AI Headshot Linkedin Generator

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  • Construction robots

    Construction robots

    Construction robots are a subset of industrial robots used for building and infrastructure construction on site, or in the production of materials and components offsite. A 2021 survey said 55% of construction companies in the United States, Europe, and China used robots in some form. This figure, however, reflects reported use across the construction value chain rather than widespread deployment of robots on active construction sites. Real-world adoption remains limited, with many robotic systems confined to pilot projects, controlled environments, or specific task applications rather than continuous on-site construction use. One of the main challenges in deploying robots on construction sites is the unstructured and variable nature of the environment, which differs fundamentally from controlled factory settings where industrial robots have traditionally operated. Some robots currently deployed on job sites assist with physically demanding or repetitive tasks: excavating, lifting heavy materials, surveying, laying out markers, tying rebar, and installing drywall. More advanced systems are being developed for exterior finishing, steel placement, masonry, and reinforced concrete work. In practice, rather than autonomous systems performing core building tasks, the most widely adopted robot applications on construction sites involve technologies such as aerial drones (or, less frequently, robot 'dogs' - for example, Boston Dynamics' Spot - or humanoid robots) used for surveying, inspection, and progress monitoring (the robots typically carry video and/or 360-degree cameras, LiDar scanners or other data capture devices, with data analysed using artificial intelligence and machine learning). Some emerging systems are designed as multifunctional construction robots, integrating multiple tools and capabilities within a single robotic platform to perform different stages of the construction process. These systems aim to improve operational flexibility and increase automation in complex construction environments. Experimental projects using robotic construction technologies and additive manufacturing have been demonstrated in several countries as part of broader efforts to industrialize the construction sector and improve productivity through automation and digitalization. == Features == Construction robots are generally required to meet the following criteria: Mobility: the ability to navigate around a construction site, including uneven terrain and confined spaces. Adaptability: the ability to handle components of variable size, weight, and shape. Environmental awareness: the ability to sense and respond to changing on-site conditions. Interactivity: the ability to operate alongside human workers and other equipment. Multitasking: the ability to perform several different operations within a single deployment. == Capabilities == Construction robots have been developed and tested for a range of on-site tasks, including: Progress monitoring — robots equipped with cameras and sensors can track construction progress and identify deviations from plans. Inspection — robots are used to investigate infrastructure at dangerous or inaccessible locations, reducing risk to human workers and eliminating human error. Wall construction — robotic systems can lay bricks and blocks with greater speed and consistency than manual labour. Earthmoving and material handling — autonomous excavators and haul trucks use GPS, lidar, and motion sensors to perform digging, trenching, and loading tasks with minimal human input. Grading and dozing — autonomous bulldozers use GPS, gyroscopes, and laser sensors to control blade angle and depth, improving surface finish accuracy and reducing material overuse. 3D printing — additive manufacturing systems can construct walls and structural elements directly from digital models. == Notable construction-related activities undertaken by robots == The distribution of robotic applications in construction varies across the project lifecycle. Most applications are concentrated in structural construction tasks such as masonry, concrete work, and assembly, while other phases, including planning, maintenance, and demolition, remain less represented. === Automated building systems === The Nisseki Yokohama Building (also known as Rail City Yokohama), a 30-storey office building in Yokohama, Japan, was constructed between 1994 and 1997 using the SMART system (Shimizu Manufacturing system by Advanced Robotics Technology), developed by Shimizu Corporation and a consortium of seven other Japanese companies. The system used automated horizontal hoists and vertical lifts to position steel beams, columns, precast concrete floor slabs, and prefabricated facade panels, with welding robots connecting structural elements under laser-guided precision. Each component was tracked by barcode to monitor progress and coordinate just-in-time delivery of materials. Obayashi Corporation developed the Advanced Building Construction System (ABCS), a similar automated platform used in several high-rise projects in Japan in the 1990s, including the NEC Head Office in Kanagawa (1997–2000). === Progress monitoring, inspection === Boston Dynamics' Spot was used in February 2024 to inspect sections of the M5 motorway in England for National Highways. A £15,000 humanoid robot (a G1 model from Chinese manufacturer Unitree) was deployed to capture 360-degree imagery and progress reports to support health and safety monitoring and reporting for UK contractor Tilbury Douglas in April 2026. In the US, Virginia Tech's ARCADE research lab is developing MARIO (Multi-Agent Robotic system for Inspection On-site), a heterogenous robotic system deploying multiple robots capable of different locomotion to perform remote real-time construction progress monitoring in complex construction sites. === Earthmoving === === Concrete works === Obayashi Corporation developed and deployed a robotic system for placing concrete layers in dam construction in Japan. A concrete floor finishing robot was deployed by Kajima and Tokimec in Japan. The MARK series were designed in 1984 to automate the levelling and trowelling of concrete slabs on construction sites, providing consistent finishing accuracy, improved efficiency, and reduced dependence on skilled labour === Masonry === SAM100 (Semi-Automated Mason), developed by Construction Robotics, is one of the first commercially available bricklaying robots for on-site masonry construction. In 2018, it was used in the construction of the University Arts Building at the University of Nevada, Reno — a $35.5 million facility — where it laid over 60,000 of the 100,000 bricks required, reducing the brick veneer installation time by approximately 50%. Hadrian X, developed by the Australian company Fastbrick Robotics, is a fully autonomous mobile bricklaying robot. In November 2022, it completed its first commercial project — five four-bedroom houses in Wellard, Western Australia. In February 2025, PulteGroup, one of the largest homebuilders in the United States, piloted Hadrian X on a site in Florida, constructing an entire house in a single day. === 3D printing === In May 2025, a residential building in Arinaga, Gran Canaria, Spain, was completed using 3D printing construction technology, as part of broader efforts to demonstrate robotic and additive manufacturing methods in the housing sector. In 2026, a three-storey apartment block in France was constructed using concrete 3D printing technology, three months faster than conventional building methods. Finland's Hyperion Robotics has opened a UK factory and used 3D printing with concrete to produce foundations for pipelines and for electricity substation bases, reducing time-consuming and weather-dependent onsite construction processes. == Social impact == The adoption of construction robots varies significantly by region and is shaped by labour market conditions, cultural attitudes, and regulatory frameworks. In Japan, construction robots have been embraced as a response to an ageing workforce and chronic labour shortages, and are generally viewed positively by the industry. In the United States, adoption has historically been slower, partly due to resistance from labour unions concerned about job displacement. Research suggests that the impact of automation on workers is uneven: while robots can create a productivity effect that benefits some workers, displacement effects are most pronounced among younger, less-educated workers in manufacturing-heavy regions. More than 60% of construction firms now report difficulty finding skilled operators, which has increased openness to automation as a practical solution to workforce shortages rather than a replacement for workers. In the UK, during onsite deployment of a humanoid robot for monitoring purposes, there were concerns that staff might think they were being watched ("It's not there to spy on people.... So, we insist that everyone is blurred out. N

