Integreat (former project name: Refguide+) is an open source mobile app that provides local information and services tailored to refugees and migrants coming to Germany. The content is maintained by local organizations, such as local governments or integration officers, and made available in locally relevant languages. It was developed by Tür an Tür - Digitalfabrik gGmbH (formerly Tür an Tür - Digital Factory gGmbH) in Augsburg together with a team of researchers and students from the Technical University of Munich. == History == In 1997, the Augsburg association "Tür an Tür", which has been working for refugees since 1992, published the brochure "First Steps", which answers local everyday questions. Since addresses and contact persons change quickly, some information is already outdated after a few weeks. Students of business informatics at the Technical University of Munich therefore developed the app Integreat within eight months together with the association and the social department of the city of Augsburg. The app was then also used by other cities and districts within months. As of February 3, 2022, information is available at 72 locations, including Munich, Dortmund, Nuremberg and Augsburg. == Mode of action == Refugees need information on areas such as registration, contact persons, health care, education, family, work and everyday life. Integreat seeks to provide refugees with this information by allowing them to select their geographic location and receive locally relevant information. This information is available offline once the app is opened so it can be used without an internet connection. In addition, the content is translated into the native languages of refugees and migrants to facilitate access. The content is licensed with a CC BY 4.0 license to facilitate collaboration and translation between content creators and dissemination of the content. Integreat is now being used for a broader migrant audience and says it can also support professionals, volunteers, and counseling centers. == Comparable mobile apps == Other mobile apps that are likewise intended to provide initial orientation for refugees include the app Ankommen, a joint project of the Federal Office for Migration and Refugees, the Goethe-Institut, the Federal Employment Agency and the Bavarian Broadcasting Corporation, which is intended as a companion for the first few weeks in Germany, and the Welcome App, a company-sponsored non-profit initiative for information about Germany and asylum procedures with a regional focus, and a book by the Konrad Adenauer Foundation (KAS) and Verlag Herder with a corresponding app Deutschland - Erste Informationen für Flüchtlinge (Germany - First Information for Refugees) as a companion for Arabic-speaking refugees in Germany.
Harris corner detector
The Harris corner detector is a corner detection operator that is commonly used in computer vision algorithms to extract corners and infer features of an image. It was first introduced by Chris Harris and Mike Stephens in 1988 upon the improvement of Moravec's corner detector. Compared to its predecessor, Harris' corner detector takes the differential of the corner score into account with reference to direction directly, instead of using shifting patches for every 45 degree angles, and has been proved to be more accurate in distinguishing between edges and corners. Since then, it has been improved and adopted in many algorithms to preprocess images for subsequent applications. == Introduction == A corner is a point whose local neighborhood stands in two dominant and different edge directions. In other words, a corner can be interpreted as the junction of two edges, where an edge is a sudden change in image brightness. Corners are the important features in the image, and they are generally termed as interest points which are invariant to translation, rotation and illumination. Although corners are only a small percentage of the image, they contain the most important features in restoring image information, and they can be used to minimize the