SHYSTER is a legal expert system developed at the Australian National University in Canberra in 1993. It was written as the doctoral dissertation of James Popple under the supervision of Robin Stanton, Roger Clarke, Peter Drahos, and Malcolm Newey. A full technical report of the expert system, and a book further detailing its development and testing have also been published. SHYSTER emphasises its pragmatic approach, and posits that a legal expert system need not be based upon a complex model of legal reasoning in order to produce useful advice. Although SHYSTER attempts to model the way in which lawyers argue with cases, it does not attempt to model the way in which lawyers decide which cases to use in those arguments. SHYSTER is of a general design, permitting its operation in different legal domains. It was designed to provide advice in areas of case law that have been specified by a legal expert using a bespoke specification language. Its knowledge of the law is acquired, and represented, as information about cases. It produces its advice by examining, and arguing about, the similarities and differences between cases. It derives its name from Shyster: a slang word for someone who acts in a disreputable, unethical, or unscrupulous way, especially in the practice of law and politics. == Methods == SHYSTER is a specific example of a general category of legal expert systems, broadly defined as systems that make use of artificial intelligence (AI) techniques to solve legal problems. Legal AI systems can be divided into two categories: legal retrieval systems and legal analysis systems. SHYSTER belongs to the latter category of legal analysis systems. Legal analysis systems can be further subdivided into two categories: judgment machines and legal expert systems. SHYSTER again belongs to the latter category of legal expert systems. A legal expert system, as Popple uses the term, is a system capable of performing at a level expected of a lawyer: "AI systems which merely assist a lawyer in coming to legal conclusions or preparing legal arguments are not here considered to be legal expert systems; a legal expert system must exhibit some legal expertise itself." Designed to operate in more than one legal domain, and be of specific use to the common law of Australia, SHYSTER accounts for statute law, case law, and the doctrine of precedent in areas of private law. Whilst it accommodates statute law, it is primarily a case-based system, in contradistinction to rule-based systems like MYCIN. More specifically, it was designed in a manner enabling it to be linked with a rule-based system to form a hybrid system. Although case-based reasoning possesses an advantage over rule-based systems by the elimination of complex semantic networks, it suffers from intractable theoretical obstacles: without some further theory it cannot be predicted what features of a case will turn out to be relevant. Users of SHYSTER therefore require some legal expertise. Richard Susskind argues that "jurisprudence can and ought to supply the models of law and legal reasoning that are required for computerized [sic] implementation in the process of building all expert systems in law". Popple, however, believes jurisprudence is of limited value to developers of legal expert systems. He posits that a lawyer must have a model of the law (maybe unarticulated) which includes assumptions about the nature of law and legal reasoning, but that model need not rest on basic philosophical foundations. It may be a pragmatic model, developed through experience within the legal system. Many lawyers perform their work with little or no jurisprudential knowledge, and there is no evidence to suggest that they are worse, or better, at their jobs than lawyers well-versed in jurisprudence. The fact that many lawyers have mastered the process of legal reasoning, without having been immersed in jurisprudence, suggests that it may indeed be possible to develop legal expert systems of good quality without jurisprudential insight. As a pragmatic legal expert system SHYSTER is the embodiment of this belief. A further example of SHYSTER’s pragmatism is its simple knowledge representation structure. This structure was designed to facilitate specification of different areas of case law using a specification language. Areas of case law are specified in terms of the cases and attributes of importance in those areas. SHYSTER weights its attributes and checks for dependence between them. In order to choose cases upon which to construct its opinions, SHYSTER calculates distances between cases and uses these distances to determine which of the leading cases are nearest to the instant case. To this end SHYSTER can be seen to adopt and expand upon nearest neighbor search methods used in pattern recognition. These nearest cases are used to produce an argument (based on similarities and differences between the cases) about the likely outcome in the instant case. This argument relies on the doctrine of precedent; it assumes that the instant case will be decided the same way as was the nearest case. SHYSTER then uses information about these nearest cases to construct a report. The report that SHYSTER generates makes a prediction and justifies that prediction by reference only to cases and