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  • Transportation Economic Development Impact System

    Transportation Economic Development Impact System

    Transportation Economic Development Impact System (TREDIS) is an economic analysis system sold by consulting firm Economic Development Research Group that is used in planning major transportation investments in the US and Canada. The role of economic impact analysis and TREDIS in the transportation planning process is explained in guidebooks of the US Department of Transportation and the American Association of State Highway and Transportation Officials. TREDIS has been most commonly used for assessing the expected economic impacts of statewide highway programs, regional multi-modal plans and public transport investment. Its history and theoretical foundation are explained in peer reviewed journal articles. == How It Works == TREDIS has a series of modules that calculate different forms of impacts and benefits. One module is an accounting framework that calculates user benefits, including impacts on cargo transportation and commuting costs, based on transportation forecasting results. A second module calculates wider economic development benefits, including impacts on business productivity, economic development and multiplier effects from the input-output analysis. It applies an economic model to estimate impacts on jobs, income, gross regional product and business output, by sector of the economy. A third module applies cost-benefit analysis from alternative perspectives.

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  • Knowledge as a service

    Knowledge as a service

    Knowledge as a service (KaaS) is a computing service that delivers information to users, backed by a knowledge model, which might be drawn from a number of possible models based on decision trees, association rules, or neural networks. A knowledge as a service provider responds to knowledge requests from users through a centralised knowledge server, and provides an interface between users and data owners. KaaS is one of several cloud computing-dependent business models in which computer resources are sold on an on-demand and pay-as-you-use basis. == Overview == At the International Semantic Web Conference 2019, it was described how knowledge can be made live and evolve on the web allowing users to learn directly from elaborated knowledge, now appearing in the form of knowledge graphs. KaaS appear when knowledge graphs are accessed via services This is opposed to DaaS which might "compute large volumes of data; integrate and analyzes that data; and publish it in real-time, using Web service APIs" (from Data as a Service) where the KaaS is able to exploit context - both the context of the user in relation to their information requests of the KaaS (where and when they make the request) and also the context of the information in relation to some objective or purpose of the users either understood by the KaaS automatically or indicated to it by the user. == Differentiating knowledge from data == Conceptual models that make such a differentiation such as the so-called DIKW pyramid have existed for perhaps more than 40 years (see a 1974 journal article about this) however definitions are not stable and universally accepted (see the discussion about the conceptualizations of DIKW within the DIKW Wikipedia article that question value of wisdom). The knowledge component of DIKW is generally agreed to be an elusive concept which is difficult to define, however Rowley 2007, in a well known student textbook differentiated knowledge from data by stating that knowledge is "defined with reference to information" and that it contains more than just facts but also "beliefs and expectations". In relation to knowledge graphs, knowledge may be additional content they provide over and above pure data which is the definition of the categories, properties and relations between the concepts, data and entities that substantiate one, many or all domains of discourse (see the definition of Ontology). The ability to represent "beliefs and expectations", or other forms of not so straightforwardly explicit knowledge is an on-going area of improvement in information sciences (see Tacit knowledge) and, with relation to KaaS, the establishment of recent informatics mechanics to do so it critical to the legitimacy of KaaS as it is differentiated from just value-added DaaS. Knowledge graphs' ability to represent context via the definition of the categories, properties and relations between the concepts, data and entities that substantiate one, many or all domains of discourse that they provide (see the definition of Ontology) has led to the idea that supplying access to KNs might be a required competency of a KaaS. == Delivery of knowledge == Much service-delivered content is dependent on a session to provide much of the context that the user (client) needs to understand answers to questions. For example, using current HTTP internet protocols, a GET request to retrieve information identified by a URI, such as a web page, a client (a human or a machine) may have access information supplied automatically to enable that client to bypass paywalls or other content access controls. Such context, in this case about the client's information access allowances, can alter the information provided. In a logical extension to this internet protocols example, a server would receive from the client, either manually or automatically, a full context which would be information about the situation the client is in and this would allow the server to best interpret the client's request. Current internet protocols allow for formats, languages and related preferences to be expressed by clients but make no mention of what a client already knows and what they may understand. The recent Content Negotiation by Profile proposes additions to both the HTTP internet protocols and related services that allow clients to also request information - a response from the server - that accords with an identified information model. This then allows clients to indicate not just formats and languages that they understand (technically that they prefer) but also domains of discourse that that do, which is a step towards comprehensive client context provision.

