AI For Business Ualbany

AI For Business Ualbany — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  Lawbot

  Lawbots are a broad class of customer-facing legal AI applications that are used to automate specific legal tasks, such as document automation and legal research. The terms robot lawyer and lawyer bot are used as synonyms to lawbot. A robot lawyer or a robo-lawyer refers to a legal AI application that can perform tasks that are typically done by paralegals or young associates at law firms. However, there is some debate on the correctness of the term. Some commentators say that legal AI is technically speaking neither a lawyer nor a robot and should not be referred to as such. Other commentators believe that the term can be misleading and note that the robot lawyer of the future will not be one all-encompassing application but a collection of specialized bots for various tasks. Lawbots use various artificial intelligence techniques or other intelligent systems to limit humans' direct ongoing involvement in certain steps of a legal matter. The user interfaces on lawbots vary from smart searches and step-by-step forms to chatbots. Consumer and enterprise-facing lawbot solutions often do not require direct supervision from a legal professional. Depending on the task, some client-facing solutions used at law firms operate under an attorney supervision. == Levels of autonomy == The following levels of autonomy (LoA) are suggested for automated AI legal reasoning: Level 0 (LoA0): No automation for AI legal reasoning Level 1 (LoA1): Simple assistance automation Level 2 (LoA2): Advanced assistance automation Level 3 (LoA3): Semi-autonomous automation Level 4 (LoA4): Domain automation Level 5 (LoA5): Fully-autonomous automation Level 6 (LoA6): Superhuman automation == Examples == Some legal AI solutions are developed and marketed directly to the customers or consumers, whereas other applications are tools for the attorneys at law firms. There are already hundreds of legal AI solutions that operate in multitude of ways varying in sophistication and dependence on scripted algorithms. One notable legal technology chatbot application is DoNotPay. It had started off as an app for contesting parking tickets, but has since expanded to include features that help users with many different types of legal issues, ranging from consumer protection to immigration rights and other social issues. == Impact on the legal industry == In the 2016 report, Deloitte estimated that more than 110,000 law jobs in just the United Kingdom alone could disappear within the next twenty years due to automation. This change could result in the creation of more highly skilled jobs and in the reduction of paralegal and temporary positions. Deloitte's report asserts that "there is significant potential for high-skilled roles that involve repetitive processes to be automated by smart and self-learning algorithms". According to Lawyers to Engage, between 22% of a lawyer’s work and 35% of a legal assistant’s work can be automated in the US. Top law schools like Harvard have already begun to integrate Artificial Intelligence into the curriculum. Legal tech start-up companies have begun developing applications that assist law firms with completing low-risk legal processes. These applications can enable lawyers to focus on more work that requires their specific expertise. The automation of processes like contract reviewing, enforcement of negotiations (smart contracts) and client intake (expert systems) allows law firms to streamline their procedures and improve efficiency. In addition, automation benefits small-to-medium law firms that do not have the resources to utilize junior talent on such routine tasks. The increase of law firms utilizing automated applications could result into legal tech becoming a necessity in the industry. Digital Reason CEO, Tim Estes, stated that those who refuse the opportunity to integrate AI in their workflow are “most at risk.” In 2018, Forbes reported a 713% increase in investments in legal tech. This rapid growth is reflective of law firms beginning to “cede business to… new model legal providers… that meld technological, business and legal expertise.” == Access to law and justice == It has been widely estimated for at least the last generation that all the programs and resources devoted to ensuring access to justice address only 20% of the civil legal needs of low-income people in the United States. Drawing on this experience, in late 2011, the U.S. government-funded Legal Services Corporation decided to convene a summit of leaders to explore how best to use technology in the access-to-justice community. The group adopted a mission for The Summit on the Use of Technology to Expand Access to Justice (Summit) consistent with the magnitude of the challenge: "to explore the potential of technology to move the United States toward providing some form of effective assistance to 100% of persons otherwise unable to afford an attorney for dealing with essential civil legal needs". In April 2017, joined by Microsoft and Pro Bono Net, the Legal Services Corporation (LSC) announced a pilot program to develop online, statewide legal portals to direct individuals with civil legal needs to the most appropriate forms of assistance. == Technological limitations == Current research in subjects such as computational privacy, explainable machine learning, Bayesian deep learning, knowledge-intensive machine learning, and transfer learning reveals that we do not yet have the technology to enable Level 4 to 6 AI lawbots. In 2023, OpenLaw began developing a model called Law Bot, which interacts in a conversational way as an attorney. The dialogue format makes it possible for Law Bot to answer follow-up questions, challenge incorrect premises, and reject inappropriate requests. Currently, they try to ensure it is in full compliance with all laws and regulations while conducting further beta testing before releasing it to the general public.

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  Sanad (government app)

  Sanad (Arabic: سند) is the official digital identity and e-government services application of the Hashemite Kingdom of Jordan. Developed and managed by the Ministry of Digital Economy and Entrepreneurship, the app provides a unified platform for accessing a range of public services and personal records digitally. == Overview == Launched in February 2020, Sanad is part of Jordan's broader digital transformation strategy aimed at improving public service delivery and enhancing administrative efficiency. The app allows users to authenticate their identity digitally and access over 550 services from more than 50 government and private sector entities. == Features == Sanad provides a wide array of services, including: Viewing and managing official digital documents Applying for government services (e.g., jordanian passport issuance or renewal, health insurance) Accessing personal records (e.g., pension, property ownership) Digitally signing documents Paying utility bills and traffic fines Receiving and tracking official notifications The app is available on iOS, Android, and HarmonyOS platforms and supports both Arabic and English languages. == Digital Identity == A core feature of Sanad is the digital identity system, which enables secure login and authentication for all integrated services. Users must activate their digital identity at designated Sanad stations across Jordan to access the full suite of services. == Adoption and Impact == As of 2025, more than 1.6 million Jordanians have activated their digital identities through Sanad. The app has played a significant role in streamlining government interactions and reducing the need for in-person visits, especially during the COVID-19 pandemic. == Recent Developments == In 2025, the Ministry launched an updated version of the app with enhanced user experience and new services, including the e-passport issuance feature.

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  E-on Vue

  Vue is a software tool for world generation by Bentley Systems, with support for many visual effects, animations, and various other features. The tool has been used in several feature-length films. In 2024, Bentley Systems announced that Vue would be discontinued, and be freely available to those that still wish to use it. == Versions == == Features == This is a list of features as of the 2023 release of Vue: === Terrains === Heightfield terrains Procedural terrains Infinite terrains Planetary terrains Real-world terrains 3D terrain sculpting Terrain export === EcoSystem Instancing Technology === Material-based EcoSystems Global EcoSystems Dynamic EcoSystems 360° EcoSystem Population Paint EcoSystem instances EcoParticles Export EcoSystem populations === Vegetation === Built-in Plant editor Compatible with PlantFactory Vegetation assets === Atmosphere, Skies and Clouds === Standard atmospheric model Spectral atmospheric model Photometric atmospheric model Atmosphere presets Procedural Volumetric 3D cloud layers Standalone 3D Metaclouds Convert meshes to Clouds Cloud morphing Import OpenVDB Export standalone and cloud layer zones to OpenVDB Export skies as HDRI === Modeling === Primitive and Feature modeling 3D Text edition tool Metablobbing Hyperblobs Export baked hyperblobs Splines Built in Road Construction toolkit Random rock generator Export rocks === Texturing and UVs === Material presets PBR Substance support Node-based procedural materials Volumetric materials and Hypertextures Stacked UVs Unwrapped UVs Ptex === Interoperability, Integration And Export === Export single assets to generic 3D formats Full scene export Integration plugins Import and Export Camera data as FBX and Nuke.chan Python API ZBrush GoZ bridge === Animation === Animate objects, materials, atmospheres, clouds, waves... Automatic wind and breeze Localized wind effects per plant / per EcoSystem population Omni and directional ventilators for local modifications of plants Time spline editor Automatic keyframe creation Automatic synchronization of cameras and lights Animation export as AfterEffects Import motion tracking information === Lighting === Global illumination, Global Radiosity, Ambient occlusion Subsurface Scattering HDRI image based lighting Point light, Quadratic point light, Spotlight, Quadratic spotlight, Directional light Use IES distribution profiles on photometric lights Area lights, light panels, light portals Physically accurate caustics computation === Rendering === Render with Ray Tracer Render with Path Tracer Stereoscopic rendering 360/180 VR Panorama Render Option Spherical panoramic rendering Tone mapping options Multipass & G-Buffer Network rendering with HyperVue / RenderCows Network rendering with RenderNodes == Users == Blue Sky Studios Digital Domain DreamWorks Animation: Kung Fu Panda Industrial Light & Magic: Indiana Jones and the Kingdom of the Crystal Skull, Pirates of the Caribbean: Dead Man's Chest Sony Pictures Imageworks Warner Bros. Interactive Entertainment Weta Digital