amount of processed data for motion tracking, image stitching, building 2D mosaics, stereo vision, image representation and other related computer vision areas. In order to capture the corners from the image, researchers have proposed many different corner detectors including the Kanade-Lucas-Tomasi (KLT) operator and the Harris operator which are most simple, efficient and reliable for use in corner detection. These two popular methodologies are both closely associated with and based on the local structure matrix. Compared to the Kanade-Lucas-Tomasi corner detector, the Harris corner detector provides good repeatability under changing illumination and rotation, and therefore, it is more often used in stereo matching and image database retrieval. Although there still exist drawbacks and limitations, the Harris corner detector is still an important and fundamental technique for many computer vision applications. == Development of Harris corner detection algorithm == Source: Without loss of generality, we will assume a grayscale 2-dimensional image is used. Let this image be given by I {\displaystyle I} . Consider taking an image patch ( x , y ) ∈ W {\displaystyle (x,y)\in W} (window) and shifting it by ( Δ x , Δ y ) {\displaystyle (\Delta x,\Delta y)} . The sum of squared differences (SSD) between these two patches, denoted f {\displaystyle f} , is given by: f ( Δ x , Δ y ) = ∑ ( x k , y k ) ∈ W ( I ( x k , y k ) − I ( x k + Δ x , y k + Δ y ) ) 2 {\displaystyle f(\Delta x,\Delta y)={\underset {(x_{k},y_{k})\in W}{\sum }}\left(I(x_{k},y_{k})-I(x_{k}+\Delta x,y_{k}+\Delta y)\right)^{2}} I ( x + Δ x , y + Δ y ) {\displaystyle I(x+\Delta x,y+\Delta y)} can be approximated by a Taylor expansion. Let I x {\displaystyle I_{x}} and I y {\displaystyle I_{y}} be the partial derivatives of I {\displaystyle I} , such that I ( x + Δ x , y + Δ y ) ≈ I ( x , y ) + I x ( x , y ) Δ x + I y ( x , y ) Δ y {\displaystyle I(x+\Delta x,y+\Delta y)\approx I(x,y)+I_{x}(x,y)\Delta x+I_{y}(x,y)\Delta y} This produces the approximation f ( Δ x , Δ y ) ≈ ∑ ( x , y ) ∈ W ( I x ( x , y ) Δ x + I y ( x , y ) Δ y ) 2 , {\displaystyle f(\Delta x,\Delta y)\approx {\underset {(x,y)\in W}{\sum }}\left(I_{x}(x,y)\Delta x+I_{y}(x,y)\Delta y\right)^{2},} which can be written in matrix form: f ( Δ x , Δ y ) ≈ ( Δ x Δ y ) M ( Δ x Δ y ) , {\displaystyle f(\Delta x,\Delta y)\approx {\begin{pmatrix}\Delta x&\Delta y\end{pmatrix}}M{\begin{pmatrix}\Delta x\\\Delta y\end{pmatrix}},} where M is the structure tensor, M = ∑ ( x , y ) ∈ W [ I x 2 I x I y I x I y I y 2 ] = [ ∑ ( x , y ) ∈ W I x 2 ∑ ( x , y ) ∈ W I x I y ∑ ( x , y ) ∈ W I x I y ∑ ( x , y ) ∈ W I y 2 ] {\displaystyle M={\underset {(x,y)\in W}{\sum }}{\begin{bmatrix}I_{x}^{2}&I_{x}I_{y}\\I_{x}I_{y}&I_{y}^{2}\end{bmatrix}}={\begin{bmatrix}{\underset {(x,y)\in W}{\sum }}I_{x}^{2}&{\underset {(x,y)\in W}{\sum }}I_{x}I_{y}\\{\underset {(x,y)\in W}{\sum }}I_{x}I_{y}&{\underset {(x,y)\in W}{\sum }}I_{y}^{2}\end{bmatrix}}} == Process of Harris corner detection algorithm == Commonly, Harris corner detector algorithm can be divided into five steps. Color to grayscale Spatial derivative calculation Structure tensor setup Harris response calculation Non-maximum suppression === Color to grayscale === If we use Harris corner detector in a color image, the first step is to convert it into a grayscale image, which will enhance the processing speed. The value of the gray scale pixel can be computed as a weighted sums of the values R, B and G of the color image, ∑ C ∈ { R , G , B } w C ⋅ C {\displaystyle \sum _{C\,\in \,\{R,G,B\}}w_{C}\cdot C} , where, e.g., w R = 0.299 , w G = 0.587 , w B = 1 − ( w R + w G ) = 0.114. {\displaystyle w_{R}=0.299,\ w_{G}=0.587,\ w_{B}=1-(w_{R}+w_{G})=0.114.} === Spatial derivative calculation === Next, we are going to find the derivative with respect to x and the derivative with respect to y, I x ( x , y ) {\displaystyle I_{x}(x,y)} and I y ( x , y ) {\displaystyle I_{y}(x,y)} . This can be approximated by applying Sobel operators. === Structure tensor setup === With I x ( x , y ) {\displaystyle I_{x}(x,y)} , I y ( x , y ) {\displaystyle I_{y}(x,y)} , we can construct the structure tensor M {\displaystyle M} . === Harris response calculation === For x ≪ y {\displaystyle x\ll y} , one has x ⋅ y x + y = x 1 1 + x / y ≈ x . {\displaystyle {\tfrac {x\cdot y}{x+y}}=x{\tfrac {1}{1+x/y}}\approx x.