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  • Wunderlist

    Wunderlist

    Wunderlist is a discontinued cloud-based task management application. It allowed users to create lists to manage their tasks from a smartphone, tablet, computer and smartwatch. Wunderlist was free; additional collaboration features were available in a paid version known as Wunderlist Pro, released April 2013. Wunderlist was created in 2011 by Berlin-based startup 6Wunderkinder (Engl.: 6Prodigies). The company was acquired by Microsoft in June 2015, at which time the app had over 13 million users. In April 2017, Microsoft announced that Wunderlist would eventually be discontinued in favor of Microsoft To Do, a new multi-platform app developed by the Wunderlist team that has direct integration with the company's Office 365 service. On December 6, 2019, Microsoft announced that it would shut down Wunderlist on May 6, 2020. After this date, the application would no longer sync but users could still import their content into Microsoft To Do. == History == In 2009, Wunderlist's CEO Christian Reber called on the social network platform XING for business partners to create a new to-do app. Frank Thelen responded and together Reber and Thelen developed first concepts for Wunderlist. The necessary seed funding was granted by High-Tech Gründerfonds and e42 GmbH. The first version of Wunderlist was launched on November 9, 2010. Initially, the program was created for desktop PCs and platforms such as Windows, Linux and Mac OS X. In December 2011, the app received approval for the iPhone. Subsequently, the developers released a version prepared for the iPad with the name Wunderlist HD. In September 2012, the developers announced a shutdown of their service Wunderkit. Instead they wanted to focus on creating a new version of Wunderlist, which was later on released in December 2012 under the name Wunderlist 2. In September 2013, the company announced it had over 5 million users. In July 2014, a new major update was released under the name of Wunderlist 3, with a new real-time sync architecture. Wunderlist reached 10 million users in December 2014. On June 1, 2015, it was announced that Microsoft had acquired 6Wunderkinder, makers of Wunderlist, for between US$100 million and US$200 million (~$258 million in 2024). Following its acquisition of the app, Microsoft announced in April 2017 a preview of To-Do, a multi-platform task management app developed by the Wunderlist team that was intended to eventually replace Wunderlist and incorporate most of its features. As of January 2019, To-Do had not yet reached feature parity with Wunderlist, with its team citing that the service had to be completely re-written to use Microsoft Azure instead of Amazon Web Services. Frustrated by the perceived lack of roadmap, in September 2019, Reber began to publicly ask Microsoft-related accounts on Twitter whether he could buy Wunderlist back. Shortly afterward, however, Microsoft unveiled updates to To-Do that make it more closely resemble Wunderlist. In December 2019, Microsoft announced that it would fully shut down Wunderlist as of May 6, 2020. The team responsible for creating Wunderlist, led by co-founder Christian Reber, created that Superlist app in early 2024. == Finances == In its initial round of funding, 100,000 euro was invested in 6Wunderkinder by Frank Thelen and others. In December 2010, High-Tech Gründerfonds invested 500,000 euro (approximately US$660,000) in the company. T-Venture also invested an undisclosed amount in the startup. In its Series A round of funding in November 2011, Atomico invested $4.2 million (~$5.76 million in 2024) while High-Tech Gründerfonds invested an undisclosed additional amount. In May 2012, High-Tech Gründerfonds sold off its stake in 6Wunderkinder to Earlybird Venture Capital. In November 2013, $19 million (~$25.2 million in 2024) was raised in a Series B round led by Sequoia Capital with participation from Earlybird and Atomico. == Awards == In 2013, Wunderlist for Mac was named App of the Year. Wunderlist was selected as a Google Play Top Developer in 2013. In 2014, Wunderlist won the "Golden Mi" award from Xiaomi, and also named as one of its Best Apps of 2014 was given a "Google Play Editor's Choice" award, and was named in Google Play's Best Apps of 2014 as well as Apple's Best of 2014.

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  • List of speech recognition software

    List of speech recognition software

    Speech recognition software is available for many computing platforms, operating systems, use models, and software licenses. Here is a listing of such, grouped in various useful ways. == Acoustic models and speech corpus (compilation) == The following list presents notable speech recognition software engines with a brief synopsis of characteristics. == Macintosh == == Cross-platform web apps based on Chrome == The following list presents notable speech recognition software that operate in a Chrome browser as web apps. They make use of HTML5 Web-Speech-API. == Mobile devices and smartphones == Many mobile phone handsets, including feature phones and smartphones such as iPhones and BlackBerrys, have basic dial-by-voice features built in. Many third-party apps have implemented natural-language speech recognition support, including: == Windows == === Windows built-in speech recognition === The Windows Speech Recognition version 8.0 by Microsoft comes built into Windows Vista, Windows 7, Windows 8 and Windows 10. Speech Recognition is available only in English, French, Spanish, German, Japanese, Simplified Chinese, and Traditional Chinese and only in the corresponding version of Windows; meaning you cannot use the speech recognition engine in one language if you use a version of Windows in another language. Windows 7 Ultimate and Windows 8 Pro allow you to change the system language, and therefore change which speech engine is available. Windows Speech Recognition evolved into Cortana (software), a personal assistant included in Windows 10. === Windows 7, 8, 10, 11 third-party speech recognition === Braina – Dictate into third party software and websites, fill web forms and execute vocal commands. Dragon NaturallySpeaking from Nuance Communications – Successor to the older DragonDictate product. Focus on dictation. 64-bit Windows support since version 10.1. Tazti – Create speech command profiles to play PC games and control applications – programs. Create speech commands to open files, folders, webpages, applications. Windows 7, Windows 8 and Windows 8.1 versions. Voice Finger – software that improves the Windows speech recognition system by adding several extensions to it. The software enables controlling the mouse and the keyboard by only using the voice. It is especially useful for aiding users to overcome disabilities or to heal from computer injuries. === Microsoft Speech API === The first version of the Microsoft Speech API was released for Windows NT 3.51 and Windows 95 in 1995, it was then part of Windows up to Windows Vista. This initial version already contained Direct Speech Recognition and Direct Text To Speech APIs which applications could use to directly control engines, as well as simplified 'higher-level' Voice Command and Voice Talk APIs. Speech recognition functionality included as part of Microsoft Office and on Tablet PCs running Microsoft Windows XP Tablet PC Edition. It can also be downloaded as part of the Speech SDK 5.1 for Windows applications, but since that is aimed at developers building speech applications, the pure SDK form lacks any user interface (numerous applications were available), and thus is unsuitable for end users. == Built-in software == Microsoft Kinect includes built-in software which allows speech recognition of commands. Older generations of Nokia phones like Nokia N Series (before using Windows 7 mobile technology) used speech-recognition with family names from contact list and a few commands. Siri, originally implemented in the iPhone 4S, Apple's personal assistant for iOS, which uses technology from Nuance Communications. Cortana (software), Microsoft's personal assistant built into Windows Phone and Windows 10. == Interactive voice response == The following are interactive voice response (IVR) systems: CSLU Toolkit Genesys HTK – copyrighted by Microsoft, but allows altering software for licensee's internal use LumenVox ASR Tellme Networks; acquired by Microsoft == Unix-like x86 and x86-64 speech transcription software == Janus Recognition Toolkit (JRTk) Mozilla DeepSpeech was developing an open-source Speech-To-Text engine based on Baidu's deep speech research paper. Weesper Neon Flow – professional voice-dictation software that provides offline speech-to-text processing on macOS and Windows using local AI models. It is not open source and offers a paid subscription after a 15‑day free trial. Vocalinux – open-source speech transcription software for Linux. == Discontinued software == IBM VoiceType (formerly IBM Personal Dictation System) IBM ViaVoice – Embedded version still maintained by IBM. No longer supported for versions above Windows Vista. Untested above macOS 10.4 or on Macintoshes with an Intel chipset. Quack.com; acquired by AOL; the name has now been reused for an iPad search app. SpeechWorks from Nuance Communications. Yap Speech Cloud – Speech-to-text platform acquired by Amazon.com.