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  Genigraphics

  Genigraphics is a large-format printing service bureau specializing in providing poster session services to medical and scientific conferences throughout the US and Canada. The company began in 1973 as a division of General Electric. == History == Genigraphics began as a computer graphics system, developed by General Electric in the late 1960s, for NASA to use in space flight simulation. The technologies thus developed provided a foundation for the company's expansion into the commercial market. The Computed Images System & Services division (CISS, to become Genigraphics Corporation) of GE delivered the first presentation graphics system to Amoco Oil's corporate headquarters in 1973. It was named the 100 Series, and was based on DEC's PDP 11 series of mini computer systems. The first Genigraphics systems (100 Series and 100A Series) used an array of buttons, dials, knobs and joysticks, along with a built in keyboard, as the means of user interface. The PDP-11/40 computer was housed in a tall cabinet and used random access magnetic tape drives (DECtape) for storing completed presentations. The graphics generator (Forox recorder) was capable of outputting 2,000 line resolution, suitable for 35mm and 72mm film and large sheet film positive using larger cassettes for recording. 4000 and 8000 line resolution was later achieved with duplex scanning and 4x scanning by modifying to the Forox recorder's settings menu. Subsequent models (100B,C,D,D+ and D+/GVP) replaced the knobs and dials with an on screen, text based menu system, a graphics tablet and a pen. The pen/tablet combination gave way to a mouse like device in later models, and served to provide the interface with the graphics tools. User interaction with the computer for functions such as media initialization or modem to modem data transfer required a DECwriter serial terminal. In 1982, GE divested the Genigraphics division along with a host of other "non essential" business units (Genitext, Geniponics) and Genigraphics Corporation was born. Shortly after the divestiture, the headquarters of Genigraphics was moved from Liverpool, New York to Saddle Brook, New Jersey. Major success followed as the company grew exponentially over the next few years selling both systems and slide creation services. Genigraphics film recorders produced high-resolution digital images on 35mm film. The computer-generated scenes for The Last Starfighter were calculated on a Cray X-MP supercomputer and mastered with a Genigraphics film recorder. At its peak, Genigraphics Corporation employed roughly 300 people in 24 offices worldwide, with revenues upwards of $70 million annually. By the late 1980s Genigraphics saw demand for its proprietary systems dwindle and began selling the MASTERPIECE 8770 film recorder and GRAFTIME software as a peripheral for DEC Vaxes, IBM PC AT’s, and Mac NuBus machines. But the MASTERPIECE film recorder proved too expensive to sell in volume. In 1988, the company began a partnership with Microsoft to help develop the PowerPoint software. In exchange, every copy of PowerPoint included a “Send to Genigraphics” link to have files sent to a Genigraphics service bureau to be produced as 35mm slides. This partnership continued until 2001. In 1989, after three years of flat revenue, Genigraphics sold its hardware business in order to focus on its service bureau business and partnership with Microsoft via PowerPoint. In 1994, all assets of Genigraphics, including equipment, software development, in-house artwork, trademarks, and rights to the Microsoft partnership, were sold to InFocus Corporation of Wilsonville, Oregon who continued to operate under the Genigraphics brand name. The twenty-four service bureaus were consolidated to a 20,000 square foot facility next to the FedEx hub in Memphis, Tennessee. This allowed PowerPoint slide orders to be received until 10pm and delivered across the United States by the following morning. In 1995, InFocus registered www.genigraphics.com and was among the first to offer a form of ecommerce allowing 35mm slides, color prints and transparencies, printed booklets, and digital projectors to be purchased online. In 1998, then current management bought Genigraphics from InFocus and have operated it continuously ever since as Genigraphics LLC. That same year, InFocus projector rentals were added to the “Send to Genigraphics” link in PowerPoint and Genigraphics became the rental and repair center for all InFocus national accounts. It also marked Genigraphics entry into the new industry of large format printing; leveraging their knowledge of, and access to, PowerPoint programming code to develop a proprietary printer driver to output directly to an Epson 9500 wide format printer. At the time, Genigraphics was the exclusive 35mm slide vendor for all Kinko’s stores in the United States and poster printing was added to the arrangement. In 2003, Genigraphics closed their 35mm slide E6 photo lab – one of the last high-volume commercial E6 labs in the US – and expanded their large format printing capabilities. Since 2003, Genigraphics has become a major player in the poster session market, providing printing and on-site services to medical and scientific conferences throughout the US and Canada. As of February 2019, over 150,000 medical or scientific ‘ePosters’ are made available through their ResearchPosters.com archive service. === Partnership with Microsoft and development of PowerPoint === As presentations began to be created on personal computers in the late 80’s, Genigraphics sought presentation software partners in Silicon Valley who would be interested in sending files to Genigraphics via dial-up modem to be produced on 35mm slides. In 1987, Michael Beetner, Director of Marketing Planning for Genigraphics, met with Robert Gaskins, head of Microsoft's Graphics Business Unit, who was leading the development of the newly released PowerPoint software. A joint development agreement between Microsoft and Genigraphics was agreed upon and announced at Mac World 1988. According to Erica Robles-Anderson and Patrik Svensson, "It would be hard to overestimate Genigraphics’ influence on PowerPoint. PowerPoint 2.0 was designed for Genigraphics film recorders. It shipped with Genigraphics color palettes, schemes, and the distinctively Genigraphics color-gradient backgrounds. The application contained a ‘Send to Genigraphics’ menu item that wrote the presentation to floppy disk or transmitted the order directly via modem. Within three and a half months PowerPoint orders accounted for ten percent of revenue at Genigraphics service centers. PowerPoint 3.0 was even more intimately dependent upon Genigraphics. The software incorporated a collection of clip art images and symbols that had been produced by hundreds of artists at dozens of service centers across tens of thousands of presentations. Genigraphics artists designed PowerPoint 3.0 colors, templates, and sample presentations. The software even used Genigraphics (rather than Excel) chart style. Bar charts were rendered two-dimensionally with apparent thickness added to make them seemingly recede from the axes. The technique made it easier for viewers to compare bar heights and estimate values from axis ticks and labels. Pie charts were handled analogously. Microsoft paid Genigraphics to produce more than 500 clip art drawings and symbols used in Microsoft programs.” In exchange for Genigraphics development efforts, Microsoft included a “Send to Genigraphics” link in every copy of PowerPoint through the 10.0 version (2000/2001). The arrangement came to an end when Microsoft restructured as a result of anti-trust lawsuits.