} In this step, we compute the smallest eigenvalue of the structure tensor using that approximation: λ min ≈ λ 1 λ 2 ( λ 1 + λ 2 ) = det ( M ) tr ( M ) {\displaystyle \lambda _{\min }\approx {\frac {\lambda _{1}\lambda _{2}}{(\lambda _{1}+\lambda _{2})}}={\frac {\det(M)}{\operatorname {tr} (M)}}} with the trace t r ( M ) = m 11 + m 22 {\displaystyle \mathrm {tr} (M)=m_{11}+m_{22}} . Another commonly used Harris response calculation is shown as below, R = λ 1 λ 2 − k ( λ 1 + λ 2 ) 2 = det ( M ) − k tr ( M ) 2 {\displaystyle R=\lambda _{1}\lambda _{2}-k(\lambda _{1}+\lambda _{2})^{2}=\det(M)-k\operatorname {tr} (M)^{2}} where k {\displaystyle k} is an empirically determined constant; k ∈ [ 0.04 , 0.06 ] {\displaystyle k\in [0.04,0.06]} . === Non-maximum suppression === In order to pick up the optimal values to indicate corners, we find the local maxima as corners within the window which is a 3 by 3 filter. == Improvement == Sources: Harris-Laplace Corner Detector Differential Morphological Decomposition Based Corner Detector Multi-scale Bilateral Structure Tensor Based Corner Detector == Applications == Image Alignment, Stitching and Registration 2D Mosaics Creation 3D Scene Modeling and Reconstruction Motion Detection Object Recognition Image Indexing and Content-based Retrieval Video Tracking
Web container
A web container (also known as a servlet container; and compare "webcontainer") is the component of a web server that interacts with Jakarta Servlets. A web container is responsible for managing the lifecycle of servlets, mapping a URL to a particular servlet and ensuring that the URL requester has the correct access-rights. A web container handles requests to servlets, Jakarta Server Pages (JSP) files, and other types of files that include server-side code. The Web container creates servlet instances, loads and unloads servlets, creates and manages request and response objects, and performs other servlet-management tasks. A web container implements the web component contract of the Jakarta EE architecture. This architecture specifies a runtime environment for additional web components, including security, concurrency, lifecycle management, transaction, deployment, and other services. == List of Servlet containers == The following is a list of notable applications which implement the Jakarta Servlet specification from Eclipse Foundation, divided depending on whether they are directly sold or not. === Open source Web containers === Apache Tomcat (formerly Jakarta Tomcat) is an open source web container available under the Apache Software License. Apache Tomcat 6 and above are operable as general application container (prior versions were web containers only) Apache Geronimo is a full Java EE 6 implementation by Apache Software Foundation. Enhydra, from Lutris Technologies. GlassFish from Eclipse Foundation (an application server, but includes a web container). Jetty, from the Eclipse Foundation. Also supports SPDY and WebSocket protocols. Open Liberty, from IBM, is a fully compliant Jakarta EE server Virgo from Eclipse Foundation provides modular, OSGi based web containers implemented using embedded Tomcat and Jetty. Virgo is available under the Eclipse Public License. WildFly (formerly JBoss Application Server) is a full Java EE implementation by Red Hat, division JBoss. === Commercial Web containers === iPlanet Web Server, from Oracle. JBoss Enterprise Application Platform from Red Hat, division JBoss is subscription-based/open-source Jakarta EE-based application server. WebLogic Application Server, from Oracle Corporation (formerly developed by BEA Systems). Orion Application Server, from IronFlare. Resin Pro, from Caucho Technology. IBM WebSphere Application Server. SAP NetWeaver.
