailed search in the bottom layer. == Algorithm == HNSW is based on a proximity graph. In this graph, nearby vectors are connected by edges. The algorithm uses these edges to move through the dataset, rather than scanning every vector. The graph is hierarchical. Every vector appears in the bottom layer. Some vectors are also placed in higher layers, with fewer vectors appearing as the layers go upward. The upper layers allow long-range movement across the dataset, while the lower layers allow a more detailed search near promising candidates. A typical search proceeds as follows: The search begins from an entry point in the highest layer. At each step, the algorithm looks at neighboring nodes and moves to a neighbor that is closer to the query. When it cannot find a closer neighbor in that layer, it moves down to the next layer. In the bottom layer, it explores a wider set of candidate nodes and returns the nearest candidates found. This search strategy is often described as greedy navigation. The algorithm repeatedly chooses locally better nodes, using the graph structure to approach the query point. == Construction and parameters == The HNSW graph is built incrementally. When a new vector is inserted, the algorithm assigns it a maximum layer, searches for nearby existing nodes, and connects the new node to selected neighbors in each layer where it appears. Implementations usually expose parameters that control the trade-off between speed, accuracy, memory use, and construction time. A higher number of graph connections can improve recall but requires more memory. A larger search candidate list can improve accuracy but makes queries slower. A larger construction candidate list can improve the quality of the graph but makes index building slower. Because HNSW is approximate, its results are not always identical to a full exact search. Its practical performance depends on the dataset, distance measure, implementation, and parameter settings. Benchmarking studies have found HNSW-based libraries to be strong performers among approximate nearest neighbor methods, although worst-case performance can differ from performance on common benchmark datasets. == Use in vector search systems == HNSW is used as an index in systems that store and search high-dimensional vectors. These systems include vector databases, search engines, and database extensions. Typical uses include semantic search, recommender systems, image similarity search, and retrieval-augmented generation. Several software projects implement or support HNSW. Libraries include hnswlib, which is associated with the original HNSW authors, and FAISS. Database and search systems that document HNSW support include Apache Lucene, Chroma, ClickHouse, DuckDB, MariaDB, Milvus, pgvector, Qdrant, and Redis.