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  Agentive logic

  Agentive logic (also called the logic of action or logic of agency) is the field of philosophical logic and logic in computer science that studies formal representations of agents, their actions, and their abilities. An agentive logic in the narrower sense is a formal system whose primitive operators express that an agent does something, can do something, or sees to it that something is the case. Agentive logics generalise modal logic by adding modalities indexed to agents and to actions. Typical examples include: STIT logics (from sees to it that) with operators of the form [ i s t i t : φ ] {\displaystyle [i\ {\mathsf {stit}}:\varphi ]} meaning that agent i {\displaystyle i} sees to it that φ {\displaystyle \varphi } holds; dynamic logics of action with program-like modalities [ α ] φ {\displaystyle [\alpha ]\varphi } and ⟨ α ⟩ φ {\displaystyle \langle \alpha \rangle \varphi } meaning, roughly, that after every (respectively, some) execution(s) of action α {\displaystyle \alpha } , φ {\displaystyle \varphi } holds; logics with explicit agentive operators such as "can do", "brings about", or "is able to ensure". Agentive logics are used in action theory in philosophy, in the semantics of natural language, in the theory of program verification, and in artificial intelligence, where they underpin formalisms for reasoning about actions, planning, and intelligent agents. == Terminology and scope == The adjective agentive derives from the Latin agens ("one who acts") and originally referred to the grammatical agent of a verb. In logical contexts it designates operators or predicates whose primary argument position is an agent rather than a proposition alone, for example A i φ {\displaystyle A_{i}\varphi } ("agent i {\displaystyle i} does φ {\displaystyle \varphi } ") or C i φ {\displaystyle C_{i}\varphi } ("agent i {\displaystyle i} can bring about φ {\displaystyle \varphi } "). In contemporary literature, agentive logic is sometimes used narrowly for formal reconstructions of St. Anselm's modal account of facere ("to do"). More broadly, the term is used interchangeably with logic of action or logic of agency to cover a family of modal and dynamic logics designed to capture the structure of action and choice. == Historical background == === Medieval and early modern roots === Medieval logicians already explored analogies between modalities of action and alethic modalities such as possibility and necessity, for instance, in discussions of obligation and power. An influential early agentive analysis is due to St. Anselm (11th century), who treated "doing φ {\displaystyle \varphi } " as a kind of modal operator on propositions, anticipating later modal logics of agency. Modern reconstructions of Anselm's theory show that the resulting "agentive logic" can be modelled with neighbourhood semantics and satisfies a recognisable square of opposition. === Modern logic of action === Modern study of the logic of action began in the mid-20th century, parallel to developments in deontic logic and tense logic. Early systems were proposed by Georg Henrik von Wright, Stig Kanger, and others, often motivated by questions about norms and responsibility. From the 1960s onward, two largely independent but eventually converging traditions emerged: a branching-time tradition, culminating in STIT logics, emphasising agents' choices among possible futures; and dynamic logics of programs and actions, developed within computer science to reason about program execution. In the 1990s and 2000s, action logics were further developed in connection with knowledge representation, planning, and multi-agent systems in AI, and with dynamic and update semantics in linguistics. == Core ideas == Despite their diversity, most agentive logics share some general themes: Agents are treated as explicit indices of modal operators, as in [ i d o e s ] φ {\displaystyle [i\ {\mathsf {does}}]\varphi } or C i φ {\displaystyle C_{i}\varphi } . Actions are represented either implicitly, via changes between possible worlds along an accessibility relation, or explicitly, as terms denoting primitive and composite actions. Choice and ability are captured by modalities describing what an agent can ensure, usually relative to assumptions about the environment and other agents. Formal properties such as closure under composition, interaction between different agents, and connections to obligation (what an agent ought to do) and knowledge (what an agent knows how to do) are investigated. == STIT logics == STIT ("sees to it that") logics, originating in work by Nuel Belnap and collaborators, treat agency in a branching-time framework. A STIT model consists of a partially ordered set of moments with a tree-like structure, sets of histories (maximal branches through the tree), and for each agent at each moment, a partition of the histories through that moment representing the choices available to the agent. Intuitively, an agent's action at a moment determines which equivalence class (choice cell) of histories becomes actual; a formula [ i s t i t : φ ] {\displaystyle [i\ {\mathsf {stit}}:\varphi ]} is true at a history–moment pair if φ {\displaystyle \varphi } holds on all histories in the choice cell corresponding to the agent's current action. Different STIT operators have been distinguished, notably: the Chellas STIT operator, often written [ i c s t i t : φ ] {\displaystyle [i\ {\mathsf {cstit}}:\varphi ]} , which requires only that the agent's choice guarantees φ {\displaystyle \varphi } ; and the deliberative STIT operator, [ i d s t i t : φ ] {\displaystyle [i\ {\mathsf {dstit}}:\varphi ]} , which additionally requires that φ {\displaystyle \varphi } is not already historically necessary. STIT frameworks have been extended with group agency operators, temporal modalities, epistemic operators, and deontic operators to study responsibility, collective action, and obligations under indeterminism. == Dynamic logics of action == Dynamic logic was originally developed to reason about the behaviour of computer programs, treating program execution as a kind of action. In propositional dynamic logic (PDL), action terms α , β , … {\displaystyle \alpha ,\beta ,\dots } denote abstract programs or actions, and formulas of the form [ α ] φ {\displaystyle [\alpha ]\varphi } and ⟨ α ⟩ φ {\displaystyle \langle \alpha \rangle \varphi } express that all, respectively some, terminating executions of α {\displaystyle \alpha } lead to states where φ {\displaystyle \varphi } holds. From the standpoint of agentive logic, dynamic logic provides: a language for building complex actions from primitives via sequencing, choice, and iteration (e.g., α ; β {\displaystyle \alpha ;\beta } , α ∪ β {\displaystyle \alpha \cup \beta } , α ∗ {\displaystyle \alpha ^{}} ); a Kripke semantics in which actions correspond to labelled accessibility relations; and proof systems (such as Hoare logic and weakest precondition calculi) for reasoning about the correctness of action sequences. Extensions such as concurrent dynamic logic add operators for parallel composition, allowing reasoning about interacting processes and concurrent actions. John-Jules Ch. Meyer and others have argued that dynamic logic is a natural base for logics of agents, by adding modalities for knowledge, belief, and ability on top of the action modalities. Dynamic logics have also been applied to normative reasoning, yielding dynamic deontic logics where actions are related to obligations and permissions, and to dynamic epistemic logics in which information-changing actions such as announcements are modelled as programs. == Situation calculus and other action formalisms == In artificial intelligence, reasoning about action and change is often based on first-order languages that explicitly represent situations, events, and fluents (time-varying properties). The best known is situation calculus, introduced by John McCarthy and developed extensively by Raymond Reiter. In such formalisms: action terms name primitive actions; a function symbol (often d o {\displaystyle {\mathsf {do}}} ) maps an action and a situation to a successor situation; and axioms describe which fluents hold in which situations and how actions change them. Reiter's successor state axioms give compact specifications of how each fluent changes under all actions, and precondition axioms specify when actions are possible. Related formalisms include the event calculus and fluent calculus, which provide alternative ways of representing events and their effects. While these systems are often first-order rather than modal, they are closely related to agentive logics: their action terms and transition structures can be seen as providing models for dynamic or STIT-style modalities, and conversely, dynamic logics can be used as abstract specification languages for such AI formalisms. == Ability, agency, and related modalities == Many agentive logics introduce explicit operators for ability or "can-do"

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  Color clock

  The color clock, or color timer, is a part of the video circuitry of computer graphics hardware that works with analog color television systems. The clock is timed to match the timing of the color standard it works with, typically NTSC or PAL, ensuring that the data being read from the computer memory to create the image on-screen is in sync with the display. Depending on the speed of the color clock, the product of the resolution and number of colors is defined. Slow color clocks of many early games consoles and home computers resulted in limited color palettes at the highest resolutions.