Too Good To Go
Too Good To Go is a service with a mobile application that connects customers to restaurants and stores that have surplus unsold food. The service covers major European cities, and in October 2020 started operations in North America. As part of the initiatives taken on the International Day of Awareness of Food Loss and Waste to reduce food loss and waste, the app is suggested alongside OLIO among many others. In 2023 Too Good To Go was the fastest-growing sustainable food app startup by number of downloads. As of August 2023, it claimed 164,000 businesses, serving 62 million users, have saved 155 million bags of food. As of March 2023, it claimed to have saved over 200 million meals. == History == The company was created in 2015 in Denmark by Thomas Bjørn Momsen, Klaus Bagge Pedersen, Adam Sigbrand and Brian Christensen. In 2017, Mette Lykke (co-founder of Endomondo) joined as CEO. In February 2019, the company raised an additional 6 million euros in a new round of investment. In August 2019, Too Good To Go was re-launched in Austria. In September 2019, Too Good To Go acquired the Spanish startup weSAVEeat and merged it into its own brand. In November 2019, the offer of Too Good To Go extended to plants through a partnership with the French retail plants company Jardiland. In December 2019, Too Good To Go partnered with the French grocery retail stores Intermarché, and donated 60K euros to the French charity Restaurants du Cœur. In October 2021, Bonnie Wright teamed up with Too Good To Go to drive the initiative to reduce food waste. == Corporate affairs == The key trends for the Danish entity Too Good To Go ApS are (as of the financial year ending December 31): == International expansion == As of March 2026 the company serves the European countries Austria, Belgium, Czechia, Denmark, the Faroe Islands, France, Germany, Ireland, Italy, the Netherlands, Norway, Poland, Portugal, Spain, Sweden, Switzerland, the United Kingdom. Outside of Europe the service is available in Australia, Canada, Japan, New Zealand and the United States. == Purpose == The purpose of Too Good To Go is to reduce food waste worldwide. It developed a mobile application that connects restaurants and stores that have unsold, surplus food, with customers who can then buy whatever food the outlet considers surplus to requirements—without being able to choose—at a much lower price than normal. The food on the app is priced at one-third its original price. The company claims this reduces the waste of food that would otherwise be discarded; food waste is a global problem that affects the environment. In three years active, the app reached more than 9.5 million users. As of 2022, more than 57.7 million users and 154,000 establishments have signed up, and 139 million meals have been collected. In 2019, the company had 350 employees in Europe. As of June 2023 the company was estimated to have 1,289 employees. == Use == Food outlets must notify the TGTG company about what they have available on each day, stating what sort of food they have (baked foods, meals, produce, vegan food), and the price for a 'surprise bag', whose contents they determine; the user cannot choose, but the original prices will be three or more times the TGTG price. Notification is made early based upon the quantity predicted to be left over, not at the end of a selling period. Users must register to use the service. A mobile phone with an Internet connection running Android or iOS is needed. The user runs the TGTG app, which lists outlets available within a chosen distance and time range. The customer can then order and pay for a 'surprise bag'. The supplier can cancel an order at any time if the expected surplus is not available—the purchaser is notified by text message—and the purchaser can cancel with two hours' notice. The phone must be taken to the food supplier in a specified pickup time window, often 30 or 60 minutes long, and the transaction is finalised by swiping the app—connected to the Internet—to confirm collection.