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  • Gerrit (software)

    Gerrit (software)

    Gerrit ( GERR-it) is a free, web-based team code collaboration tool. Software developers in a team can review each other's modifications on their source code using a Web browser and approve or reject those changes. It integrates closely with Git, a distributed version control system. Gerrit is a fork of Rietveld, a code review tool for Subversion. Both are named after Dutch designer Gerrit Rietveld. == History == Originally written in Python like Rietveld, it is now written in Java (Java EE Servlet) with SQL since version 2 and a custom-made Git-based database (NoteDb) since version 3. In versions 2.0–2.16 Gerrit used Google Web Toolkit for its browser-based front-end. After being developed and used in parallel with GWT for versions 2.14–2.16, a new Polymer web UI replaced the GWT UI in version 3.0.

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  • LemonStand

    LemonStand

    LemonStand was a Canadian e-commerce company headquartered in Vancouver, British Columbia, that developed cloud-based computer software for online retailers. LemonStand was shut down on June 5, 2019. == History == LemonStand Version 1 was launched on July 28, 2001. It is written in the PHP programming language. Version 1 was released as an on-premises proprietary licensed software, and the commercial license was not free. However, there was a free trial license available. June 2012, LemonStand raised seed funding from the BDC Venture Capital, and a group of angel investors. December 20, 2013, a cloud-based SaaS version of the LemonStand eCommerce platform was released publicly. May 9, 2014, LemonStand and Payfirma, a payments processing company, partnered to provide integrated services for online retailers. May 3, 2016, LemonStand raised funding from BDC Venture Capital and Silicon Valley–based angel investors. March 5, 2019, LemonStand announced their intention to shut down on June 5, 2019. LemonStand was quietly acquired by Mailchimp at the end of February. == Pricing == LemonStand offered three levels of service plans. LemonStand did not charge any transaction fees.

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  • Software requirements

    Software requirements

    Software requirements for a system are the description of what the system should do, the service or services that it provides and the constraints on its operation. The IEEE Standard Glossary of Software Engineering Terminology defines a requirement as: A condition or capability needed by a user to solve a problem or achieve an objective A condition or capability that must be met or possessed by a system or system component to satisfy a contract, standard, specification, or other formally imposed document A documented representation of a condition or capability as in 1 or 2 The activities related to working with software requirements can broadly be broken down into elicitation, analysis, specification, and management. Note that the wording Software requirements is additionally used in software release notes to explain, which depending on software packages are required for a certain software to be built/installed/used. == Elicitation == Elicitation is the gathering and discovery of requirements from stakeholders and other sources. A variety of techniques can be used such as joint application design (JAD) sessions, interviews, document analysis, focus groups, etc. Elicitation is the first step of requirements development. == Analysis == Analysis is the logical breakdown that proceeds from elicitation. Analysis involves reaching a richer and more precise understanding of each requirement and representing sets of requirements in multiple, complementary ways. Requirements Triage or prioritization of requirements is another activity which often follows analysis. This relates to Agile software development in the planning phase, e.g. by Planning poker, however it might not be the same depending on the context and nature of the project and requirements or product/service that is being built. == Specification == Specification involves representing and storing the collected requirements knowledge in a persistent and well-organized fashion that facilitates effective communication and change management. Use cases, user stories, functional requirements, and visual analysis models are popular choices for requirements specification. == Validation == Validation involves techniques to confirm that the correct set of requirements has been specified to build a solution that satisfies the project's business objectives, and to detect and correct errors in the requirements before implementation. == Management == Requirements change during projects and there are often many of them. Management of this change becomes paramount to ensuring that the correct software is built for the stakeholders. == Tool support for Requirements Engineering == === Tools for Requirements Elicitation, Analysis and Validation === Taking into account that these activities may involve some artifacts such as observation reports (user observation), questionnaires (interviews, surveys and polls), use cases, user stories; activities such as requirement workshops (charrettes), brainstorming, mind mapping, role-playing; and even, prototyping; software products providing some or all of these capabilities can be used to help achieve these tasks. There is at least one author who advocates, explicitly, for mind mapping tools such as FreeMind; and, alternatively, for the use of specification by example tools such as Concordion. Additionally, the ideas and statements resulting from these activities may be gathered and organized with wikis and other collaboration tools such as Trello. The features actually implemented and standards compliance vary from product to product. === Tools for Requirements Specification === A Software requirements specification (SRS) document might be created using general-purpose software like a word processor or one of several specialized tools. Some of these tools can import, edit, export and publish SRS documents. It may help to make SRS documents while following a standardised structure and methodology, such as ISO/IEC/IEEE 29148:2018. Likewise, software may or not use some standard to import or export requirements (such as ReqIF) or not allow these exchanges at all. === Tools for Requirements Document Verification === Tools of this kind verify if there are any errors in a requirements document according to some expected structure or standard. === Tools for Requirements Comparison === Tools of this kind compare two requirement sets according to some expected document structure and standard. === Tools for Requirements Merge and Update === Tools of this kind allow the merging and update of requirement documents. === Tools for Requirements Traceability === Tools of this kind allow tracing requirements to other artifacts such as models and source code (forward traceability) or, to previous ones such as business rules and constraints (backwards traceability). === Tools for Model-Based Software or Systems Requirement Engineering === Model-based systems engineering (MBSE) is the formalised application of modelling to support system requirements, design, analysis, verification and validation activities beginning in the conceptual design phase and continuing throughout development and later lifecycle phases. It is also possible to take a model-based approach for some stages of the requirements engineering and, a more traditional one, for others. Very many combinations might be possible. The level of formality and complexity depends on the underlying methodology involved (for instance, i is much more formal than SysML and, even more formal than UML) === Tools for general Requirements Engineering === Tools in this category may provide some mix of the capabilities mentioned previously and others such as requirement configuration management and collaboration. The features actually implemented and standards compliance vary from product to product. There are even more capable or general tools that support other stages and activities. They are classified as ALM tools.