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  • Parchive

    Parchive

    Parchive (a portmanteau of parity archive, and formally known as Parity Volume Set Specification) is an erasure code system that produces par files for checksum verification of data integrity, with the capability to perform data recovery operations that can repair or regenerate corrupted or missing data. Parchive was originally written to solve the problem of reliable file sharing on Usenet, but it can be used for protecting any kind of data from data corruption, disc rot, bit rot, and accidental or malicious damage. Despite the name, Parchive uses more advanced techniques (specifically error correction codes) than simplistic parity methods of error detection. As of 2015, PAR1 is obsolete, PAR2 is mature for widespread use, and PAR3 is a discontinued experimental version developed by MultiPar author Yutaka Sawada. The original SourceForge Parchive project has been inactive since April 30, 2015. A new PAR3 specification has been worked on since April 28, 2019 by PAR2 specification author Michael Nahas. An alpha version of the PAR3 specification has been published on January 29, 2022 while the program itself is being developed. == History == Parchive was intended to increase the reliability of transferring files via Usenet newsgroups. Usenet was originally designed for informal conversations, and the underlying protocol, NNTP was not designed to transmit arbitrary binary data. Another limitation, which was acceptable for conversations but not for files, was that messages were normally fairly short in length and limited to 7-bit ASCII text. Various techniques were devised to send files over Usenet, such as uuencoding and Base64. Later Usenet software allowed 8 bit Extended ASCII, which permitted new techniques like yEnc. Large files were broken up to reduce the effect of a corrupted download, but the unreliable nature of Usenet remained. With the introduction of Parchive, parity files could be created that were then uploaded along with the original data files. If any of the data files were damaged or lost while being propagated between Usenet servers, users could download parity files and use them to reconstruct the damaged or missing files. Parchive included the construction of small index files (.par in version 1 and .par2 in version 2) that do not contain any recovery data. These indexes contain file hashes that can be used to quickly identify the target files and verify their integrity. Because the index files were so small, they minimized the amount of extra data that had to be downloaded from Usenet to verify that the data files were all present and undamaged, or to determine how many parity volumes were required to repair any damage or reconstruct any missing files. They were most useful in version 1 where the parity volumes were much larger than the short index files. These larger parity volumes contain the actual recovery data along with a duplicate copy of the information in the index files (which allows them to be used on their own to verify the integrity of the data files if there is no small index file available). In July 2001, Tobias Rieper and Stefan Wehlus proposed the Parity Volume Set specification, and with the assistance of other project members, version 1.0 of the specification was published in October 2001. Par1 used Reed–Solomon error correction to create new recovery files. Any of the recovery files can be used to rebuild a missing file from an incomplete download. Version 1 became widely used on Usenet, but it did suffer some limitations: It was restricted to handle at most 255 files. The recovery files had to be the size of the largest input file, so it did not work well when the input files were of various sizes. (This limited its usefulness when not paired with the proprietary RAR compression tool.) The recovery algorithm had a bug, due to a flaw in the academic paper on which it was based. It was strongly tied to Usenet and it was felt that a more general tool might have a wider audience. In January 2002, Howard Fukada proposed that a new Par2 specification should be devised with the significant changes that data verification and repair should work on blocks of data rather than whole files, and that the algorithm should switch to using 16 bit numbers rather than the 8 bit numbers that PAR1 used. Michael Nahas and Peter Clements took up these ideas in July 2002, with additional input from Paul Nettle and Ryan Gallagher (who both wrote Par1 clients). Version 