Flok (formerly Loyalblocks) was an American tech startup based in New York City that provides marketing services such as chatbots/AI, customer loyalty programs, mobile apps and CRM services to local businesses. In January 2017, the company was acquired by Wix.com. Around March 2017, Flok ceased regular communication. At some point in 2019 Flok communicated to its customers that it would shut down in March 2020. == Background == Flok was founded in 2011 by Ido Gaver and Eran Kirshenboim and has offices in Tel Aviv, Israel. In May 2013, Flok secured a $9 million Series A Round from General Catalyst Partners with participation from Founder Collective and existing investor Gemini Israel Ventures. In total, Flok has raised over $18 million in venture capital in three rounds. In May 2014, Flok announced a self-service loyalty platform for SMBs to build their own programs with beacon integration. At that time, approximately 40,000 businesses were using the service. In 2016, Flok released a turnkey chatbot service for local businesses, and was featured in AdWeek for developing the first weed bot chatbot for a California cannabis business. == Services == Flok offered an eponymous customer-facing app, that consumers use to receive rewards and deals from partner businesses, and a Flok business app for merchants to manage the platform.
Artisse AI
Artisse AI is a Hong Kong-based technology company founded by William Wu. The company developed a mobile photography application using generative artificial intelligence to transform selfies into high-quality, personalized images. The app allows users to visualize themselves in various scenarios, outfits, and hairstyles, and they can adjust lighting and ambiance to match their preferences. The app launched in 2023 across multiple markets, including the United States, United Kingdom, Japan, South Korea, Canada, and Australia. By January 2024, users had generated over 5 million images. That same month, the company secured $6.7 million in seed funding to support product development and marketing. == History == Artisse was originally founded in South Korea in 2022 by William Wu. The early concept was connected to a virtual idol initiative developed in collaboration with a K-pop agency, intended to support Wu's blockchain gaming business. The project later evolved into a standalone AI photography application. The current version of the Artisse app was developed following the company's relocation to Hong Kong in 2022. In January 2024, Artisse secured $6.7 million in seed funding, led by The London Fund. The investment was aimed at supporting product development, marketing, and user acquisition. Artisse uses an AI algorithm to create hyperrealistic images from uploaded photos. The app generates personalized images by combining generative AI technology, a global pool of licensed talent, and finished art services. The app works with individual users and businesses, offering professional-grade photos and advertisement images. According to the British newspaper Evening Standard the company has developed the world's first and most advanced AI photographer. It captures 15-30 photos of the user and generates 2D images, placing them in various outfits and locations worldwide. === Catheron Gaming === Artisse AI originated from Catheon Gaming, a blockchain gaming and entertainment company founded in 2021 by William Wu. Catheon Gaming published more than 30 Web3 titles in its first year, developed a blockchain game distribution platform, and offered advisory services to external developers. In 2022, HSBC and KPMG listed Catheon Gaming among the "Top 10 Emerging Giants" in the Asia–Pacific region, selected from a pool of more than 6,000 startups. In June 2023, Catheon Gaming was rebranded as Artisse Interactive, creating two divisions: Artisse Gaming, which continued blockchain and Web3 game development, and Artisse AI, which focused on generative photography technology. == Technology == Artisse uses a proprietary generative AI model combined with open-source imaging frameworks and diffusion models. Users are prompted to upload between 15 and 30 personal images, allowing the AI to train a personalized model in 30 to 40 minutes. After training, the app generates new images based on either textual or visual prompts, with options to adjust elements such as clothing, hairstyles, lighting, and backgrounds. To enhance realism, the app integrates augmented reality features and image refinement tools. The company has introduced features to address representation issues related to body shape and skin tone, although concerns persist about the ethical implications of altering personal traits. == Products == === Artisse mobile app === Available on iOS and Android platforms in 35 languages. Users initially receive 25 free images, after which the app adopts a subscription pricing model ranging from approximately $6 to $30 per month. By early 2024, the app reported around 4,000 paying subscribers out of more than 200,000 downloads. === Business and enterprise services === Artisse provides B2B solutions for creating marketing imagery and partners with agencies like Iconic Management to enable cost-effective virtual photoshoots. Additional features in development include virtual try-on capabilities and augmented reality integration for fashion retail. == Reception == Media coverage has noted the app's photorealistic image outputs with some sources highlighting its ease of use. However, concerns have been raised regarding image authenticity, algorithmic biases, and the potential impact on professional photography and modeling. Artisse has been widely covered by media outlets including TechCrunch, PetaPixel, Forbes Australia, and The Evening Standard. These publications discussed the app's integration of generative AI technology within the consumer photography space, its growing market influence, and its rapid adoption by users worldwide.