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  • Georges Giralt PhD Award

    Georges Giralt PhD Award

    The Georges Giralt PhD Award is a European scientific prize for extraordinary contributions to robotics. It is awarded yearly at the European Robotics Forum by euRobotics AISBL, a non-profit organisation based in Brussels with the objective of turning robotics beneficial for Europe’s economy and society. Georges Giralt received his PhD in 1958, from Paul Sabatier University, in the domain of electrical machines, and soon afterwards became a pioneer in robotics, in Europe and worldwide. He was especially instrumental in bringing in scientific foundations and methodology when the domain was still young, and a loose coupling of mechanical and electrical engineering, adopting the early results of automatic control. The high reputation of the Georges Giralt PhD Award is based on the prominent role of the awarding institution euRobotics. With more than 250 member organisations, euRobotics represents the academic and industrial robotics community in Europe. Moreover, it provides the European robotics community with a legal entity to engage in a public/private partnership with the European Commission. The award is covered by various media. Entitled for participation in the Georges Giralt PhD Award are all robotics-related dissertations which have been successfully defended at a European university. The US-American counterpart is the Dick Volz Award. == Award winners == 2026: Antonio González Morgado 2025: Erfan Shahriari 2024: Manuel Keppler 2023: Antonio Andriella, Ribin Balachandran 2022: Antonio Loquercio, Michael Lutter 2021: Giuseppe Averta, Bernd Henze 2020: Cosimo Della Santina 2019: Grazioso Stanislao, Teodor Tomic 2018: Frank Bonnet, Daniel Leidner 2017: Johannes Englsberger 2016: Alexander Dietrich, Mark Müller 2015: Jörg Stückler 2014: Manuel Catalano, Fabien Expert, Rainer Jaekel 2013: Jens Kober 2012: Sami Haddadin 2011: Mario Pratts 2010: Ludovic Righetti 2009: Alejandro-Dizan Vasquez-Govea 2008: Cyrill Stachniss, Eduardo Rocon 2007: Pierre Lamon 2006: Martijn Wisse 2005: Juan Andrade Cetto 2004: Gilles Duchemin 2003: Ralf Koeppe 2002: Gianluca Antonelli, Jens-Steffen Gutmann

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  • SAP Cloud Infrastructure