their similarities and differences: the calculations that SHYSTER performs in coming to its opinion do not appear in that opinion. Safeguards are employed to warn users if SHYSTER doubts the veracity of its advice. == Results == SHYSTER was tested in four different and disparate areas of case law. Four specifications were written, each representing an area of Australian law: an aspect of the law of trover; the meaning of "authorization [sic]" in copyright law of Australia; the categorisation of employment contracts; and the implication of natural justice in administrative decision-making. SHYSTER was evaluated under five headings: its usefulness, its generality, the quality of its advice, its limitations, and possible enhancements that could be made to it. Despite its simple knowledge representation structure, it has shown itself capable of producing good advice, and its simple structure has facilitated the specification of different areas of law. Appreciating the difficulties encountered by legal expert systems developers in adequately representing legal knowledge can assist in appreciating the shortcomings of digital rights management technologies. Some academics believe future digital rights management systems may become sophisticated enough to permit exceptions to copyright law. To this end SHYSTER's attempt to model "authorization [sic]" in the Copyright Act can be viewed as pioneering work in this field. The term "authorization [sic]" is undefined in the Copyright Act. Consequently, a number of cases have been before the courts seeking answers as to what conduct amounts to authorisation. The main contexts in which the issue has arisen are analogous to permitted exceptions to copyright currently prevented by most digital rights management technologies: "home taping of recorded materials, photocopying in educational institutions and performing works in public". When applied to one case concerning compact cassettes, SHYSTER successfully agreed that Amstrad did not authorise the infringement. 'shyster-myci'n Popple highlighted the most obvious avenue of future research using SHYSTER as the development of a rule-based system, and the linking together of that rule-based system with the existing case-based system to form a hybrid system. This intention was eventually realised by Thomas O’Callaghan, the creator of SHYSTER-MYCIN: a hybrid legal expert system first presented at ICAIL '03, 24–28 June 2003 in Edinburgh, Scotland. MYCIN is an existing medical expert system, which was adapted for use with SHYSTER. MYCIN’s controversial "certainty factor" is not used in SHYSTER-MYCIN. The reason for this is the difficulty in scientifically establishing how certain a fact is in a legal domain. The rule-based approach of the MYCIN part is used to reason with the provisions of an Act of Parliament only. This hybrid system enables the case-based system (SHYSTER) to determine open textured concepts when required by the rule-based system (MYCIN). The ultimate conclusion of this joint endeavour is that a hybrid approach is preferred in the creation of legal expert systems where "it is appropriate to use rule-based reasoning when dealing with statutes, and…case-based reasoning when dealing with cases".
Perceptual robotics
Perceptual robotics is an interdisciplinary science linking Robotics and Neuroscience. It investigates biologically motivated robot control strategies, concentrating on perceptual rather than cognitive processes and thereby sides with J. J. Gibson's view against the Poverty of the stimulus theory. As a working definition, the following quote from Chapter 64 by H. Bülthoff, C. Wallraven and M. Giese from The Springer Handbook of Robotics, edited by Bruno Siciliano and Oussama Khatib, published by Springer in 2007, could be used: In the following we will apply the term Perceptual Robotics to signify the design of robots based on principles that are derived from human perception on all three levels in the sense of Marr. This includes a realization in terms of specific neural circuits as well as the transfer of more abstract biologically-inspired strategies for the solution of relevant computational problems.
Deep image compositing
Deep image compositing is a way of compositing and rendering digital images that emerged in the mid-2010s. In addition to the usual color and opacity channels a notion of spatial depth is created. This allows multiple samples in the depth of the image to make up the final resulting color. This technique produces high quality results and removes artifacts around edges that could not be dealt with otherwise. == Deep data == Deep data is encoded by advanced 3D renderers into an image that samples information about the path each rendered pixel takes along the z axis extending outward from the virtual camera through space, including the color and opacity of every non-opaque surface or volume it passes through along the way, as well as neighboring samples. It might be considered somewhat analogous to the way ray tracing generates simulated photon paths through such mediums; however, ray tracing and other traditional rendering techniques generally produce images that contain only three or four channels of color and opacity values per pixel, flattened into a two dimensional frame. Depth maps, on