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  Nagarik App

  Nagarik App (translation: Citizen App) is a mobile application launched by the Government of Nepal to provide government-related services in a single online platform. The app was developed to facilitate an easier, systematic, and simplified delivery of government services to Nepali citizens digitally. The app was launched to play a pivotal role in revolutionizing the way citizens interact with the government. It offers government services through a single unified platform, minimizing the need for citizens to navigate multiple channels or physical offices for their diverse needs of government services. The services are added gradually according to the needs and services required. The government aims to reduce the physical queues and the need to be physically present to get services from the different government offices. One can get services online round-the-clock even during holidays. As of now, 25 services are included in the app, ranging from Police Clearance Report to Voters Card. The app contains and provides a vast range of government services. The app was launched on the occasion of the fourth National Information and Communication Technology Day, 2021 (2078 BS). The event marked a significant milestone in Nepal’s digital transformation journey. It aims to reduce all the bureaucratic hurdles that the citizens have been facing and make government services more efficient and convenient. In Oct 20, 2024, a E-Chalan was introduced for managing traffic violations in initially piloting in Kathmandu Valley. The Kathmandu Valley Traffic Police Office announced that physical licenses would no longer be confiscated for traffic rule violations. Instead, a "Digital Chit (E-Chalan)" system was implemented, allowing drivers to pay fines electronically. Integrated with the NagarikApp, the system enables police to access drivers' licenses, record violations, and update details directly in the app. == Features and Services == Inland Revenue Department (Nepal) PAN Registration Election Commission (Nepal) Voter Card Pre-Registration and Details Nepal Police Online Clearance Report Traffic Violations and Fine Payment Nepal Passport, Driving License, National Identity Card (NID), Citizenship, and Voter ID link details My Municipality (Includes contact info of the representatives, services such as ambulance, nearby police, and budget programs and plans) The Government Press ID card PF/PAN/SST/CIT statements can be viewed Nagarik Pahichan Dwar (Online bank accounts can be opened and KYC can be verified for selected banks using the QR) == Awards and honors == Each year, World Summit Award honors outstanding digital applications and solutions across various categories. The winners of the World Summit Award represent the pinnacle of innovation in their respective categories. Nagarik App was selected among 180 participants and won the World Summit Award of 2022 in Government and Citizen Engagement category. == Latest Statistics & Usage Trends (2082 BS / 2025 AD) == As of August 2025, over 1.5 million Nepali citizens have registered and actively use the Nagarik App, according to the National Information Technology Center (NITC). The majority of daily logins come from: Kathmandu Valley – 37% of total users Province 1 (Koshi) – 19% of total users Bagmati Province – 15% of total users On average, 45,000+ transactions (service requests, document verifications, and payments) are processed through the app each day. The most-used services include: PAN Card Registration – 28% of total requests Police Clearance Report – 22% Driving License Linking & E-Chalan Payment – 18% Vehicle Tax Payment – 14% Source: Internal report from NITC, July 2025 == Step-by-Step: How to Link Your Driving License with Nagarik App == Update the App – Install the latest version from Play Store or App Store. Login or Register – Ensure your SIM is registered in your own name. Go to “Transport Services” in the menu. Select “Driving License” – Enter your license number and date of birth. Verify via OTP – Sent to your registered mobile number. Confirmation – Your digital license will appear inside the app. This guide is continuously updated to reflect the latest rules from the Kathmandu Valley Traffic Police Office and changes in NITC’s backend system. For in-depth details, step-by-step tutorials, and the most recent Nagarik App updates, visit the full article on The Bipin Blog.

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  System Service Descriptor Table

  The System Service Descriptor Table (SSDT) is an internal dispatch table within Microsoft Windows. == Function == The SSDT maps syscalls to kernel function addresses. When a syscall is issued by a user space application, it contains the service index as parameter to indicate which syscall is called. The SSDT is then used to resolve the address of the corresponding function within ntoskrnl.exe. In modern Windows kernels, two SSDTs are used: One for generic routines (KeServiceDescriptorTable) and a second (KeServiceDescriptorTableShadow) for graphical routines. A parameter passed by the calling userspace application determines which SSDT shall be used. == Hooking == Modification of the SSDT allows to redirect syscalls to routines outside the kernel. These routines can be either used to hide the presence of software or to act as a backdoor to allow attackers permanent code execution with kernel privileges. For both reasons, hooking SSDT calls is often used as a technique in both Windows kernel mode rootkits and antivirus software. In 2010, many computer security products which relied on hooking SSDT calls were shown to be vulnerable to exploits using race conditions to attack the products' security checks.

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  Cognitive robotics

  Cognitive robotics or cognitive technology is a subfield of robotics concerned with endowing a robot with intelligent behavior by providing it with a processing architecture that will allow it to learn and reason about how to behave in response to complex goals in a complex world. Cognitive robotics may be considered the engineering branch of embodied cognitive science and embodied embedded cognition, consisting of robotic process automation, artificial intelligence, machine learning, deep learning, optical character recognition, image processing, process mining, analytics, software development and system integration. == Core issues == While traditional cognitive modeling approaches have assumed symbolic coding schemes as a means for depicting the world, translating the world into these kinds of symbolic representations has proven to be problematic if not untenable. Perception and action and the notion of symbolic representation are therefore core issues to be addressed in cognitive robotics. == Starting point == Cognitive robotics views human or animal cognition as a starting point for the development of robotic information processing, as opposed to more traditional artificial intelligence techniques. Target robotic cognitive capabilities include perception processing, attention allocation, anticipation, planning, complex motor coordination, reasoning about other agents and perhaps even about their own mental states. Robotic cognition embodies the behavior of intelligent agents in the physical world (or a virtual world, in the case of simulated cognitive robotics). Ultimately, the robot must be able to act in the real world. == Learning techniques == === Motor Babble === A preliminary robot learning technique called motor babbling involves correlating pseudo-random complex motor movements by the robot with resulting visual and/or auditory feedback such that the robot may begin to expect a pattern of sensory feedback given a pattern of motor output. Desired sensory feedback may then be used to inform a motor control signal. This is thought to be analogous to how a baby learns to reach for objects or learns to produce speech sounds. For simpler robot systems, where, for instance, inverse kinematics may feasibly be used to transform anticipated feedback (desired motor result) into motor output, this step may be skipped. === Imitation === Once a robot can coordinate its motors to produce a desired result, the technique of learning by imitation may be used. The robot monitors the performance of another agent and then the robot tries to imitate that agent. It is often a challenge to transform imitation information from a complex scene into a desired motor result for the robot. Note that imitation is a high-level form of cognitive behavior and imitation is not necessarily required in a basic model of embodied animal cognition. === Knowledge acquisition === A more complex learning approach is "autonomous knowledge acquisition": the robot is left to explore the environment on its own. A system of goals and beliefs is typically assumed. A somewhat more directed mode of exploration can be achieved by "curiosity" algorithms, such as Intelligent Adaptive Curiosity or Category-Based Intrinsic Motivation. These algorithms generally involve breaking sensory input into a finite number of categories and assigning some sort of prediction system (such as an artificial neural network) to each. The prediction system keeps track of the error in its predictions over time. Reduction in prediction error is considered learning. The robot then preferentially explores categories in which it is learning (or reducing prediction error) the fastest. == Other architectures == Some researchers in cognitive robotics have tried using architectures such as (ACT-R and Soar (cognitive architecture)) as a basis of their cognitive robotics programs. These highly modular symbol-processing architectures have been used to simulate operator performance and human performance when modeling simplistic and symbolized laboratory data. The idea is to extend these architectures to handle real-world sensory input as that input continuously unfolds through time. What is needed is a way to somehow translate the world into a set of symbols and their relationships. == Questions == Some of the fundamental questions to be answered in cognitive robotics are: How much human programming should or can be involved to support the learning processes? How can one quantify progress? Some of the adopted ways are reward and punishment. But what kind of reward and what kind of punishment? In humans, when teaching a child, for example, the reward would be candy or some encouragement, and the punishment can take many forms. But what is an effective way with robots?