Europa-Lehrmittel
The Verlag Europa-Lehrmittel Nourney, Vollmer GmbH & Co. KG (English: European educational media), based in Haan (district Gruiten) near Düsseldorf, Germany, develops educational media for vocational training and further education as used in vocational schools, universities, companies and further education institutions for industrial-technical, business, social, health as well as gastronomic professions. The contents are delivered in print as well as in digital formats, including offered as apps, simulations, software and through an exam preparation portal. The publisher has its own digital learning platform, the Europathek. General partner is Nourney, Vollmer & Co. GmbH, a school and specialist book publisher. == History == The publishing house was founded in Wuppertal in 1948, when the first books were published. In Eislingen/Fils a construction office was set up to create the technical drawings for the textbooks - today the drawing office of the publisher in Ostfildern. A little over 40 years later, in 1989, a new company building was built in Haan-Gruiten. In 1997, Europa-Lehrmittel acquired the Fachbuchverlag Pfanneberg with its program aimed at gastronomic professions. Further program expansions in vocational training were realized by adding titles of the publishing houses Lau, Gerber, Pluspunkt, parts of the Verlag Handwerk und Technik program and that of the Gildebuchverlag. In 2013, the scientific and technical university titles of Verlag Harri Deutsch were taken over, including the bestselling "Handbook of Mathematics" by Ilya Nikolaevich Bronstein and Konstantin Adolfovic Semendyayev. In addition, Europa-Lehrmittel's first exam apps appeared. In 2017, the publisher introduced its own digital learning platform named Europathek. The Prüfungsdoc (English: Examination Doc) platform, which enables targeted online preparation for exams, went online the same year. In 2018, the titles of the Düsseldorf publishing house SOL for self-organized learning were incorporated into Europa-Lehrmittel's program. == Program == The program meanwhile includes well over 2000 print and digital publications, including specialist titles on metal technology, automotive and electrical engineering (like "Tabellenbuch Metall" (English: Metal Book of Tables)), on business administration, as well as works for the gastronomic profession (e.g. "Der junge Koch" (English: The Young Cook)). Many titles also appear as licensed editions in over 20 different languages on all continents. All contents are presented in an annual catalog, on the website, in schools, in companies, and at trade fairs and congresses. The publisher is a member of the Deutscher Hauswirtschaftsrat. == Digital educational media == Europathek: In addition to digital books, Europathek also provides media packages, additional materials and e-learning contents. The online contents of the 'media shelf' can be used per web browser. There are also software versions and apps for offline use, with the help of which the obtained titles can be viewed after downloading. Prüfungsdoc (English: Examination Doc): Prüfungsdoc offers web-based learning, practice and repetition to prepare for the intermediate and final examinations. This includes simulations of the exams with direct evaluation of the results.
Cardiovascular health awareness in Nepal
Cardiovascular health awareness in Nepal has improved in recent times. It has emerged as a cost-effective tool for prevention of heart disease in recent years. == Background == Cardiovascular diseases in Nepal is responsible for maximum number of deaths and rapid urbanization possess extra threat in this situation emphasizing need for widespread awareness campaigns. Efforts to enhance cardiovascular health awareness among the general public, particularly through social media platforms, have proven pivotal in motivating individuals to adopt healthier lifestyle choices and improve heart health in Nepal in recent years. These awareness initiatives, often organized on special occasions such as World Heart Day of World Heart Federation, World Hypertension Day, and World Health Day of World Health Organization, have garnered active participation from the public, reflecting a growing interest in health-related information. Notably, various hospitals, organizations, and health professionals actively contribute to cardiovascular health awareness in Nepal. The prevalence of heart health-related content on social media platforms indicates a substantial dissemination of information. The engagement of a large number of followers underscores the audience's keen interest in health matters, evident in the significant engagement on various social media accounts and pages. During an event of cardiovascular health awareness a Guinness World Record was achieved on World Heart Day 2023. A Facebook live awareness programme on heart health prevention attracted 11,212 viewers during the 30-minute presentation and was awarded the "Most viewers for a cardiovascular health awareness live stream on Facebook" by Guinness World Record signifying depth of public involvement and importance of social media use in health awareness. In 2014, a 11-day heart camp was conducted in Nepal to raise awareness . In 2015, a is a heart health education program " Mission to Save Heart" was conducted to empower health professionals to manage heart attacks and reduce heart disease deaths.