2.0 of the Parchive specification was published by Michael Nahas in September 2002. Peter Clements then went on to write the first two Par2 implementations, QuickPar and par2cmdline. Abandoned since 2004, Paul Houle created phpar2 to supersede par2cmdline. Yutaka Sawada created MultiPar to supersede QuickPar. MultiPar uses par2j.exe (which is partially based on par2cmdline's optimization techniques) to use as MultiPar's backend engine. == Versions == Versions 1 and 2 of the file format are incompatible. (However, many clients support both.) === Par1 === For Par1, the files f1, f2, ..., fn, the Parchive consists of an index file (f.par), which is CRC type file with no recovery blocks, and a number of "parity volumes" (f.p01, f.p02, etc.). Given all of the original files except for one (for example, f2), it is possible to create the missing f2 given all of the other original files and any one of the parity volumes. Alternatively, it is possible to recreate two missing files from any two of the parity volumes and so forth. Par1 supports up to a total of 256 source and recovery files. === Par2 === Par2 files generally use this naming/extension system: filename.vol000+01.PAR2, filename.vol001+02.PAR2, filename.vol003+04.PAR2, filename.vol007+06.PAR2, etc. The number after the "+" in the filename indicates how many blocks it contains, and the number after "vol" indicates the number of the first recovery block within the PAR2 file. If an index file of a download states that 4 blocks are missing, the easiest way to repair the files would be by downloading filename.vol003+04.PAR2. However, due to the redundancy, filename.vol007+06.PAR2 is also acceptable. There is also an index file filename.PAR2, it is identical in function to the small index file used in PAR1. Par2 specification supports up to 32,768 source blocks and up to 65,535 recovery blocks. Input files are split into multiple equal-sized blocks so that recovery files do not need to be the size of the largest input file. Although Unicode is mentioned in the PAR2 specification as an option, most PAR2 implementations do not support Unicode. Directory support is included in the PAR2 specification, but most or all implementations do not support it. === Par3 === The Par3 specification was originally planned to be published as an enhancement over the Par2 specification. However, to date, it has remained closed source by specification owner Yutaka Sawada. A discussion on a new format started in the GitHub issue section of the maintained fork par2cmdline on January 29, 2019. The discussion led to a new format which is also named as Par3. The new Par3 format's specification is published on GitHub, but remains being an alpha draft as of January 28, 2022. The specification is written by Michael Nahas, the author of Par2 specification, with the help from Yutaka Sawada, animetosho and malaire. The new format claims to have multiple advantages over the Par2 format, including support for: More than 216 files and more than 216 blocks. Packing small files into one block, as well as deduplication when a block appears in multiple files. UTF-8 file names. File permissions, hard links, symbolic/soft links, and empty directories. Embedding PAR data inside other formats, like ZIP archives or ISO disk images. "Incremental backups", where a user creates recovery files for some file or folder, change some data, and create new recovery files reusing some of the older files. More error correction code algorithms (such as LDPC and sparse random matrix). BLAKE3 hashes, dropping support for the MD5 hashes used in PAR2. == Software == === Multi-platform === par2+tbb (GPLv2) — a concurrent (multithreaded) version of par2cmdline 0.4 using TBB. Only compatible with x86 based CPUs. It is available in the FreeBSD Ports system as par2cmdline-tbb. Original par2cmdline — (obsolete). Available in the FreeBSD Ports system as par2cmdline. par2cmdline maintained fork by BlackIkeEagle. par2cmdline-mt is another multithreaded version of par2cmdline using OpenMP, GPLv2, or later. Currently merged into BlackIkeEagle's fork and maintained there. ParPar (CC0) is a high performance, multithreaded PAR2 client and Node.js library. Does not support verifying or repair, it can currently only create PAR2 archives. par2deep (LGPL-3.0) — Produce, verify and repair par2 files recursively, both on the command line as well as with the aid of a graphical user interface. It is available in the Python Package Index system as par2deep. par2cron (MIT License) is an o