Distributed manufacturing
Distributed manufacturing, also known as distributed production, cloud producing, distributed digital manufacturing, and local manufacturing, is a form of decentralized manufacturing practiced by enterprises using a network of geographically dispersed manufacturing facilities that are coordinated using information technology. It can also refer to local manufacture via the historic cottage industry model, or manufacturing that takes place in the homes of consumers. == Enterprise == In enterprise environments, the primary attribute of distributed manufacturing is the ability to create value at geographically dispersed locations. For example, shipping costs could be minimized when products are built geographically close to their intended markets. Also, products manufactured in a number of small facilities distributed over a wide area can be customized with details adapted to individual or regional tastes. Manufacturing components in different physical locations and then managing the supply chain to bring them together for final assembly of a product is also considered a form of distributed manufacturing. Digital networks combined with additive manufacturing allow companies a decentralized and geographically independent distributed production (cloud manufacturing). == Consumer == Within the maker movement and DIY culture, small scale production by consumers often using peer-to-peer resources is being referred to as distributed manufacturing. Consumers download digital designs from an open design repository website like Youmagine or Thingiverse and produce a product for low costs through a distributed network of 3D printing services such as 3D Hubs, Geomiq. In the most distributed form of distributed manufacturing the consumer becomes a prosumer and manufacturers products at home with an open-source 3-D printer such as the RepRap. In 2013 a desktop 3-D printer could be economically justified as a personal product fabricator and the number of free and open hardware designs were growing exponentially. Today there are millions of open hardware product designs at hundreds of repositories and there is some evidence consumers are 3-D printing to save money. For example, 2017 case studies probed the quality of: (1) six common complex toys; (2) Lego blocks; and (3) the customizability of open source board games and found that all filaments analyzed saved the prosumer over 75% of the cost of commercially available true alternative toys and over 90% for recyclebot filament. Overall, these results indicate a single 3D printing repository, MyMiniFactory, is saving consumers well over $60 million/year in offset purchases of only toys. These 3-D printers can now be used to make sophisticated high-value products like scientific instruments. Similarly, a study in 2022 found that 81% of open source designs provided economic savings and the total savings for the 3D printing community is more than $35 million from downloading only the top 100 products at YouMagine. In general, the savings are largest when compared to conventional products when prosumers use recycled materials in 'distributed recycling and additive manufacturing' (DRAM). == Emergency Distributed Manufacturing During COVID-19 Pandemic == Distributed manufacturing became far more visible during the COVID-19 pandemic because it offered a practical response to the breakdown of centralized global supply chains. As lock downs, border restrictions, and factory shutdowns disrupted conventional production, decentralized networks using local facilities such as Open Source Medical Supplies stepped in and manufactured over 48 million products. Additive manufacturing /3D printing were used to produce urgently needed items such as face shields, ventilators and their components, nasopharyngeal swabs, and other personal protective equipment. This demonstrated that distributed manufacturing could reduce lead times, improve responsiveness, and lessen dependence on distant suppliers during crisis conditions for a wide range of products. Peer-reviewed studies on pandemic-era manufacturing note that additive manufacturing was especially valuable because digital design files could be shared rapidly and produced close to the point of need, enabling hospitals, universities, small firms, and maker communities to supplement strained medical supply chains. The pandemic also helped shift distributed manufacturing from being seen as a niche or experimental model to a credible strategy for resilience, flexibility, and emergency response. At the same time, scholars caution that its wider adoption depends on solving issues related to quality assurance, regulation, material consistency, and coordination across distributed production sites. Overall, COVID-19 popularized distributed manufacturing by showing that localized, digitally enabled production could complement traditional manufacturing systems when speed, adaptability, and supply-chain resilience were critical. == Social change == Some call attention to the conjunction of commons-based peer production with distributed manufacturing techniques. The self-reinforced fantasy of a system of eternal growth can be overcome with the development of economies of scope, and here, the civil society can play an important role contributing to the raising of the whole productive structure to a higher plateau of more sustainable and customised productivity. Further, it is true that many issues, problems and threats rise due to the large democratization of the means of production, and especially regarding the physical ones. For instance, the recyclability of advanced nanomaterials is still questioned; weapons manufacturing could become easier; not to mention the implications on counterfeiting and on "intellectual property". It might be maintained that in contrast to the industrial paradigm whose competitive dynamics were about economies of scale, commons-based peer production and distributed manufacturing could develop economies of scope. While the advantages of scale rest on cheap global transportation, the economies of scope share infrastructure costs (intangible and tangible productive resources), taking advantage of the capabilities of the fabrication tools. And following Neil Gershenfeld in that "some of the least developed parts of the world need some of the most advanced technologies", commons-based peer production and distributed manufacturing may offer the necessary tools for thinking globally but act locally in response to certain problems and needs. As well as supporting individual personal manufacturing social and economic benefits are expected to result from the development of local production economies. In particular, the humanitarian and development sector are becoming increasingly interested in how distributed manufacturing can overcome the supply chain challenges of last mile distribution. Further, distributed manufacturing has been proposed as a key element in the Cosmopolitan localism or cosmolocalism framework to reconfigure production by prioritizing socio-ecological well-being over corporate profits, over-production and excess consumption. == Technology == By localizing manufacturing, distributed manufacturing may enable a balance between two opposite extreme qualities in technology development: Low technology and High tech. This balance is understood as an inclusive middle, a "mid-tech", that may go beyond the two polarities, incorporating them into a higher synthesis. Thus, in such an approach, low-tech and high-tech stop being mutually exclusive. They instead become a dialectic totality. Mid-tech may be abbreviated to "both…and…" instead of "neither…nor…". Mid-tech combines the efficiency and versatility of digital/automated technology with low-tech's potential for autonomy and resilience. == Contracting in Distributed Manufacturing == Research into contracting and order processing models tailored for distributed manufacturing has highlighted the need for flexible, role-based frameworks and advanced digital tools. These tools and frameworks are essential for addressing issues related to quality assurance, payment structures, legal compliance, and coordination among multiple actors. By addressing these challenges, contracting models for distributed manufacturing can unlock its potential for more localized, efficient, and sustainable production systems. A system prototype has been developed to simplify contracting for distributed manufacturing. This tool allows buyers to manage orders across multiple manufacturers using a single interface, automating workflows to ensure clarity and accountability for everyone involved. This research was led by the Internet of Production, as part of the mAkE project (African European Maker Innovation Ecosystem), funded by the European Horizon 2020 research and innovation programme.