the other hand, contain z axis information encoded in a grayscale image. Each level of gray represents a different slice of the z space. The "thickness" of each slice is determined at time of render, allowing for more or less depth fidelity depending on how deep the scene is. Depth maps have been a boon to compositors for blending 3D renders with live action and practical elements. To be useful, the map must have high enough bit depth to encode separation between close-to-camera objects and objects near infinity. Most 3D software packages are now capable of generating 16-bit and 32-bit depth maps, providing up to 2 billion depth levels. Depth maps do not however include transparency information about non-opaque surfaces or volumes and as such, objects beyond and viewed through these semi- or fully-transparent objects will have no depth information of their own and may not get composited or blurred correctly. Even the popular addition of cryptomattes to many post-production and VFX studios' pipelines, while providing separate color-coded ID shapes for individual elements in a rendered scene to further bridge the gap between CGI and compositing, don't allow for the nearly automated and fully non-linear workflows that deep data does. This is because deep images encapsulate enough 3D information that normally time-intensive tasks such as rotoscoping with numerous holdout mattes for complex interactions between moving characters and semi-transparent environmental volumes like smoke or water, are essentially trivial. Instead of going through that process, multiple mattes could easily be generated from a single set of deep images with no need to re-render every matte element and background for each case. In addition to that efficiency and flexibility, deep data images inherently provide much higher visual quality in common areas that have been difficult with traditional renders, such as the motion-blurred edges of characters with semi-transparent elements like hair. One downside to the use of deep images is their substantial file size, since they encode a relatively enormous amount of data per frame compared to even multichannel formats such as OpenEXR. === Function-based (integrated) === The data is stored as a function of depth. This results in a function curve that can be used to look up the data at any arbitrary depth. Manipulating the data is harder. === Sample-based (deintegrated) === Each sample is considered as an independent piece and can so be manipulated easily. To make sure the data is representing the right detail, an additional expand value needs to be introduced. == Generating deep data == 3D renderers produce the necessary data as a part of the rendering pipeline. Samples are gathered in depth and then combined. The deep data can be written out before this happens and so is nothing new to the process. Generating deep data from camera data needs a proper depth map. This is used in a couple of cases but still not accurate enough for detailed representation. For basic holdout task this can be sufficient though. == Compositing deep data images == Deep images can be composited like regular images. The depth component makes it easier to determine the layering order. Traditionally this had to be input by the user. Deep images have that information for themselves and need no user input. Edge artifacts are reduced as transparent pixels have more data to work with. == History == Deep Images have been around in 3D rendering packages for quite a while now. The use of them for holdouts was first done at several VFX houses in shaders. Holdout mattes can be generated at render time. Using them in a more interactive manner was started recently by several companies, SideFX integrated it in their Houdini software and facilities like Industrial Light & Magic, DreamWorks Animation, Weta, AnimalLogic and DRD studios have implemented interactive solutions. In 2014 the Academy of Motion Picture Arts and Sciences honored the technology with its annual SciTech awards. Dr. Peter Hillman for the long-term development and continued advancement of innovative, robust and complete toolsets for deep compositing and to Colin Doncaster, Johannes Saam, Areito Echevarria, Janne Kontkanen and Chris Cooper for the development, prototyping and promotion of technologies and workflows for deep compositing. == Resources == Pixar Paper Deep Image Paper Video tutorial of Deep Imaging as used on 2012 film Rise of the Planet of the Apes, Nuke compositing software Deep Compositing Course Deep Image File Format at Google Code Academy Award for the Technology Theory of Deep Pixels OpenEXR Deep Pixels
System requirements specification
A System Requirements Specification (SysRS) (abbreviated SysRS to be distinct from a software requirements specification (SRS)) is a structured collection of information that embodies the requirements of a system. A business analyst (BA), sometimes titled system analyst, is responsible for analyzing the business needs of their clients and stakeholders to help identify business problems and propose solutions. Within the systems development life cycle domain, the BA typically performs a liaison function between the business side of an enterprise and the information technology department or external service providers.