Horus Music
Horus Music Limited is a global digital distribution and label services company. Established in 2006, Horus Music allows artists, labels and right-holders to send their music to over 200 download, streaming, and interactive platforms including iTunes, Google Play, Amazon, VEVO, 7digital, Spotify, Beatport, Deezer, Tidal, as well as offering digital marketing and playlisting opportunities. == History == The company were named Best Business Partner of 2014 by Huawei Technology of China, and were also a finalist in the International Trade category as part of the Leicester Mercury Business Awards during that same year. Their client base consists of unsigned and independent musicians and record labels, as well as well known recording artists. In November 2015, Horus Music sponsored the UK’s first Independent Label Week, in order to highlight the music that is released by the UK’s indie labels. In 2016, Horus Music celebrated their 10th anniversary Horus Music's sister companies Help for Bands and Help For Writers, provide advice and opportunities for musicians and E-book distribution for writers, respectively. Anara Publishing opened in 2017 which allows the company to work closely with a handpicked roster of musicians to provide royalty administration and sync licensing services. On 21 April 2017, Her Majesty Queen Elizabeth II’s 91st birthday, Horus Music was awarded with the Queen’s Award for Enterprise in International Trade. In 2021, Horus Music, UnitedMasters, and Symphonic Distribution partnered with pioneering music fintech company, beatBread, to offer clients access to more capital. beatBread's chordCashAI technology provides an automated advance experience for independent musicians while enable clients to choose their own terms and retain ownership of their music. == Clients == Horus Music has partnered with a number of charities including Save the Children, for the recording "Look into Your Heart", featuring Beverley Knight with Rolling Stones' Mick Jagger and Ronnie Wood, 100% of proceeds from the single were donated to the charity. The Pixel Project, who produced songs about violence against women and the blood cancer charity Bloodwise. The company have spoken openly about the state of the music industry and artists' rights and were one of the first distributors to remove their catalogue from Rdio after the streaming service was acquired by Pandora. Their relationships with artists and labels, as well as leading industry contacts, means they have the ability to work with musicians in a myriad of ways, including offering performance opportunities and even local auditions for TV shows such as The Voice UK. == Horus Music India == Horus Music India opened in 2016 and is based in Mumbai. By opening Horus Music India, the company are able to expand on their local connections as well as to provide a much more personalised service to musicians based in this area. The appointment of two Business Development Managers in India cemented their move.
Visual analytics
Visual analytics is a multidisciplinary science and technology field that emerged from information visualization and scientific visualization. It focuses on how analytical reasoning can be facilitated by interactive visual interfaces. == Overview == Visual analytics is "the science of analytical reasoning facilitated by interactive visual interfaces." It can address problems whose size, complexity, and need for closely coupled human and machine analysis may make them otherwise intractable. Visual analytics advances scientific and technological development across multiple domains, including analytical reasoning, human–computer interaction, data transformations, visual representation for computation and analysis, analytic reporting, and the transition of new technologies into practice. As a research agenda, visual analytics brings together several scientific and technical communities from computer science, information visualization, cognitive and perceptual sciences, interactive design, graphic design, and social sciences. Visual analytics integrates new computational and theory-based tools with innovative interactive techniques and visual representations to enable human-information discourse. The design of the tools and techniques is based on cognitive, design, and perceptual principles. This science of analytical reasoning provides the reasoning framework upon which one can build both strategic and tactical visual analytics technologies for threat analysis, prevention, and response. Analytical reasoning is central to the analyst's task of applying human judgments to reach conclusions from a combination of evidence and assumptions. Visual analytics has some overlapping goals and techniques with information visualization and scientific visualization. There is currently no clear consensus on the boundaries between these fields, but broadly speaking the three areas can be distinguished as follows: Scientific visualization deals with data that has a natural geometric structure (e.g., MRI data, wind flows). Information visualization handles abstract data structures such as trees or graphs. Visual analytics is especially concerned with coupling interactive visual representations with underlying analytical processes (e.g., statistical procedures, data mining techniques) such that high-level, complex activities can be effectively performed (e.g., sense making, reasoning, decision making). Visual analytics seeks to marry techniques from information visualization with techniques from