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  National Cyber Security Policy 2013

  National Cyber Security Policy is a policy framework by Department of Electronics and Information Technology (DeitY) It aims at protecting the public and private infrastructure from cyber attacks. The policy also intends to safeguard "information, such as personal information (of web users), financial and banking information and sovereign data". This was particularly relevant in the wake of US National Security Agency (NSA) leaks that suggested the US government agencies are spying on Indian users, who have no legal or technical safeguards against it. Ministry of Communications and Information Technology (India) defines Cyberspace as a complex environment consisting of interactions between people, software services supported by worldwide distribution of information and communication technology. == Reason for Cyber Security policies == India had no Cyber security policy before 2013. In 2013, The Hindu newspaper, citing documents leaked by NSA whistle-blower Edward Snowden, has alleged that much of the NSA surveillance was focused on India's domestic politics and its strategic and commercial interests. This sparked a furore among people. Under pressure, the government unveiled a National Cyber Security Policy 2013 on 2 July 2013. == Vision == To build a secure and resilient cyberspace for citizens, business, and government and also to protect anyone from intervening in user's privacy.It mentioned a five year target of training five lakh cyber security personnel by 2018. == Mission == To protect information and information infrastructure in cyberspace, build capabilities to prevent and respond to cyber threat, reduce vulnerabilities and minimize damage from cyber incidents through a combination of institutional structures, people, processes, technology, and cooperation. == Objective == Ministry of Communications and Information Technology (India) define objectives as follows: To create a secure cyber ecosystem in the country, generate adequate trust and confidence in IT system and transactions in cyberspace and thereby enhance adoption of IT in all sectors of the economy. To create an assurance framework for the design of security policies and promotion and enabling actions for compliance to global security standards and best practices by way of conformity assessment (Product, process, technology & people). To strengthen the Regulatory Framework for ensuring a SECURE CYBERSPACE ECOSYSTEM. To enhance and create National and Sectoral level 24x7 mechanism for obtaining strategic information regarding threats to ICT infrastructure, creating scenarios for response, resolution and crisis management through effective predictive, preventive, protective response and recovery actions. -To improve visibility of integrity of ICT products and services by establishing infrastructure for testing & validation of security of such product. To create workforce for 500,000 professionals skilled in next 5 years through capacity building skill development and training. To provide fiscal benefit to businesses for adoption of standard security practices and processes. To enable Protection of information while in process, handling, storage & transit so as to safeguard privacy of citizen's data and reducing economic losses due to cyber crime or data theft. To enable effective prevention, investigation and prosecution of cybercrime and enhancement of law enforcement capabilities through appropriate legislative intervention. == Strategies == Creating a secured Ecosystem. Creating an assurance framework. Encouraging Open Standards. Strengthening The regulatory Framework. Creating a mechanism for Security Threats Early Warning, Vulnerability management, and response to security threats. Securing E-Governance services. Protection and resilience of Critical Information Infrastructure. Promotion of Research and Development in cyber security. Reducing supply chain risks Human Resource Development (fostering education and training programs both in formal and informal sectors to Support the Nation's cyber security needs and build capacity. Creating cyber security awareness. Developing effective Public-Private partnerships. To develop bilateral and multilateral relationships in the area of cyber security with another country. (Information sharing and cooperation) a Prioritized approach for implementation.