    SAP Cloud Infrastructure

    SAP Cloud Infrastructure is an SAP-operated IaaS cloud platform, used to run SAP’s cloud business and customer-facing deployments for SAP and non-SAP workloads. It is developed and operated with open-source technologies within SAP’s data center network, based on OpenStack and Kubernetes and supporting SAP S/4HANA and general-purpose applications. It offers compute, storage, and platform services that are accessible to SAP customers. == History == In 2012, SAP promoted aspects of cloud computing. In October 2012, SAP announced a platform as a service called the SAP Cloud Platform. In May 2013, a managed private cloud called the S/4HANA Enterprise Cloud service was announced. SAP Converged Cloud was announced in January 2015. SAP Converged Cloud was originally developed as SAP's internal standardized Infrastructure as a Service (IaaS) offering to support SAP’s cloud solutions. Originating from SAP Converged Cloud, SAP Cloud Infrastructure was developed and announced as SAP’s cloud computing offering that is provided for both SAP and customer workloads. In 2025, it had a global footprint of 15 regions and 29 data centers, encompassing more than 200,000 active VMs and over 6,000 hypervisors. In September 2025, SAP announced an expansion of its European “SAP Sovereign Cloud” portfolio, explicitly naming SAP Cloud Infrastructure (alongside SAP Sovereign Cloud On-Site) as part of the stack positioned for public sector and regulated environments. == Services and Features == SAP Cloud Infrastructure (SCI) is an infrastructure-as-a-service (IaaS) offering by SAP that provides virtual compute, storage, and networking services, together with identity, key management, and operational services. SCI follows a self-service model and is managed via APIs and a web-based user interface. === Compute === SCI provides virtual machine instances that can be provisioned from operating system images and selected in predefined sizes (“flavors”). It supports lifecycle operations such as create/modify/resize/delete, power control, and snapshots; instances can be organized into server groups to influence placement policies. === Storage === SCI provides persistent storage services including: Block storage (virtual volumes) with attach/detach to instances, online expansion, cloning, snapshots, and provisioning volumes from images or snapshots. Object storage (containers and objects) managed via API/CLI with access control lists (ACLs) and configurable redundancy options. File storage (shared file systems) with access controls, online resize, snapshots/restore, and replication across availability zones. === Networking === SCI provides software-defined networking (SDN) for tenant networks (networks, subnets, routers) and connectivity features such as floating IPs for public reachability. Network security controls include security groups and firewall policies; connectivity options include BGP-based VPN networking. === Load balancing and DNS === SCI includes managed load balancing for distributing traffic across backend instances and an authoritative DNS service (DNSaaS) with API-based management of DNS zones and records, including options for zone sharing/transfer across projects/tenants and service integrations for automated record creation. === Identity, access, and key management === SCI includes identity and access management for authentication/authorization in projects/tenants (for example token handling, role assignment, and credential management) and key/secrets management for storing and controlling access to secret material such as keys and certificates, including support for different backends (depending on configuration). === Cloud-native services === SCI includes a container image registry (image push/pull, access policies, and lifecycle controls) and an auto-scaling capability for file shares based on configurable rules. === Observability and audit === SCI includes metrics and audit logging capabilities for operational monitoring and for listing/filtering audit-relevant events across services. === Availability and service levels === SCI documentation describes availability-related features such as load balancing, storage redundancy options, and replication for file shares across availability zones. SAP cloud services are governed by contractual service-level agreements (SLA); SAP Cloud Infrastructure references an SLA supplement defining infrastructure-specific terms when referenced in order forms. === SAP cloud services === SAP cloud services can run on different underlying infrastructures, including SAP Cloud Infrastructure in addition to SAP NS2 or hyperscalers. SAP cloud solutions available on SAP Cloud Infrastructure include SAP Cloud ERP, SAP HCM, SAP Solutions for Spend Management, Supply Chain Management, Business Transformation Management, and SAP Business Technology Platform (including related analytics and business data solutions). For example, SAP HANA Cloud documentation lists SAP Cloud Infrastructure as one of the supported infrastructures alongside hyperscalers. === Sustainability === SAP describes sustainability initiatives for its data centers, including energy-efficient infrastructure (for example, advanced cooling systems and power management), renewable electricity usage where feasible, and operational practices such as recycling electronic waste and minimizing water usage. SAP also references environmental management and energy management standards such as ISO 14001 and ISO 50001 for its data center operations. SAP-owned data centers run with 100% renewable electricity and that renewable electricity has been used since 2014 to power SAP facilities including owned data centers and co-locations. == SAP Cloud Infrastructure for SAP Sovereign Cloud == SAP Sovereign Cloud is a portfolio of SAP solutions designed to help organizations adopt SAP cloud solutions such as the SAP Cloud ERP while maintaining control over data, infrastructure, and compliance in line with local laws and regulations. The portfolio offers multiple deployment options, including SAP Cloud Infrastructure and SAP Sovereign Cloud On-Site, alongside sovereign hyperscaler-based options such as via SAP NS2, and targets customers such as public-sector bodies and other highly regulated organizations. In Europe, SAP Cloud Infrastructure is an Infrastructure-as-a-Service (IaaS) deployment option within SAP Sovereign Cloud for SAP and customer / third party workloads, operated on SAP’s data center network and developed using open-source technologies, with customer data stored within the European Union. Sovereignty-related characteristics for the SAP Cloud Infrastructure include: EU footprint and ownership model: SAP-operated data centers in Germany include sites in St. Leon-Rot and Walldorf, and co-location sites in Frankfurt. EU AI Cloud: EU AI Cloud is a sovereign AI offering for Europe that provides secure, compliant environments for building and running AI, including governed access to auditable large language models from SAP and partners. It offers AI models on the SAP Cloud Infrastructure and SAP Business Technology Platform (SAP BTP), enabling deployment of AI applications and models on high-performance European infrastructure (including accelerator/GPU-based compute for AI workloads). Availability zones and secure interconnect: Three availability zones in three independent data centers in Germany, connected via SAP-owned fiber on SAP-owned property. Facility and security standards: ISO/IEC 27001 governance of delivery and operations of SAP cloud services and SAP-owned data centers. Additional facility and availability standards: EN 50600 availability class 3 (European data centre standard) and/or ISO/IEC 22237 availability class 3 (international equivalent). Technology foundation: Based on open-source cloud infrastructure framework (OpenStack) and Kubernetes, without dependencies on hyperscaler technologies. Sovereignty controls: Data sovereignty (data residency), operational sovereignty (administration and maintenance restricted to approved, security-cleared personnel), technical sovereignty (locally hosted control planes with separation via encryption or dedicated infrastructure), and legal sovereignty (use of locally based legal entities or those in approved countries). Classified information processing: Roadmap to meet high and very high requirements for handling classified or sensitive information under European regulatory and security regimes. Public-sector readiness and EU sovereignty assurance levels: Implemented to meet SEAL-3 (Digital Resilience) and SEAL-4 (Full Digital Sovereignty) of the European Commission’s Cloud Sovereignty Framework. Staffing constraints: Operations model selectable to restrict sensitive operations to vetted personnel from EU or NATO countries.

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  • Yorba (software)

    Yorba (software)

    Yorba is a web-based personal information management platform for finding, monitoring, or deleting online accounts and subscriptions. Yorba is a participating member of Consumer Reports’ Data Rights Protocol (DRP) consortium that develops open technical standards for exercising consumer data rights under laws including the California Consumer Privacy Act. == History == Yorba began as a research project around 2021. It was founded by Chris Zeunstrom (CEO), Nolan Cabeje (CDO) and David Schmudde (CTO). Zeunstrom says he began developing Yorba after growing frustrated with managing numerous email accounts, noting overloaded inboxes create distraction and potential security vulnerabilities. Yorba’s early development was also influenced by security issues he encountered at a previous company, which had been affected by data breaches at a time when such incidents were becoming increasingly common. In 2023, Yorba launched a private beta as a public benefit corporation funded through a give-back model operated by Zeunstrom's New York-based design firm, Ruca. In January 2024, Yorba entered public beta and reported over 1,000 users, including 160 premium subscribers. At the time of the public beta launch, Yorba integrated with Gmail and announced plans to expand compatibility to other online services and cloud storage providers. In September 2024, Yorba completed conformance testing under the Data Rights Protocol, an initiative developed by Consumer Reports, to establish a standard and open-source framework for securely transmitting consumer data rights requests under laws like the California Consumer Privacy Act. Yorba was named among twelve participating companies that implemented the protocol alongside OneTrust and Consumer Reports’ own Permission Slip app. Yorba was one of nine startups selected as 2025 finalist in the Santander X Global Awards international entrepreneurship competition. == Features == Yorba scans user inbox history data to identify online accounts, mailing lists, and possible data breaches. It uses natural language processing and machine learning to identify a user's accounts, services, and subscriptions. The platform prompts password resets for compromised accounts and locates unused accounts. The platform also supports mailing list management by identifying and helping users unsubscribe from newsletters. Paid subscribers can locate and cancel recurring charges. Yorba links with financial institutions in the U.S., Canada, and EU via Plaid Inc. to detect recurring charges and delete unwanted subscriptions. == Privacy and Ethics == Yorba's founder has openly criticized dark patterns that make canceling services difficult, citing personal frustration with inbox clutter as part of his inspiration for Yorba. Yorba offers privacy policy analysis in partnership with Amsterdam-based nonprofit Terms of Service; Didn’t Read, assigning grades based on invasiveness or ethical concerns. As of 2024, the company described its pricing as designed to cover operational costs and sustain the platform without outside investment.