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  • Five safes

    Five safes

    The Five Safes is a framework for helping make decisions about making effective use of data which is confidential or sensitive. It is mainly used to describe or design research access to statistical data held by government and health agencies, and by data archives such as the UK Data Service. It is not an internationally accepted standard. Two of the Five Safes refer to statistical disclosure control, and so the Five Safes is usually used to contrast statistical and non-statistical controls when comparing data management options. == Concept == The Five Safes proposes that data management decisions be considered as solving problems in five 'dimensions': projects, people, settings, data and outputs. The combination of the controls leads to 'safe use'. These are most commonly expressed as questions, for example: These dimensions are scales, not limits. That is, solutions can have a mix of more or fewer controls in each dimension, but the overall aim of 'safe use' independent of the particular mix. For example, a public use file available for open download cannot control who uses it, where or for what purpose, and so all the control (protection) must be in the data itself. In contrast, a file which is only accessed through a secure environment with certified users can contain very sensitive information: the non-statistical controls allow the data to be 'unsafe'. One academic likened the process to a graphic equalizer, where bass and treble can be combined independently to produce a sound the listener likes, which has proven to be a very useful metaphor. This 2023 Data Foundation webinar is an expert discussion of how the elements interact, including an excellent introductory representation. There is no 'order' to the Five Safes, in that one is necessarily more important than the others. However, Ritchie argued that the 'managerial' controls (projects, people, setting) should be addressed before the 'statistical' controls (data, output). The Five Safes concept is associated with other topics which developed from the same programme at ONS, although these are not necessarily implemented. Safe people is associated with 'active researcher management', while safe outputs is linked with principles-based output statistical disclosure control. The Five Safes is a positive framework, describing what is and is not. The EDRU ('evidence-based, default-open, risk-managed, user-centred') attitudinal model is sometimes used to give a normative context == The 'data access spectrum' == From 2003 the Five Safes was also represented in a simpler form as a 'Data Access Spectrum'. The non-data controls (project, people, setting, outputs) tend to work together, in that organisations often see these as a complementary set of restrictions on access. These can then be contrasted with choices about data anonymisation to present a linear representation of data access options. This presentation is consistent with the idea of 'data as a residual', as well as data protection laws of the time which often characterised data simply as anonymous or not anonymous. A similar idea had already been developed independently in 2001 by Chuck Humphrey of the Canadian RDC network, the 'continuum of access'. More recently, The Open Data Institute has developed a 'Data Spectrum toolkit' which includes industry-specific examples. == History and terminology == The Five Safes was devised in the winter of 2002/2003 by Felix Ritchie at the UK Office for National Statistics (ONS) to describe its secure remote-access Virtual Microdata Laboratory (VML). It was described at this time as the 'VML Security Model'. This was adopted by the NORC data enclave, and more widely in the US, as the 'portfolio model' (although this is now also used to refer to a slightly different legal/statistical/educational breakdown). In 2012 the framework as was still being referred to as the 'VML security model', but its increasing use among non-UK organisations led to the adoption of the more general and informative phrase 'Five Safes'. The original framework only had four safes (projects, people, settings and outputs): the framework was used to describe highly detailed data access through a secure environment, and so the 'data' dimension was irrelevant. From 2007 onwards, 'safe data' was included as the framework was used to a describe a wider range of ONS activities. As the US version was based upon the 2005 specification, some US iterations uses have the original four dimensions (eg). Some discussions, such as the OECD, use the term 'secure' instead 'safe'. However, the use of both these terms can cause presentational problems: less control in a particular dimension could be seen to imply 'unsafe users' or 'insecure settings', for example, which distracts from the main message. Hence, the Australian government uses the term "five data sharing principles". The 'Anonymisation Decision-Making Framework' uses a framework based on the Five Safes but relabelling "projects", "people", and "settings" as "governance", "agency" and "infrastructure", respectively; "Output" is omitted, and "safe use" becomes "functional anonymisation". There is no reference to the Five Safes or any associated literature. The Australian version was required to include references to the Five Safes, and presented it as an alternative without comment. == Application == The framework has had three uses: pedagogical, descriptive, and design. Since 2016, it has also been used, directly and indirectly in legislation. See for more detailed examples. === Pedagogy === The first significant use of the framework, other than internal administrative use, was to structure researcher training courses at the UK Office for National Statistics from 2003. UK Data Archive, Administrative Data Research Network, Eurostat, Statistics New Zealand, the Mexican National Institute of Statistics and Geography, NORC, Statistics Canada and the Australian Bureau of Statistics, amongst others, have also used this framework. Most of these courses are for researchers using restricted-access facilities; the Eurostat courses are unusual in that they are designed for all users of sensitive data. === Description === The framework is often used to describe existing data access solutions (e.g. UK HMRC Data Lab, UK Data Service, Statistics New Zealand) or planned/conceptualised ones (e.g. Eurostat in 2011). An early use was to help identify areas where ONS' still had 'irreducible risks' in its provision of secure remote access. The framework is mostly used for confidential social science data. To date it appears to have made little impact on medical research planning, although it is now included in the revised guidelines on implementing HIPAA regulations in the US, and by Cancer Research UK and the Health Foundation in the UK. It has also been used to describe a security model for the Scottish Health Informatics Programme. === Design === In general the Five Safes has been used to describe solutions post-factum, and to explain/justify choices made, but an increasing number of organisations have used the framework to design data access solutions. For example, the Hellenic Statistical Agency developed a data strategy built around the Five Safes in 2016; the UK Health Foundation used the Five Safes to design its data management and training programmes. Use in the private sector is less common but some organisations have incorporated the Five Safes into consulting services. In 2015 the UK Data Service organized a workshop to encourage data users from the academic and private sectors to think about how to manage confidential research data, using the Five Safes to demonstrate alternative options and best practice. Early adopters for strategic design use were in Australia: both the Australian Bureau of Statistics and the Australian Department of Social Service used the Five Safes as an ex ante design tool. In 2017 the Australian Productivity Commission recommended adopting a version of the framework to support cross-government data sharing and re-use. This underwent extensive consultation and culminated in the DAT Act 2022. Since 2020 the Five Safes has been the overriding framework for the design of new secure facilities and data sharing arrangements in the UK for public health and social sciences. This has been promoted by the Office for Statistics Regulation, the UK Statistics Authority, NHS DIgital, and the research funding bodies Administrative Data Research UK and DARE UK. === Regulation and legislation === Three laws have incorporated the Fives Safes. They are explicit in the South Australian Public Sector (Data Sharing) Act 2016, and implicit in the research provisions of the UK Digital Economy Act 2017. The Australian Data Availability and Transparency Act 2022 renames the Five Safes as the Five Data Sharing Principles.A 2025 statutory review of the DAT Act 2022 found "that the DAT Act has not been effective in achieving its objectives.". The review includes specific referen