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  Webull

  Webull Corporation, often stylized as simply Webull, is a U.S.-based financial services holding company headquartered in St. Petersburg, Florida. It owns and operates the Webull electronic trading platform for self-directed retail investors. Depending on jurisdiction, the Webull platform offers trading in stocks, exchange-traded funds (ETFs), options, margin, bonds, cryptocurrency and futures, as well as market-data tools. Webull began operations in 2016 under Hunan Fumi Information Technology, a China-based financial technology company founded by Wang Anquan. It launched U.S. brokerage services through Webull Financial LLC in 2018 and expanded during the retail-trading boom of 2020 and 2021. In April 2025, Webull became a publicly traded company on the Nasdaq through a merger with special-purpose acquisition company SK Growth Opportunities Corporation. The company's U.S. brokerage revenue relies substantially on payment for order flow, with options trading accounting for the larger share of its order-flow rebates in 2025. Webull has faced regulatory actions related to options customer approvals, complaint handling, suspicious activity reporting, social-media marketing and customer disclosures. It has also faced scrutiny from U.S. lawmakers and state officials over its historical and operational ties to China and the handling of U.S. customer data. == History == === Founding === Webull was founded in 2016 under Hunan Fumi Information Technology, a China-based financial technology company, by Wang Anquan, a former employee of Alibaba Group and Xiaomi. Hunan Fumi Information Technology received backing from Xiaomi, Shunwei Capital, and other investors in China. Fumi Technology was a Hunan-based fintech start-up incubated by Xiaomi and raised about CNY200 million (approximately US$30 million) in a Series B financing round in 2018. On May 24, 2017, Webull Financial LLC was established as a Delaware limited liability company. It began offering brokerage services in the United States in May 2018. Wang hired Anthony Denier as CEO of the U.S. brokerage that year and the two mapped out their strategy on napkins at a Mexican restaurant in New York City. Webull Corporation was incorporated in the Cayman Islands in September 2019 as the group's holding company. === Retail trading boom === In May 2020, the company received SEC approval to launch a robo-advisor on its platform. By August 2020, the platform had over 11 million registered users, and in October 2020, it had 750,000 daily active users. Webull introduced options trading in 2020 and later added cryptocurrency trading through a separate digital-asset business. In November 2020, Webull began supporting cryptocurrency transactions. In December 2020, Webull launched trading services in Hong Kong. During the GameStop short squeeze in January 2021, Webull gained attention as some retail traders looked for alternatives to Robinhood. On January 27, 2021, Webull recorded its highest-ever number of active daily users, at 952,000, and the Webull app was downloaded across the Apple App and Google Play stores an estimated 100,000 times. That week, approximately 1.2 million people downloaded the Webull mobile app, which the company reported as a 1,548% week-over-week increase. On January 28, 2021, Webull was directed by its clearing house to temporarily halt buy orders for stocks affected by the GameStop short squeeze. In June 2021, Webull was reported to be considering a U.S. initial public offering that could raise up to $400 million. === Restructuring and expansion === Webull restructured its China-related corporate arrangements in 2022 and later stated that Hunan Fumi was no longer affiliated with the group. In 2022 and 2023, Webull expanded in several non-U.S. markets, including Singapore, Australia, South Africa, Japan, the United Kingdom and Indonesia. In June 2023, Webull moved cryptocurrency trading to a separate app called Webull Pay. By the end of 2023, Webull had 4.3 million funded accounts and US$8.2 billion in customer assets. In January 2024, Anthony Denier was promoted to group president of Webull Corporation. In November 2024, Webull launched overnight, or extended-hours, trading, expanding the trading window of U.S. stocks for users inside and outside the United States. === SPAC merger and Nasdaq listing === On February 28, 2024, Webull agreed to go public through a business combination with SK Growth Opportunities Corporation (NASDAQ: SKGR), a special-purpose acquisition company, in a deal that valued the company at approximately US$7.3 billion. The proposed valuation drew scrutiny because of Webull's limited financial disclosure at announcement, reliance on payment for order flow and small expected public float. SK Growth shareholders approved the business combination on March 30, 2025, and the transaction closed on April 10, 2025. Webull's Class A ordinary shares and warrants began trading on the Nasdaq on April 11, 2025 under the ticker symbols BULL and BULLW (incentive warrants traded under BULLZ until their redemption in June 2025). The merger brought Webull to the public market but generated little cash for the company: after shareholder redemptions, Webull disclosed net proceeds of US$430,066 from the transaction. After the listing, Webull's shares experienced extreme volatility, rising as much as 500% to US$79.56 on April 14, 2025, after closing at US$13.25 on the prior trading day. The initial post-listing surge increased the value of Webull holdings owned by earlier investors, including RIT Capital Partners, which had first invested in Webull in 2021. In April 2026, after Webull's shares had fallen about 70% over the previous year, the company authorized a US$100 million share repurchase program. == Business model and financials == Webull provides a self-directed electronic trading platform available through mobile, desktop and web applications. Depending on jurisdiction, the platform offers trading in stocks, exchange-traded funds, options, margin, futures, fixed income products, cryptocurrency, cash management features and market data tools. In the United States, Webull Financial LLC is a registered broker-dealer and member of FINRA and the Securities Investor Protection Corporation, while Webull operates in other markets through locally licensed brokerage subsidiaries. Webull operates a commission-free or low-cost brokerage model for self-directed retail investors. In the United States, a substantial part of its trading-related revenue comes from payment for order flow, while in some non-U.S. markets the company more commonly charges commissions directly to customers. The platform is aimed at more active retail investors, including users seeking options tools, extended-hours trading and real-time market data. For 2025, Webull reported total revenue of US$571.0 million, up from US$390.2 million in 2024. Equity and option order-flow rebates accounted for US$304.1 million, or 53.3% of revenue, making order-flow rebates the company's largest reported revenue category. Interest-related income accounted for US$154.3 million, handling charge income for US$87.3 million and other revenue for US$25.3 million. Options were the larger component of the company's order-flow rebates in 2025, generating US$210.0 million compared with US$94.2 million from equities. Webull also generates revenue from interest-related activities, including margin financing, customer bank deposits, stock lending and corporate bank deposits. The company has stated that its interest-related income is affected by interest rates, customer cash balances, margin balances and demand for stock lending. The company had approximately 20 million registered users worldwide as of February 2024. As of December 31, 2025, it reported 26.8 million registered users, 5.0 million funded accounts and US$24.6 billion in customer assets. As of March 2025, Webull operated in Hong Kong, Singapore, Australia, South Africa, Japan, the United Kingdom, the United States, Indonesia, Canada, Brazil, Thailand, Malaysia and Mexico. == Marketing and sponsorships == Webull has used paid digital advertising, referral incentives, free-stock promotions, affiliate marketing and sports sponsorships to acquire customers and promote its brand. In its 2025 annual filing, the company reported marketing and branding expenses of US$152.3 million in 2023, US$138.7 million in 2024 and US$135.9 million in 2025. Webull said most of its advertising and promotion costs were related to paid search and paid social advertising, and that it had reduced free-stock promotions while shifting toward deposit- and asset-transfer-based incentives. In September 2021, BSE Global, the parent company of the Brooklyn Nets and New York Liberty, entered into a global multi-year agreement with Webull. Under the agreement, Webull became an official sponsor and online brokerage partner of the teams, with branding that included a jersey patch on Brooklyn Nets uniforms. Spo

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  Waveform graphics

  Waveform graphics is a simple vector graphics system introduced by Digital Equipment Corporation (DEC) on the VT55 and VT105 terminals in the mid-1970s. It was used to produce graphics output from mainframes and minicomputers. DEC used the term "waveform graphics" to refer specifically to the hardware, but it was used more generally to describe the whole system. The system was designed to use as little computer memory as possible. At any given X location it could draw two dots at given Y locations, making it suitable for producing two superimposed waveforms, line charts or histograms. Text and graphics could be mixed, and there were additional tools for drawing axes and markers. The waveform graphics system was used only for a short period of time before it was replaced by the more sophisticated ReGIS system, first introduced on the VT125 in 1981. ReGIS allowed the construction of arbitrary vectors and other shapes. Whereas DEC normally provided a backward compatible solution in newer terminal models, they did not choose to do this when ReGIS was introduced, and waveform graphics disappeared from later terminals. == Description == Waveform graphics was introduced on the VT55 terminal in October 1975, an era when memory was extremely expensive. Although it was technically possible to produce a bitmap display using a framebuffer using technology of the era, the memory needed to do so at a reasonable resolution was typically beyond the price point that made it practical. All sorts of systems were used to replace computer memory with other concepts, like the storage tubes used in the Tektronix 4010 terminals, or the zero memory racing-the-beam system used in the Atari 2600. DEC chose to attack this problem through a clever use of a small buffer representing only the vertical positions on the screen. Such a system could not draw arbitrary shapes, but would allow the display of graph data. The system was based on a 512 by 236 pixel display, producing 512 vertical columns along the X-axis, and 236 horizontal rows on the Y-axis. Y locations were counted up from the bottom, so the coordinate 0,0 was in the lower left, and 511, 235 in the upper right. Had this been implemented using a framebuffer with each location represented by a single bit, 512 × 236 × 1 = 120,832 bits, or 15,104 bytes, would have been required. At the time, memory cost about $50 per kilobyte, so the buffer alone would cost over $700 (equivalent to $4,570 in 2025). Instead, the waveform graphic system used one byte of memory for each X axis location, with the byte's value representing the Y location. This required only 512 bytes for each graph, a total of 1024 bytes for the two graphs. Drawing a line required the programmer to construct a series of Y locations and send them as individual points, the terminal could not connect the dots itself. To make this easier, the terminal automatically incremented the X location every time an Y coordinate was received, so a graph line could be sent as a long string of numbers for subsequent Y locations instead of having to repeatedly send the X location every time. Drawing normally started by sending a single instruction to set the initial X location, often 0 on the left, and then sending in data for the entire curve. The system also included storage for up to 512 markers on both lines. These were always drawn centered on the Y value of the line they were associated with, meaning that a simple on/off indication for X locations was all that was needed, requiring only 1024 bits, or 128 bytes, in total. The markers extended 16 pixels vertically, and could only be aligned on 16-pixel boundaries, so they were not necessarily centered across the underlying graph. Markers were used to indicate important points on the graph, where a symbol of some sort would normally be used. The system also allowed a vertical line to be drawn for every horizontal location and a horizontal one at every vertical location. These were also stored as simple on/off bits, requiring another 128 bytes of memory. These lines were used to draw axes and scale lines, or could be used for a screen-spanning crosshair cursor. A separate set of two 7-bit registers held additional information about the drawing style and other settings. Although complex from the user's perspective, this system was easy to implement in hardware. A cathode ray tube produces a display by scanning the screen in a series of horizontal motions, moving down one vertical line after each horizontal scan. At any given instant during this process, the display hardware examines a few memory locations to see if anything needs to be displayed. For instance, it can determine whether to draw a marker on graph 0 by examining register 1 to see if markers are turned on, looking in the marker buffer to see if there is a 1 at the current X location, and then examining the Y location of graph 0 to see if it is within 16 pixels of the current scan line. If all of these are true, a spot is drawn to present that portion of the marker. As this will be true for 16 vertical locations during the scanning process, a 16-pixel high marker will be drawn. Sold alone, the VT55 was priced at $2,496 (equivalent to $16,295 in 2025),. Like other models of the VT50 series, the terminal could be equipped with an optional wet-paper printer in a panel on the right of the screen. This added $800 (equivalent to $5,223 in 2025) to the price. DEC also offered VT55 in a package with a small model of the PDP-11 to create one model of the DEClab 11/03 system. The DEClab normally sold for $14,000 (equivalent to $91,397 in 2025) with a DECwriter II (LA36) hard-copy terminal for $15,000 (equivalent to $97,925 in 2025), with the VT55. The system had I/O channels for up to 15 lab devices, and included libraries for FORTRAN and BASIC for reading the data and creating graphs. The fairly extensive VT55 Programmers Manual covered the latter in depth. == Commands and data == Data was sent to the terminal using an extended set of codes similar to those introduced on the VT52. VT52 codes generally started with the ESC character (octal 33, decimal 27) and was then followed by a single letter instruction. For instance, the string of four characters ESC H ESC J would reposition the cursor in the upper left (home) and then clear the screen from that point down. These codes were basically modeless; triggered by the ESC the resulting escape mode automatically exited again when the command was complete. Escape codes could be interspersed with display text anywhere in the stream of data. In contrast, the graphics system was entirely modal, with escape sequences being sent to cause the terminal to enter or exit graph drawing mode. Data sent between these two codes were interpreted by the graphics hardware, so text and graphics could not be mixed in a single stream of instructions. Graphics mode was entered by sending the string ESC 1, and exited again with the string ESC 2. Even the commands within the graphics mode were modal; characters were interpreted as being additional data for the previous load character (command) until another load character is seen. Ten load characters were available: @ - no operation, used to tell the terminal the last command is no longer active A - load data into register 0, selecting the drawing mode for the two graphs I - load data into register 1, selecting other drawing options H - load the starting X position (Horizontal) for the following commands B - load data for Y locations for graph 0 starting at the H position selected earlier J - load data for Y locations for graph 1 starting at the H position selected earlier C - store a marker on graph 0 at the following X location K - store a marker on graph 1 at the following X location D - draw a horizontal line at the given Y location L - draw a vertical line at the given X location X and Y locations were sent as 10-bit decimal numbers, encoded as ASCII characters, with 5 bits per character. This means that any number within the 1024 number space (210) can be stored as a string of two characters. To ensure the characters can be transmitted over 7-bit links, the pattern 01 is placed in front of both 5-bit numbers, producing 7-bit ASCII values that are always within the printable range. This results in a somewhat complex encoding algorithm. For instance, if one wanted to encode the decimal value 102, first you convert that to the 10-bit decimal pattern 0010010010. That is then split that into upper and lower 5-bit parts, 00100 and 10010. Then append 01 binary to produce 7-bit numbers 0100100 and 0110010. Individually convert back to decimal 40 and 50, and then look up those characters in an ASCII chart, finding ( and 2. These have to be sent to the terminal least significant character first. If these were being used to set the X coordinate, the complete string would be H2(. When used as X and Y locations for the graphs, extra digits were ignored. For instance, the 512 pixel X axis r