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  • Process map

    Process map

    Process map is a global-system process model that is used to outline the processes that make up the business system and how they interact with each other. Process map shows the processes as objects, which means it is a static and non-algorithmic view of the processes. It should be differentiated from a detailed process model, which shows a dynamic and algorithmic view of the processes, usually known as a process flow diagram. There are different notation standards that can be used for modelling process maps, but the most notable ones are TOGAF Event Diagram, Eriksson-Penker notation, and ARIS Value Added Chain. == Global process models == Global characteristics of the business system are captured by global or system models. Global process models are presented using different methodologies and sometimes under different names. Most notably, they are named process map in Visual Paradigm and MMABP, value-added chain in ARIS, and process diagram in Eriksson-Penker notation – which can easily lead to the confusion with process flow (detailed process model). Global models are mainly object-oriented and present a static view of the business system; they do not describe dynamic aspects of processes. A process map shows the presence of processes and their mutual relationships. The requirement for the global perspective of the system as a supplementary to the internal process logic description results from the necessity of taking into consideration not only the internal process logic but also its significant surroundings. The algorithmic process model cannot take the place of this perspective since it represents the system model of the process. The detailed process model and the global process model represent different perspectives on the same business system, so these models must be mutually consistent. A macro process map represents the major processes required to deliver a product or service to the customer. These macro process maps can be further detailed in sub-diagrams. It is often the case that process maps cross different functional areas of the organization. Process maps are used by many companies to have a holistic view of all processes and the connections between them. Maps help in navigating the sub-processes and make understanding of the organization's operations easier. The process map shows relationships and dependencies between processes and its focus should be on core business processes of the organization. A process map can be seen as the most abstract level of the process architecture, and it acts as the introduction to the more detailed levels. A process map that is correctly designed is able to provide a general understanding of a company's operations. Designing the process map is an important and strategic step for the organization, and it is followed by further business process modelling implementation. == Context == Methodology for Modelling and Analysis of Business Process (MMABP) is a business process modelling methodology developed at the Department of Information Technology, Faculty of Informatics and Statistics of the Prague University of Economics and Business. The methodology is defined as a “general methodology for modelling business systems using informatics methods and approaches”. Methodology is used to analyse business processes and to develop a comprehensive model of the system. The goal of developing a model is to be used for process optimization. The model should be created following the characteristics and specifics of the organization in question and following external influences that can affect the organization. The model should be optimal from an economic perspective, but it should also be optimal from a factual perspective, meaning that it should be as simple as possible while maintaining complete functionality. Business system modelling is based on a two-dimensional approach: Real World structure (substance) – set of objects and their relationships Real World behaviour – set of mutually connected business processes Additionally, there are also two views of the systems: Global view of the system Detailed view of the system's parts This results in the need to model the system from four different perspectives in order to achieve the complete and comprehensive view of the business system. MMABP also proposes which notation languages can be used for modelling each perspective, and it also suggests some improvements to the notation languages in order to fit the purpose. Global view of the objects – Conceptual model (Class diagram) Detailed view of the objects – Object life cycle (State Chart) Global view of the processes – Process map (Eriksson-Penker Diagram/TOGAF Event Diagram/ARIS VAC) Detailed view of the processes – Model of the process flow (BPMN Diagram) Data Flow Diagram (DFD) is additional diagram used for describing the required functionalities of the information system. == Notation standards == === Eriksson-Penker Diagram === Eriksson-Penker diagram is a tool used in business model analysis and design. It is named after Hans-Erik Eriksson and Magnus Penker, who developed the concept in their book "Business modelling with UML: Business Patterns at Work”. Eriksson-Penker diagrams are used to map out the key components of a business model and how they interact with one another. The diagrams typically consist of a series of boxes and lines that represent the different elements of the business model, such as the value proposition, customer segments, channels, revenue streams, and key resources. The lines between the boxes represent the relationships and dependencies between the different elements of the business model. These diagrams are useful for visualizing and understanding the various components of a business model, and can help organizations identify potential areas for improvement or areas of risk. They can also be used as a communication tool to help stakeholders understand the business model and its underlying assumptions. These diagrams are useful for visualizing and understanding the various components of a business model, and can help organizations identify potential areas for improvement or areas of risk. They can also be used as a communication tool to help stakeholders understand the business model and its underlying assumptions. It is possible to use Eriksson-Penker diagrams to create a global process view of a business. In this case, a diagram would be used to map out the key processes and activities that are involved in the business, as well as the relationships and dependencies between these processes. For example, an Eriksson-Penker diagram could be used to depict the various steps involved in the product development process, from concept development to market launch. It could also be used to show how different functions within the organization, such as marketing, sales, and production, interact and depend on one another to support the overall business. Eriksson-Penker diagram is one of the most popular de facto standards that can be used for an object-oriented global view of business processes. It is developed as an extension of the UML, and it is often used together with the BPMN to compensate for the lack of possibility to model the global view with this widely accepted standard. === TOGAF Event Diagram === TOGAF (The Open Group Architecture Framework) is a framework for enterprise architecture that provides a common language and set of standards for designing, planning, implementing, and governing an enterprise's IT architecture. TOGAF event diagrams are diagrams used in the TOGAF framework to represent the flow of events within a system or process. The TOGAF Event Diagram is a visual representation of the events within an organization or system. It can be used to show the sequence of events that occur in a particular process, as well as the relationships between the events and the stakeholders involved. TOGAF Event Diagrams can be useful in creating a global process view because they provide a visual representation of the events, which can be helpful in understanding how the process fits into the larger context of the organization. TOGAF Event Diagram is the most perspective standard for the system view of processes today. It is used to represent the system of processes as well as their connections to the functional organizational structure. === ARIS Value Added Chain === ARIS (Architecture of Integrated Information Systems) is a methodology and a set of tools for designing and managing business processes. It is based on the idea that business processes are the core of an organization and that they can be modelled and optimized to improve efficiency and effectiveness. The ARIS methodology provides a framework for understanding and analysing business processes, as well as for designing and implementing improvements to those processes. It includes a set of graphical modelling languages and tools for creating process models, as well as a database for storing and managing pr