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  • The Algorithm Auction

    The Algorithm Auction

    The Algorithm Auction is the world's first auction of computer algorithms. Created by Ruse Laboratories, the initial auction featured seven lots and was held at the Cooper Hewitt, Smithsonian Design Museum on March 27, 2015. Five lots were physical representations of famous code or algorithms, including a signed, handwritten copy of the original Hello, World! C program by its creator Brian Kernighan on dot-matrix printer paper, a printed copy of 5,000 lines of Assembly code comprising the earliest known version of Turtle Graphics, signed by its creator Hal Abelson, a necktie containing the six-line qrpff algorithm capable of decrypting content on a commercially produced DVD video disc, and a pair of drawings representing OkCupid's original Compatibility Calculation algorithm, signed by the company founders. The qrpff lot sold for $2,500. Two other lots were “living algorithms,” including a set of JavaScript tools for building applications that are accessible to the visually impaired and the other is for a program that converts lines of software code into music. Winning bidders received, along with artifacts related to the algorithms, a full intellectual property license to use, modify, or open-source the code. All lots were sold, with Hello World receiving the most bids. Exhibited alongside the auction lots were a facsimile of the Plimpton 322 tablet on loan from Columbia University, and Nigella, an art-world facing computer virus named after Nigella Lawson and created by cypherpunk and hacktivist Richard Jones. Sebastian Chan, Director of Digital & Emerging Media at the Cooper–Hewitt, attended the event remotely from Milan, Italy via a Beam Pro telepresence robot. == Effects == Following the auction, the Museum of Modern Art held a salon titled The Way of the Algorithm highlighting algorithms as "a ubiquitous and indispensable component of our lives."

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  • Edge inference

    Edge inference

    Edge inference is the process of running machine learning or deep learning models on local devices (edge devices) such as smartphones, IoT devices, embedded systems, and edge servers instead of centralized cloud computing infrastructure. A key feature of edge computing is edge inference, which allows for real-time data processing, low latency, and improved privacy by reducing the amount of data sent to remote servers.