SIP (software)
SIP is an open source software tool used to connect computer programs or libraries written in C or C++ with the scripting language Python. It is an alternative to SWIG. SIP was originally developed in 1998 for PyQt — the Python bindings for the Qt GUI toolkit — but is suitable for generating bindings for any C or C++ library. == Concept == SIP takes a set of specification (.sip) files describing the API and generates the required C++ code. This is then compiled to produce the Python extension modules. A .sip file is essentially the class header file with some things removed (because SIP does not include a full C++ parser) and some things added (because C++ does not always provide enough information about how the API works). For PyQt v4 I use an internal tool (written using PyQt of course) called metasip. This is sort of an IDE for SIP. It uses GCC-XML to parse the latest header files and saves the relevant data, as XML, in a metasip project. metasip then does the equivalent of a diff against the previous version of the API and flags up any changes that need to be looked at. Those changes are then made through the GUI and ticked off the TODO list. Generating the .sip files is just a button click. In my subversion repository, PyQt v4 is basically just a 20M XML file. Updating PyQt v4 for a minor release of Qt v4 is about half an hours work. In terms of how the generated code works then I don't think it's very different from how any other bindings generator works. Python has a very good C API for writing extension modules - it's one of the reasons why so many 3rd party tools have Python bindings. For every C++ class, the SIP generated code creates a corresponding Python class implemented in C. == Notable applications that use SIP == PyQt, a python port of the application framework and widget toolkit Qt QGIS, a free and open-source cross-platform desktop geographic information system (GIS) QtiPlot, a computer program to analyze and visualize scientific data calibre (software), a free and open-source cross-platform e-book manager Veusz, a free and open-source cross-platform program to visualize scientific data
AVS Video Editor
AVS Video Editor is a video editing software published by Online Media Technologies Ltd. It is a part of AVS4YOU software suite which includes video, audio, image editing and conversion, disc editing and burning, document conversion and registry cleaner programs. It offers the opportunity to create and edit videos with a vast variety of video and audio effects, text and transitions; capture video from screen, web or DV cameras and VHS tape; record voice; create menus for discs, as well as to save them to plenty of video file formats, burn to discs or publish on Facebook, YouTube, Flickr, etc. == Description == === Interface === The layout consists of the timeline or storyboard view, preview pane and media library (transitions, video effects, text or disc menus) collections. The storyboard view shows the sequence of video clips with the transitions between them and used to change the order of clips or add transitions. Timeline view consists of main video, audio, effects, video overlay and text lines for editing. Once on the timeline video can be duplicated, split, muted, frozen, cropped, stabilized, its speed can be slowed down or increased, audio and color corrected. === Importing footage === Video, audio and image files necessary for video project can be imported into the program from computer hard disk drive. User can also capture video from computer screen, web or mini DV camera, as well as from VHS tape, record voice. === Output (web, device, disc, format) === AVS Video Editor gives the opportunity to save video to a computer hard drive to one of the video formats: AVI, DVD, Blu-ray, MOV, MP4, M4V, MPEG, WMV, MKV, WebM, M2TS, TS, FLV, SWF, RM, 3GP, GIF, DPG, AMV, MTV; burn to DVD or Blu-ray disc with menus; create a video for mobile players, mobile phones or gaming consoles and upload it right to the device. The most popular devices such as Apple iPod, Apple iPhone, Apple iPad, Sony PSP, Samsung Galaxy, Android and BlackBerry smartphones and tablets are supported. There is also an option to create a video that can be streamed via web and save it into Flash or WebM format or for the popular web services: YouTube, Facebook, Telly (Twitvid), Dailymotion, Flickr and Dropbox. === Features === Single and multithread modes: if a computer supports multi-threading, video creation process is performed faster in multithread mode, especially on a multi-core system. Customization of the output file settings, such as bitrate, frame rate, frame size, video and audio codecs, etc. Transitions - help video clips smoothly go into one another, dissolve or overlap two video or image files. Fade in and fade out video and audio files - dissolve a video to and from a blank image, reduce the audio volume at the end of the video and increase at the beginning. Slideshow creation - create a presentation of a series of still images. Voice recording Projects - once a project is created and saved, the next time saving video to some other format will be fast, projects are also used if a user do not have a possibility to create, edit and save video all at once. Video overlay option - superpose video image over the video clip that is being edited. Disk menu and chapters creation - an option for DVD and Blu-ray video. Freeze frame - make a still shot from a video clip. Stabilization feature - reduce jittering or blurring caused by shaky motions of a camera. Enhanced deinterlacing method - increase video quality for interlaced input file - spots and blurred areas are compensated. Scene detection - search and separate one scene of the video from the other. Loop DVD and SWF - output SWF and DVD video are played back continuously. Caching for processing high definition files - create a duplicate video file smaller in size to use it on the preview window and accelerate processing of HD files. Chroma key option - add video overlay half transparent so that only part of it is visible and all the rest disappears to reveal the video underneath. Capture video material from DV tapes, VHS tapes, web