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  • Niki.ai

    Niki.ai

    Niki was an artificial intelligence company headquartered in Bangalore, Karnataka. It was founded in May 2015 by IIT Kharagpur graduates Sachin Jaiswal, Keshav Prawasi, Shishir Modi, and Nitin Babel. The Niki android app was launched for a limited beta in June 2015, then released for public during YourStory's TechSparks 2015, and is a Tech30 company. The company raised an undisclosed amount in seed funding from Unilazer Ventures, a Mumbai-based VC firm founded by Ronnie Screwvala, in October 2015. This was followed by another seed funding round by Ratan Tata in May 2016. The company then raised US$2 million in Series A round of funding from SAP.iO, existing investors and some US and German-based investors, among others. Niki.ai shut down in October 2021 as per media reports. Website not working. == Product == The product is an artificial intelligence-powered chatbot which works as an intelligent personal assistant, named Niki. Leveraging natural language processing and machine learning, Niki presents a chat-based natural language user interface to the users where they can interact with Niki in their natural language. Niki understands how users chat in India, deciphers the words, in the context of product/services that they would like to purchase, and comes up with apt recommendations. Initially, it was only available on the Android platform as a mobile app. The company has expanded its operations to the Facebook Messenger and Apple iOS platforms. The company aims to soon be present on more messaging platforms like Slack and WhatsApp. The company currently provides 20+ services to over 2 million consumers, covering a wide spectrum ranging from utility services like mobile recharge, bill payments, travel services like cabs, buses, hotels and entertainment services like movies and events. Services such as flights and healthcare are also planned. == Partnerships == In September 2017, Infosys Finacle joined with Niki.ai to provide chat-based service to banking customers. In August 2017, Niki partnered with LazyPay to enable a 'buy now, pay later' feature for its users.

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  • Content as a service

    Content as a service

    Content as a service (CaaS) or managed content as a service (MCaaS) is a service-oriented model, where the service provider delivers the content on demand to the service consumer via web services that are licensed under subscription. The content is hosted by the service provider centrally in the cloud and offered to a number of consumers that need the content delivered into any applications or system, hence content can be demanded by the consumers as and when required. Content as a Service is a way to provide raw content (in other words, without the need for a specific human compatible representation, such as HTML) in a way that other systems can make use of it. Content as a Service is not meant for direct human consumption, but rather for other platforms to consume and make use of the content according to their particular needs. This happens usually on the cloud, with a centralized platform which can be globally accessible and provides a standard format for your content. With Content as a Service, you centralize your content into a single repository, where you can manage it, categorize it, make it available to others, search for it, or do whatever you wish with it. == Overview == The content delivered typically could be one or more of the following The technical terminology related to equipment or spares that is required to procure or design the materials The industrial terminology of the equipment or spares Technical values pertaining to various types, specifications, applications, characteristics of equipment or spares Sourcing information which will help in procurement or supply-chain management of equipment or spares Descriptive specifications of equipment or spares based on the product reference number or identifier UNSPSC codes or industry practiced classifications ISO, IEC compliant terminology Ontology or Technical Dictionary of products & services Predefined content for specific business needs The term "Content as a service" (CaaS) is considered to be part of the nomenclature of cloud computing service models & Service-oriented architecture along with Software as a service (SaaS), Infrastructure as a service (IaaS), and Platform as a service (PaaS).

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  • Geo-replication

    Geo-replication

    Geo-replication systems are designed to provide improved availability and disaster tolerance by using geographically distributed data centers. This is intended to improve the response time for applications such as web portals. Geo-replication can be achieved using software, hardware or a combination of the two. == Software == Geo-replication software is a network performance-enhancing technology that is designed to provide improved access to portal or intranet content for users at the most remote parts of large organizations. It is based on the principle of storing complete replicas of portal content on local servers, and then keeping the content on those servers up-to-date using heavily compressed data updates. === Portal acceleration === Geo-replication technologies are used to provide replication of the content of portals, intranets, web applications, content and data between servers, across wide area networks WAN to allow users at remote sites to access central content at LAN speeds. Geo-replication software can improve the performance of data networks that suffer limited bandwidth, latency and periodic disconnection. Terabytes of data can be replicated over a wide area network, giving remote sites faster access to web applications. Geo-replication software uses a combination of data compression and content caching technologies. differencing technologies can also be employed to reduce the volume of data that has to be transmitted to keep portal content accurate across all servers. This update compression can reduce the load that portal traffic places on networks, and improve the response time of a portal. === Portal replication === Remote users of web portals and collaboration environments will frequently experience network bandwidth and latency problems which will slow down their experience of opening and closing files, and otherwise interacting with the portal. Geo-replication technology is deployed to accelerate the remote end user portal performance to be equivalent to that experienced by users locally accessing the portal in the central office. === Differencing engine technologies === To deliver this reduction in the size of the required data updates across a portal, geo-replication systems often use differencing engine technologies. These systems are able to difference the content of each portal server right down to the byte level. This knowledge of the content that is already on each server enables the system to rebuild any changes to the content on one server, across each of the other servers in the deployment from content already hosted on those other servers. This type of differencing system ensures that no content, at the byte level, is ever sent to a server twice. === Offline portal replication on laptops === Geo-replication systems are often extended to deliver local replication beyond the server and down to the laptop used by a single user. Server to laptop replication enables mobile users to have access to a local replica of their business portal on a standard laptop. This technology may be employed to provide in the field access to portal content by, for example, sales forces and combat forces. == Geo-replication systems ==

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