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  • Recommender system

    Recommender system

    A recommender system, also called a recommendation algorithm, recommendation engine, or recommendation platform, is a type of information filtering system that suggests items most relevant to a particular user. The value of these systems becomes particularly evident in scenarios where users must select from a large number of options, such as products, media, or content. Major social media platforms and streaming services rely on recommender systems that employ machine learning to analyze user behavior and preferences, thereby enabling personalized content feeds. Typically, the suggestions refer to a variety decision-making processes, including the selection of a product, musical selection, or online news source to read. The implementation of recommender systems is pervasive, with commonly recognised examples including the generation of playlist for video and music services, the provision of product recommendations for e-commerce platforms, and the recommendation of content on social media platforms and the open web. These systems can operate using a single type of input, such as music, or multiple inputs from diverse platforms, including news, books and search queries. Additionally, popular recommender systems have been developed for specific topics, such as restaurants and online dating services. Recommender systems have also been developed to explore research articles and experts, collaborators, and financial services. A content discovery platform is a software recommendation platform that employs recommender system tools. It utilizes user metadata in order to identify and suggest relevant content, whilst reducing ongoing maintenance and development costs. A content discovery platform delivers personalized content to websites, mobile devices, and set-top boxes. A large range of content discovery platforms currently exist for various forms of content ranging from news articles and academic journal articles to television. As operators compete to serve as the gateway to home entertainment, personalized television emerges as a key service differentiator. Academic content discovery has recently become another area of interest, the emergence of numerous companies dedicated to assisting academic researchers in keeping up to date with relevant academic content and facilitating serendipitous discovery of new content. == Overview == Recommender systems usually make use of either or both collaborative filtering and content-based filtering, as well as other systems such as knowledge-based systems. Collaborative filtering approaches build a model from a user's past behavior (e.g., items previously purchased or selected and/or numerical ratings given to those items) as well as similar decisions made by other users. This model is then used to predict items (or ratings for items) that the user may have an interest in. Content-based filtering approaches utilize a series of discrete, pre-tagged characteristics of an item in order to recommend additional items with similar properties. === Example === The differences between collaborative and content-based filtering can be demonstrated by comparing two early music recommender systems, Last.fm and Pandora Radio. We can also look at how these methods are applied in e-commerce, for example, on platforms like Amazon. Last.fm creates a "station" of recommended songs by observing what bands and individual tracks the user has listened to on a regular basis and comparing those against the listening behavior of other users. Last.fm will play tracks that do not appear in the user's library, but are often played by other users with similar interests. As this approach leverages the behavior of users, it is an example of a collaborative filtering technique. Pandora uses the properties of a song or artist (a subset of the 450 attributes provided by the Music Genome Project) to seed a "station" that plays music with similar properties. User feedback is used to refine the station's results, deemphasizing certain attributes when a user "dislikes" a particular song and emphasizing other attributes when a user "likes" a song. This is an example of a content-based approach. In e-commerce, Amazon's well-known "customers who bought X also bought Y" feature is a prime example of collaborative filtering. It also uses content-based filtering when it recommends a book by the same author you've previously read or a pair of shoes in a similar style to ones you've viewed. Each type of system has its strengths and weaknesses. In the above example, Last.fm requires a large amount of information about a user to make accurate recommendations. This is an example of the cold start problem, and is common in collaborative filtering systems. Whereas Pandora needs very little information to start, it is far more limited in scope (for example, it can only make recommendations that are similar to the original seed). === Alternative implementations === Recommender systems are a useful alternative to search algorithms since they help users discover items they might not have found otherwise. Of note, recommender systems are often implemented using search engines indexing non-traditional data. In some cases, like in the Gonzalez v. Google Supreme Court case, may argue that search and recommendation algorithms are different technologies. Recommender systems have been the focus of several granted patents, and there are more than 50 software libraries that support the development of recommender systems including LensKit, RecBole, ReChorus and RecPack. == History == Elaine Rich created the first recommender system in 1979, called Grundy. She looked for a way to recommend users books they might like. Her idea was to create a system that asks users specific questions and classifies them into classes of preferences, or "stereotypes", depending on their answers. Depending on users' stereotype membership, they would then get recommendations for books they might like. Another early recommender system, called a "digital bookshelf", was described in a 1990 technical report by Jussi Karlgren at Columbia University, and implemented at scale and worked through in technical reports and publications from 1994 onwards by Jussi Karlgren, then at SICS, and research groups led by Pattie Maes at MIT, Will Hill at Bellcore, and Paul Resnick, also at MIT, whose work with GroupLens was awarded the 2010 ACM Software Systems Award. Montaner provided the first overview of recommender systems from an intelligent agent perspective. Adomavicius provided a new, alternate overview of recommender systems. Herlocker provides an additional overview of evaluation techniques for recommender systems, and Beel et al. discussed the problems of offline evaluations. Beel et al. have also provided literature surveys on available research paper recommender systems and existing challenges. == Approaches == === Collaborative filtering === One approach to the design of recommender systems that has wide use is collaborative filtering. Collaborative filtering is based on the assumption that people who agreed in the past will agree in the future, and that they will like similar kinds of items as they liked in the past. The system generates recommendations using only information about rating profiles for different users or items. By locating peer users/items with a rating history similar to the current user or item, they generate recommendations using this neighborhood. This approach is a cornerstone for e-commerce sites that analyze the purchasing patterns of thousands of users to suggest what you might like. Collaborative filtering methods are classified as memory-based and model-based. A well-known example of memory-based approaches is the user-based algorithm, while that of model-based approaches is matrix factorization (recommender systems). A key advantage of the collaborative filtering approach is that it does not rely on machine analyzable content and therefore it is capable of accurately recommending complex items such as movies without requiring an "understanding" of the item itself. Many algorithms have been used in measuring user similarity or item similarity in recommender systems. For example, the k-nearest neighbor (k-NN) approach and the Pearson Correlation as first implemented by Allen. When building a model from a user's behavior, a distinction is often made between explicit and implicit forms of data collection. Examples of explicit data collection include the following: Asking a user to rate an item on a sliding scale. Asking a user to search. Asking a user to rank a collection of items from favorite to least favorite. Presenting two items to a user and asking him/her to choose the better one of them. Asking a user to create a list of items that he/she likes (see Rocchio classification or other similar techniques). Examples of implicit data collection include the following: Observing the items that a user views in an online store, media library, or other repository of med

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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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