computational transformation and analysis of data. Information visualization forms part of the direct interface between user and machine, amplifying human cognitive capabilities in six basic ways: by increasing cognitive resources, such as by using a visual resource to expand human working memory, by reducing search, such as by representing a large amount of data in a small space, by enhancing the recognition of patterns, such as when information is organized in space by its time relationships, by supporting the easy perceptual inference of relationships that are otherwise more difficult to induce, by perceptual monitoring of a large number of potential events, and by providing a manipulable medium that, unlike static diagrams, enables the exploration of a space of parameter values These capabilities of information visualization, combined with computational data analysis, can be applied to analytic reasoning to support the sense-making process. == History == As an interdisciplinary approach, visual analytics has its roots in information visualization, cognitive sciences, and computer science. The term and scope of the field was defined in the early 2000s through researchers such as Jim Thomas, Kristin A. Cook, John Stasko, Pak Chung Wong, Daniel A. Keim and David S. Ebert. As a reaction to the September 11, 2001 attacks the United States Department of Homeland Security was established in late 2002, combining dozens of previously separated government agencies. Building upon earlier work on visual data mining by Daniel A. Keim starting in the late 1990s, this simultaneously lead to the development of a research agenda for visual analytics. As part of these efforts the National Visualization and Analytics Center (NVAC) at Pacific Northwest National Laboratory was established in 2004, whose charter was to develop system to mitigate information overload after the September 11, 2001 attacks in the intelligence community. Their research work determined core challenges, posed open research questions, and positioned visual analytics as a new research domain, in particular through the 2005 research agenda Illuminating the Path. In 2006, the IEEE VIS community led by Pak Chung Wong and Daniel A. Keim launched the annual IEEE Conference on Visual Analytics Science and Technology (VAST), providing a dedicated venue for research into visual analytics, which in 2020 merged to form the IEEE Visualization conference. In 2008, scope and challenges of visual analytics were conceptually defined by Daniel A. Keim and Jim Thomas in their influential book about visual data mining. The domain was further refined as part of the European Commissions FP7 VisMaster program in the late 2000s. == Topics == === Scope === Visual analytics is a multidisciplinary field that includes the following focus areas: Analytical reasoning techniques that enable users to obtain deep insights that directly support assessment, planning, and decision making Data representations and transformations that convert all types of conflicting and dynamic data in ways that support visualization and analysis Techniques to support production, presentation, and dissemination of the results of an analysis to communicate information in the appropriate context to a variety of audiences. Visual representations and interaction techniques that take advantage of the human eye's broad bandwidth pathway into the mind to allow users to see, explore, and understand large amounts of information at once. === Analytical reasoning techniques === Analytical reasoning techniques are the method by which users obtain deep insights that directly support situation assessment, planning, and decision making. Visual analytics must facilitate high-quality human judgment with a limited investment of the analysts’ time. Visual analytics tools must enable diverse analytical tasks such as: Understanding past and present situations quickly, as well as the trends and events that have produced current conditions Identifying possible alternative futures and their warning signs Monitoring current events for emergence of warning signs as well as unexpected events Determining indicators of the intent of an action or an individual Supporting the decision maker in times of crisis. These tasks will be conducted through a combination of individual and collaborative analysis, often under extreme time pressure. Visual analytics must enable hypothesis-based and scenario-based analytical techniques, providing support for the analyst to reason based on the available evidence. === Data representations === Data representations are structured forms suitable for computer-based transformations. These structures must exist in the original data or be derivable from the data themselves. They must retain the information and knowledge content and the related context within the original data to the greatest degree possible. The structures of underlying data representations are generally neither accessible nor intuitive to the user of the visual analytics tool. They are frequently more complex in nature than the original data and are not necessarily smaller in size than the original data. The structures of the data representations may contain hundreds or thousands of dimensions and be unintelligible to a person, but they must be transformable into lower-dimensional representations for visualization and analysis. === Theories of visualization === Theories of visualization include: Jacques Bertin's Semiology of Graphics (1967) Nelson Goodman's Languages of Art (1977) Jock D. Mackinlay's Automated design of optimal visualization (APT) (1986) Leland Wilkinson's Grammar of Graphics (1998) Hadley Wickham's Layered Grammar of Graphics (2010) === Visual representations === Visual representations translate data into a visible form that highlights important features, including commonalities and anomalies. These visual representations make it easy for users to perceive salient aspects of their data quickly. Augmenting the cognitive reasoning process with perceptual reasoning through visual representations permits the analytical reasoning process to become faster and more focused. == Process == The input for the data sets used in the visual analytics process are heterogeneous data sources (i.e., the internet, newspapers, books, scientific experiments, expert systems). From these rich sources, the data sets S = S1, ..., Sm are chosen, whereas each Si , i ∈ (1, ..., m) consists of attrib
Attention inequality
Attention inequality is the inequality of distribution of attention across users on social networks, people in general, and for scientific papers. Yun Family Foundation introduced "Attention Inequality Coefficient" as a measure of inequality in attention and arguments it by the close interconnection with wealth inequality. == Relationship to economic inequality == Attention inequality is related to economic inequality since attention is an economically scarce good. The same measures and concepts as in classical economy can be applied for attention economy. The relationship develops also beyond the conceptual level—considering the AIDA process, attention is the prerequisite for real monetary income on the Internet. On data of 2018, a significant relationship between likes and comments on Facebook to donations is proven for non-profit organizations. == Attention economy == The attention economy refers to the practice of maximizing the attention users give to a product for advertising-related reasons. Attention economy remains one of the most common forms of advertising, and has been steadily increasing thanks to new technologies such as television, internet and social media. It is one of the most widely-used approaches to economy for its effectiveness for maximising the noticeability of a certain product. == Attention inequality in social media == In social media, attention inequality refers to the unequal distribution of users' attention on social media platforms. This means that instead of an equal distribution of attention, fewer sources receive a disproportionate share of attention, leaving many unnoticed. This phenomenon is possibly the result of social media algorithms, which are commonly designed to drive maximum engagement. This phenomenon is a large factor in the polarization and creation of echo-chambers. Social media algorithms tend to note content that is already performing well and display it to more users, while content that is equally engaging or well-made is not recommended to users. Posts that trigger strong emotions usually out-perform more "uncontroversial" content. When many users interact with the post, it signals the algorithm that the specific post drives engagement. The algorithm then tends to recommend that type of content to an exponential number of people, potentially outperforming "un-emotional" content. These factors, when combined, tend to create an unequal social media environment. == Attention inequality in science == According to a recent 2025 study about research inequality among scientists published in Information Processing and Management, scientific discourse is restricted to a small group of connected scientists, and is frequently not an accurate representation of the whole scientific community. Using citation-network analysis in the fields of nanoscience and chemical physics, the study claims that a group of connected scientists has a significant notability in the scientific community. The calculated connection strength between these scientists is estimated to be about 4.5, the study also says that these authors cite each other four times more often than would be predicted in a random network, whereas ordinary scientists that exist outside of this group only reach an estimated connection strength of 0.9. The study findings suggest that that scientific attention is not distributed by merit, but rather by the connectedness of the scientists involved in the research. == Extent == As data of 2008 shows, 50% of the attention is concentrated on approximately 0.2% of all hostnames, and 80% on 5% of hostnames. The Gini coefficient of attention distribution lay in 2008 at over 0.921 for such commercial domains names as ac.jp and at 0.985 for .org-domains. The Gini coefficient was measured on Twitter in 2016 for the number of followers as 0.9412, for the number of mentions as 0.9133, and for the number of retweets as 0.9034. For comparison, the world's income Gini coefficient was 0.68 in 2005 and 0.904 in 2018. More than 96% of all followers, 93% of the retweets, and 93% of all mentions are owned by 20% of Twitter. == Causes == At least for scientific papers, today's consensus states that inequality is unexplainable by variations of quality and individual talent. The Matthew effect plays a significant role in the emergence of attention inequality—those who already enjoy large amounts of attention get even more attention, and those who do not lose even more. Ranking algorithms based on relevance to the user have been found to alleviate the inequality of the number of posts across topics.