Qloo
Qloo (pronounced "clue") is a company that uses artificial intelligence (AI) to understand taste and cultural correlations. It provides companies with an application programming interface (API). It received funding from Leonardo DiCaprio, Elton John, Barry Sternlicht, Pierre Lagrange and others. Qloo establishes consumer preference correlations via machine learning across data spanning cultural domains including music, film, television, dining, nightlife, fashion, books, and travel. The recommender system uses AI to predict correlations for further applications. == History == Qloo was founded in 2012 by chief executive officer Alex Elias and chief operating officer Jay Alger. Qloo initially launched an app designed for consumers, allowing them to understand their own tastes and receive personalized recommendations. The company amassed several million users and built a large catalog of cultural entities and corresponding user sentiment. In 2012, Qloo raised $1.4 million in seed funding from investors including Cedric the Entertainer, and venture capital firm Kindler Capital. Qloo had a public beta release in November 2012 after its initial funding. In 2013, the company raised an additional $1.6 million from Cross Creek Pictures founding partner Tommy Thompson, and Samih Toukan and Hussam Khoury, founders of Maktoob, an Internet services company purchased by Yahoo! for $164 million in 2009. On November 14, 2013, a website and an iPhone app were announced. The company later released an Android app, and tablet versions, in mid-2014. In 2015, Twitter approached Qloo about powering personalized social feeds and targeted eCommerce ads on the platform based on what users were posting. Qloo developed an enterprise-grade API to support Twitter’s needs. Twitter ended up pivoting to enable brands to use the social platform for customer service and support, but Qloo was able to sell access to its cultural intelligence via API to many other enterprise clients, marking the official transition from a B2C company to a B2B company. In 2016, Qloo secured $4.5 million in venture capital investment. The $4.5 million was split between a number of investors, including Barry Sternlicht, Pierre Lagrange, and Leonardo DiCaprio. In July 2017, Qloo raised $6.5 million in funding rounds from AXA Strategic Ventures, and Elton John. Following the investment, the founders stated in an interview with Tech Crunch that they would use the investment to expand Qloo's database. They hoped the move would secure larger contracts with corporate clients. At the time, clients already included Fortune 500 companies such as Twitter, PepsiCo, and BMW. In 2019, the company announced that it had acquired cultural recommendation service TasteDive, with Alex Elias becoming chairman of TasteDive. In September 2019, Qloo was named among the Top 14 Artificial Intelligence APIs by ProgrammableWeb. In 2022, Qloo raised $15M in Series B funding from Eldridge and AXA Venture Partners, enabling the privacy-centric AI leader to expand its team of world-class data scientists, enrich its technology, and build on its sales channels in order to continue to offer premier insights into global consumer taste for Fortune 500 companies across the globe. Qloo was recognized as the "Best Decision Intelligence Company" at the 2023 AI Breakthrough Awards. Also in 2023, the company was awarded a Top Performer Award by SourceForge. As of 2024, Qloo is a three-time Inc. 5000 honoree: No. 360 (2022), No. 344 (2021), No. 187 (2020). Qloo raised $25 million Series C round on February 21, 2024. The round was led by AI Ventures with participation from AXA Venture Partners, Eldridge, and Moderne Ventures, allowing Qloo to address new commercial surface areas for Taste AI, including on-device learning and foundational models leveraging Qloo, as well as introduce self-service platform to make consumer and taste analytics available to small and mid-sized enterprises and individuals. Qloo also announced pursuing opportunistic M&A using its balance sheet along the lines of the TasteDive acquisition completed, which expanded Qloo's first-party data moat and corpus of cultural learning. This latest financing brought the total amount raised since the company's founding in 2012 to over $56 million. == Services