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  Jaggaer

  JAGGAER, formerly SciQuest, is a provider of cloud-based business automation technology for Business Spend Management. Its headquarters is in Durham, North Carolina. == Company history == SciQuest was established in 1995 as a B2B eCommerce exchange.The company went public with an IPO in 1999. In 2001, SciQuest transitioned from a B2B exchange company into eProcurement software and supplier enablement platforms. SciQuest was taken private in 2004 and continued to move into eProcurement, inventory management and accounts payable automation. SciQuest completed an IPO in September 2010, raising approximately $57 million. SciQuest, and its 510 person workforce, was taken private in June 2016 as part of a $509 million acquisition by Accel-KKR, a private equity firm headquartered in Menlo Park, CA. In 2017 SciQuest was rebranded as JAGGAER and announced increased focus on offering a complete, integrated source-to-pay suite. Along with the name change, the company expanded its market focus to manufacturing, healthcare, consumer packaged goods, retail, education, life sciences, logistics and the public sector. JAGGAER acquired the European direct materials procurement specialist Pool4Tool in June 2017 giving it end-to-end direct as well as indirect materials procurement coverage. JAGGAER acquired spend management company BravoSolution in 2017, and entered into a joint venture with United Arab Emirates-based Tejari. In February 2019 JAGGAER launched JAGGAER One, which unifies its full product suite on a single platform. In 2019 the UK-based private equity firm Cinven acquired a majority holding in the company. Jim Bureau was subsequently named JAGGAER's Chief Executive Officer. Bureau left the firm in March 2023, and Andy Hovancik was announced as the company's CEO in June. In 2024, JAGGAER was acquired by Vista Equity Partners, a private equity firm specializing in enterprise software investments. == Current positioning == As of April 2025, JAGGAER positions itself as "an enterprise procurement and supplier collaboration SaaS provider." Its core technology platform, which is called JAGGAER One, serves "direct and indirect procurement with specializations in Higher Education, Discrete and Process Manufacturing, and Public Sector." == Product Categories == The JAGGAER One platform supports the following products: Spend Analytics Category Management Supplier Management Sourcing Contracts eProcurement Invoicing Inventory Management Supply Chain Collaboration Quality Management == Acquisitions == SciQuest acquired the following companies: AECsoft - January 2011. Provider of supplier management and sourcing technology. Upside Software, Inc. - August 2012. Provider of contract lifecycle management (CLM) solutions. Spend Radar, LLC - October 2012, Provider of spend analysis software. CombineNet - September 2013, Provider of advanced sourcing software JAGGAER acquired the following companies: POOL4TOOL - June 2017, Provider of direct sourcing and supply chain management software BravoSolution - December 2017, Provider of global platform spend management solutions

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  Split screen (computing)

  Split screen is a display technique in computer graphics that consists of dividing graphics and/or text into non-overlapping adjacent parts, typically as two or four rectangular areas. This allows for the simultaneous presentation of (usually) related graphical and textual information on a computer display. TV sports adopted this presentation methodology in the 1960s for instant replay. Non-dynamic split screens differ from windowing systems in that the latter allowed overlapping and freely movable parts of the screen (the "windows") to present both related and unrelated application data to the user. In contrast, split-screen views are strictly limited to fixed positions. The split screen technique can also be used to run two instances of an application, potentially allowing another user to interact with the second instance. == In operating systems == Split screen modes are used by mobile operating systems to enable computer multitasking similar to the window interface present in desktop operating systems. Android supports split screen view of two apps natively on all devices, while certain devices, such as Samsung Galaxy Z TriFold, support three sumultaneous views. Split screen functionality is not supported on iOS, but a similar feature called Split View is present in iPadOS, first introduced in 2015 with the first generation of iPad Pro. == In video games == The split screen feature is commonly used in non-networked, also known as couch co-op, video games with multiplayer options. In its most easily understood form, a split screen for a multiplayer video game is an audiovisual output device (usually a standard television for video game consoles) where the display has been divided into 2-4 equally sized areas (depending on number of players) so that the players can explore different areas simultaneously without being close to each other. This has historically been remarkably popular on consoles, which until the 2000s did not have access to the Internet or any other network and is less common today with modern support for networked console-to-console multiplayer. In competitive split-screen games, it is customarily considered cheating to look at another player's screen section to gain an advantage. === History === Split screen gaming dates back to at least the 1970s, with games such Drag Race (1977) from Kee Games in the arcades being presented in this format. It has always been a common feature of two or more player home console and computer games too, with notable titles being Kikstart II for 8-bit systems, a number of 16-bit racing games (such as Lotus Esprit Turbo Challenge and Road Rash II), and action/strategy games (such as Toejam & Earl and Lemmings), all employing a vertical or horizontal screen split for two player games. Xenophobe is notable as a three-way split screen arcade title, although on home platforms it was reduced to one or two screens. The addition of four controller ports on home consoles also ushered in more four-way split screen games, with Mario Kart 64 and Goldeneye 007 on the Nintendo 64 being two well known examples. In arcades, machines tended to move towards having a whole screen for each player, or multiple connected machines, for multiplayer. On home machines, especially in the first and third person shooter genres, multiplayer is now more common over a network or the internet rather than locally with split screen. Starting from the late 2000s, the presence of split screen multiplayer has largely been declining due to the increasing prevalence of online multiplayer, though TechRadar reported a resurgence of split screen due to support from independent studios and increased interest from the players.