cameras, etc. Movie closing credits - add information on movie editing, e.g. crew, cast, data, etc. Creeping line, subtitles, text - add different captions (static and animated), shapes and images to video. Speech balloons and other graphic objects - geometrical shapes to highlight an object in the video. Zoom effect - magnify or reduce the view of the image. Rotate effect - rotate video image at different degrees, e.g. 90, 180, etc. Grayscale and old movie effects - create a black and white video image. Old movie adds also scratches, noise, shake and dust to video, as if it's being played on an old projector. Blur and sharpen effects - visually smooth and soften an image, or make video image better focused. Snow and particles effects - adds snow or various objects (bubbles, flowers, leaves, butterflies etc.) that are moving, flying or falling on the video. Pan and zoom Timer, countdown effects - add a timepiece that measures or counts down a time interval to the video being edited. Snapshots - capture a particular moment of a video clip. Sound track replacement - mute audio track from video and add another one. Audio amplify, noise removal, equalizer, etc. - make video sound louder, attenuate the noise, change frequency pattern of the audio, make some other audio adjustments. Trim and multi-trim options - change video clip duration cutting out unnecessary parts or detect scenes and cut out parts in any place of the video clip. Color correction (brightness, temperature, contrast, saturation, gamma, etc.) effects - allow adjustment of tonal range, color, and sharpness of video files. Crop scale effect - get rid of mattes that appear after changing aspect ratio of a video file. Adjusting the Playback Speed Volume and balance - change sound volume in the output video. Change volume value proportion for main video and added soundtrack, completely mute main video audio and leave added soundtrack only, etc. === Utilities embedded into AVS Video Editor === AVS Mobile Uploader is used to transfer edited and converted media files to portable devices via Bluetooth, Infrared or USB connection. AVS Video Burner is used to burn converted video files to different disc types: CD, DVD, Blu-ray. AVS Video Recorder is used to capture video from analog video sources and supports different types of devices: capture card, web camera (webcam), DV camera, HDV camera. AVS Video Uploader is used to transfer video files to popular video-sharing websites, like Facebook, Dailymotion, YouTube, Photobucket, TwitVid, MySpace, Flickr. AVS Screen Capture is used to capture any actions on the desktop to make presentations or video tutorials more vivid and easily comprehensible. == Important upgrades == The initial release of AVS Video Editor was in 2003 when the program was offered inside AVS software bundles together with AVS Video Tools, AVS Audio Tools and DVD Copy software. In 2005 the program is offered as a part of multifunctional AVS4YOU software suite. AVS Video Editor is frequently updated. The main updates include adding several important features for video editing
Excalidraw
Excalidraw is an open-source, web-based virtual whiteboard and diagramming application. It is used to create diagrams, wireframes, and sketches within a web browser without requiring account registration. The software features a characteristic hand-drawn visual style and supports real-time multi-user collaboration using client-side end-to-end encryption. Excalidraw is released under the MIT License and is maintained by Excalidraw s.r.o., a company based in Brno, Czech Republic. == History == Excalidraw was created on 1 January 2020 by Christopher Chedeau, a software engineer at Meta Platforms. Chedeau, who previously co-created React Native and Prettier, initially developed the application as a personal project before registering the domain on 3 January 2020. Within its first months, the project attracted open-source contributors who assisted in expanding its features and rewriting the codebase into TypeScript and React. By early 2021, day-to-day operations moved to Czech developers David Luzar and Milos Vetesnik. In May 2021, the team incorporated Excalidraw s.r.o. in Brno and launched a commercial cloud-based version named Excalidraw+ to fund the open-source project's development. By May 2026, the main open-source repository on GitHub had accumulated over 123,000 stars. == Features and architecture == The application provides an infinite canvas for geometric shapes, lines, arrows, text, and freehand drawing. Its visual presentation relies on Rough.js, a JavaScript graphics library that alters standard vector paths to mimic irregular, hand-drawn lines. Excalidraw operates as a Progressive web application (PWA), allowing local installation and offline usage, saving data natively to local browser storage. Files use a native, JSON-based extension format (.excalidraw), and canvases can be exported to PNG or SVG formats. Real-time collaboration sessions are executed using Socket.IO via a relay server. Data transmission uses the browser's native Web Cryptography API to achieve end-to-end encryption. A symmetric AES key is generated on the client side and appended to the sharing URL as a fragment identifier (following the # character). Because web browsers do not transmit URL fragments to HTTP servers, the data remains unreadable to the distribution server. == Ecosystem == Excalidraw is distributed as an npm package, allowing third-party developers to embed the whiteboard component directly into external React web applications. Community-developed extensions integrate the application's file format into text editors and note-taking systems, including Visual Studio Code and Obsidian. The platform also has native integrations in commercial platforms such as Notion and HackerRank. == Reception == Google's developer relations team published a technical case study on Excalidraw as a reference implementation for Progressive Web Apps. The analysis highlighted the software's adoption of advanced web platform capabilities, specifically its utilization of the File System Access API and native Clipboard API to replicate desktop software behavior within a web browser environment.