and features == Qloo calls itself a cultural AI platform to provide real-time correlation data across domains of culture and entertainment including: film, music, television, dining, nightlife, fashion, books, and travel. Each category contains subcategories. Qloo’s knowledge of a user's taste in one category can be utilized to offer suggestions in other categories. Users then rate the suggestions, providing it with feedback for future suggestions. Qloo has partnerships with companies such as Expedia and iTunes. == Technology == Qloo’s Taste AI technology uses machine learning to decode and predict consumers’ interests, maintaining user anonymity. It is powered by 3.7 billion lifestyle entities (brands, music, film, TV, dining, nightlife, fashion, books, travel, and more) and trillions of anonymized consumer behavioral signals. Through AI, Qloo identifies patterns in these data signals, making predictions about how much interest a person or group has in a concept or thing. Central to Qloo’s technology are algorithms designed to detect and mitigate biases within datasets and models, allowing Qloo to assess the fairness of its AI systems with a focus on attributes such as age, gender, and race, enabling the company to fine-tune its AI models to align with their ethical standards. They also use visualization tools to probe the behavior of their AI models for conducting counterfactual analyses and for comparing the performances of the AI models across diverse demographic segments. Qloo’s Taste AI doesn’t collect or use any Personally Identifiable Information (PII). Instead, it derives recommendations for audience segments based on co-occurrences between lifestyle entities and anonymized behavioral signals. == Applications == Starbucks uses Qloo to create in-store music playlists tailored to specific neighborhoods. Hershey’s uses Qloo to customize the content of assorted candy bags. Michelin uses Qloo to serve recommendations in its Michelin Guide app. Netflix leverages Qloo’s technology to enhance merchandising by identifying actors who resonate with certain demographics. Qloo also works with PepsiCo, Samsung, The New York Mets, BuzzFeed, and Ticketmaster, Universal Music Group, and OOH advertising company JCDecaux.
GCube system
gCube is an open source software system specifically designed and developed to enact the building and operation of a Data Infrastructure providing their users with a rich array of services suitable for supporting the co-creation of Virtual Research Environments and promoting the implementation of open science workflows and practices. It is at the heart of the D4Science Data Infrastructure. == Overview == It is primarily organised in a number of web service called to offer functionality supporting the phases of knowledge production and sharing. In addition, it consists of a set of software libraries supporting service development, service-to-service integration, and service capabilities extension, and a set of portlets dedicated to realise user interface constituents facilitating the exploitation of one or more services. It is designed and conceived to enact system of systems. In fact, its gCube services rely on standards and mediators to interact with other services as well as are made available by standard and APIs to make it possible for clients to use them. For instance, the DataMiner service implements the Web Processing Service protocol to facilitate clients to execute processes. The set of components dealing with Identity and Access Management rely on Keycloak and federates other IDMs thus making the overall Authentication and the Authorization management compliant with open standards such as OAuth2, User-Managed Access (UMA), and OpenID Connect (OIDC)protocols. The Catalogue relies on DCAT, OAI-PMH, and Catalogue Service for the Web to collect contents from other catalogues and data sources and offers its content by DCAT, OAI-PMH, and a proprietary REST API (gCat REST API). Its Continuous Integration/Continuous Delivery pipeline implemented by Jenkins represents an innovative approach to software delivering conceived to be scalable and easy to maintain and upgrade at a minimal cost. == History == gCube has been developed in the context of the D4Science initiative with the support of several EU projects.