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  Language-Theoretic Security

  Language-theoretic security, or LangSec, is an approach to software security that focuses on input handling, complexity, and program design as strategies to improve the verifiability of computer programs. It was introduced in 2005 by Robert J. Hansen and Meredith L. Patterson at BlackHat and in 2011 by Len Sassaman and Patterson. It aims to create a formal description of which software is likely to have security vulnerabilities of particular classes, and why. It considers programs to have an inherent parser component, whether or not explicit, composed of that part of the program which operates on external input before that input is fully parsed. A central hypothesis of language-theoretic security is that vulnerabilities in software increase according to the computational power of the notional input-accepting automaton equivalent to this parser, using the definitions of automata theory. The lower bound on this computational power is the input language complexity of the program. The extent to which reducing this complexity is possible is a function of the specification of the communication protocol or file format the program takes as input. == Parsing as a security mechanism == The behaviour of a program is defined with reference to its expected input. Unexpected input being used by a program is a factor in numerous security bugs, including the so-called Android master key vulnerability (CVE-2013-4787), because accepting unexpected input renders the program's specification ambiguous. In that instance, the unexpected ambiguity came in the form of a ZIP file with duplicate filenames. If a program fully parses its input and only acts on input that unambiguously meets the specification, it follows that the program will avoid these types of vulnerabilities. This is an intentional inversion of the Postel principle. Accepting only unambiguous and valid input is a more formal requirement than input validation or sanitization, and narrows the number of possible but unanticipated program states that can be induced in an application via user input. Conversely, failure to do this is associated with security vulnerabilities. Input sanitization in particular is held to be an inadequate approach to avoiding malicious input because it inherently ignores context-sensitive properties of the input; it can therefore result in paradoxical effects, such as sanitization code activating otherwise inert cross-site scripting payloads in browsers. === Parser differentials === If the language of accepted program input is sufficiently simple, it is possible to verify that two implementations parse the same input language consistently. This is advantageous because it shows no parser differential exists between the two implementations. The requisite level of simplicity is theoretically that for which there is a solution to the equivalence problem. If the two parsers involved in CVE-2013-4787 were equivalent - that is, if they rendered the same output state given the same input state - the vulnerability could not have existed. One strategy for doing this is to publish machine-readable specifications of a format or protocol, and then use a parser generator to generate the parser code. An example of a parser generator built for this purpose is DaeDaLus. The combination of Lex with any of GNU Bison, ANTLR, or Yacc also accomplishes this. However, many parser generators allow the mixing of general purpose code with the parsing definitions, which weakens the guarantees provided by parsing. === Analysis of injection attacks === Injection attacks are generally the result of differences between the serializer (or "unparser") and the corresponding parser at a layer boundary in a system; therefore, they are a special case of parser differentials. In a SQL injection attack, for example, an attacker is able to cause the application with which they are interacting to serialize a SQL query that has different semantics than intended. In the simplest case where the payload ends a string and adds new code, the payload has crossed the code-data boundary in SQL. In language-theoretic security, this is treated as a bug in the serializer of the SQL query, which should instead be written in a way that constrains its possible outputs to those within the scope of the intended query. === Parser combinators === If a parser generator is not used, it is still possible to avoid implementation bugs by using parser combinator such as Nom to implement the parser code. This has the drawback of relying on a programmer correctly translating the specification into the language of the parser generator library, though this task is still less error-prone than hand-coding a parser. == Input format complexity == Complexity in computer programs is associated with security vulnerabilities. Within the domain of language-theoretic security, complexity is described with reference to the computational power of the abstract machine necessary to implement the program, or more particularly, to implement the parser for its input language. This complexity describes whether it is possible to show that there is no unintended or undesired functionality in the program which might be exploitable by an attacker. To be bounded in complexity, the program's input must be well-defined both in terms of form and of semantics. === Weird machines === A weird machine is a model of computation in a program that exists in parallel with, but is distinct from, the intended abstract model of computation in that program. Some classes of weird machine arise from the multi-layered nature of computer programs, or the context in which the programs run; others result from the unanticipated functionality a program has due to its complexity or to software bugs. The more complex the computation model of a program, the more likely it is to implement a weird machine. Depending on context, the weird machine may or may not be concretely useful for an attacker. Since the space of weird machines in the context of some program is the universe of all possible states that are not within the program's intended states, many exploited states including remote code execution and injection attacks belong to the domain of weird machines. A reduction in weird machines is therefore a likely correlate with reduced program vulnerability. === SafeDocs project === SafeDocs is a DARPA project undertaken in 2018 to take existing file formats, create safer subsets of them, and develop programming tools to work for the safer formats. The initial test case for this was PDF. The purpose of creating safer subsets in this case is to lower the minimum bound on parser complexity so that it becomes possible to create tools that will generate correct, normative parsers for them. == Relation to programming languages == The analytic framework of language-theoretic security assumes programs to be virtual machines that execute their input. A document that is read by an application is in this sense a form of machine code, in a generalization of the data as code idea, following the automata theory description of parsers. === Type-safe programming languages === Parsing input and serializing output are operations that consume one data type and emit another. A programming language can therefore check that data is correctly parsed and contains the expected structure by checking data types, and correct serializing (or unparsing) can be implemented as operations on the data types that are relevant to the program's output. This approach can be used to show that the recognizer and unparser patterns have been implemented. It is also possible to implement type checking across a distributed system to enforce parsing and unparsing of the expected structures and to verify that the assumptions made in designing the compositional properties of a distributed system have been followed. === Memory-safe programming languages === In the general case, spatial memory correctness is undecidable. If any proof of spatial memory correctness is to be made, it is therefore necessary to bound the complexity of the code. Interpreted languages such as Java and Python effectively accomplish this via runtime bounds checking, and frameworks for runtime bounds checking also exist for C. The effect of these strategies for spatial memory correctness are to create a halt state in place of a spatial memory correctness violation; therefore, it can be shown that the program will not violate spatial memory correctness, but in exchange, it cannot be shown in the general case that programs will not have runtime bounds checking exceptions. Some programming languages, such as Rust, accomplish this using borrow checking. The borrow checker acts to assure spatial memory correctness by compile-time reference counting. Code for which spatial memory correctness cannot be shown to not be violated therefore does not compile, inherently limiting the complexity of the spatial memory correctness of the program to what is decidable. Thi

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