QANDA (stands for 'Q and A') is an AI-based learning platform developed by Mathpresso Inc., a South Korea-based education technology company. Its best known feature is a solution search, which uses optical character recognition technology to scan problems and provide step-by-step solutions and learning content. As of March 2024, QANDA solved over 6.3 billion questions. QANDA has 90 million total registered users and has reached 8 million monthly active users (MAU) in 50 countries. 90% of the cumulative users are from overseas such as Vietnam and Indonesia. In January 2024, its MathGPT, a math-specific small large language model set a new world record, surpassed Microsoft's 'ToRA 13B', the previous record holder in benchmarks assessing mathematical performance such as 'MATH' (high school math) and 'GSM8K' (grade school math). 'MathGPT' was co-developed with Upstage and KT. In March 2024, Mathpresso launched 'Cramify' (formerly known as Prep.Pie), an AI-powered study material generator designed to create personalized exam prep materials for U.S. college students. It uses generative AI to create customized study materials uploaded by students. Its features include a range of tools including study summarizer and question solver. == History == Co-founder Jongheun ‘Ray’ Lee first came up with the idea of QANDA during his freshman year in college. While he was tutoring to earn money, Lee realized that the quality of education a student receives is greatly based on their location. Lee saw his K-12 students were regularly asking similar questions and realized that these questions were from a pre-selected number of textbooks currently being used in schools. He decided to team up with his high school friend, Yongjae ‘Jake’ Lee to build a platform whereby, one uses a mobile app to scan and submit questions, and students can ask and receive detailed responses. Lee's school friends, Wonguk Jung and Hojae Jeong, joined the team. In June 2015, Mathpresso, Inc. was founded in Seoul, South Korea. In January 2016, Mathpresso's first product QANDA was launched. It supported a Q&A feature between students and tutors. In October 2017, QANDA introduced an AI-based search capability that permitted users to search for answers in seconds. In April 2020, Jake Yongjae Lee(CEO & co-founder) and Ray Jongheun Lee (co-founder) were selected as Forbes 30 under 30 Asia. In June 2021, QANDA raised $50 million in series C funding. Jake Yongjae Lee was recognized as an Innovator Under 35 by MIT Technology Review. In November 2021, QANDA secured a strategic investment from Google. Since its inception, it has received backing in Series C funding from investors namely Google, Yellowdog, GGV Capital, Goodwater Capital, KDB, and SKS Private Equity with participation from SoftBank Ventures Asia, Legend Capital, Mirae Asset Venture Investment, and Smilegate Investment. In September 2023, Mathpresso has raised $8 million (10 billion KRW) from Korea's telecom giant, KT. The total cumulative investment is about 130 million US dollars. The partnership aims to accelerate the development of an education-specific Large Language Model. The company intends to incorporate the LLM model to fortify its AI tutor, which later will be integrated into the existing services: QANDA App, B2B & B2G Saas, and 1:1 online tutoring (QANDA Tutor). == Features == QANDA features OCR-based solution search, one-on-one Q&A tutoring, a study timer. In 2021, QANDA launched additional features, including the premium subscription model that offers unlimited “byte-sized” micro-video lectures and the community feature that enhances collaborative learning. In 2021, QANDA launched QANDA Tutor, a tablet-based 1:1 tutoring service and QANDA Study, a 1:N online school in Vietnam. In 2022, QANDA launched an exam prep feature that offers past exam materials from school via online. This feature is currently available in South Korea. In August 2023, QANDA launched a beta version of an LLM-powered AI Tutor. == Awards and recognition == Best Hidden Gems of 2017 by Google Playstore 2018 AWS AI Startup Challenge Award National representative for the Google AI for Social Good APAC, 2018 Best Self-Improvement Apps of 2018 by Google Playstore GSV Edtech 150 — the Most Transformational Growth Companies in Digital Learning Speaker at the Google App Summit, 2021 Selected as a prospect unicorn company by Korea Technology Finance Corporation in 2023 Winner of G20-DIA Global Pitching in 2023 2021, 2022, 2023 East Asia EdTech 150 by HolonIQ
Bandhan Tod
Bandhan Tod is a mobile app to stop child marriage in India's Bihar state through SOS button in the app. When the SOS on Bandhan Tod is activated, the nearest small NGO will attempt to resolve the issue. If the family resists, then the police gets notified. Till now so many child marriages has been cancelled through Bandhan Tod interventions. Bandhan Tod is an initiative of Gender Alliance managed by Prashanti Tiwari to support the state government's efforts to end child marriage and dowry.
Human image synthesis
Human image synthesis is technology that can be applied to make believable and even photorealistic renditions of human-likenesses, moving or still. It has effectively existed since the early 2000s. Many films using computer generated imagery have featured synthetic images of human-like characters digitally composited onto the real or other simulated film material. Towards the end of the 2010s deep learning artificial intelligence has been applied to synthesize images and video that look like humans, without need for human assistance, once the training phase has been completed, whereas the old school 7D-route required massive amounts of human work. == Timeline of human image synthesis == In 1971 Henri Gouraud made the first CG geometry capture and representation of a human face. Modeling was his wife Sylvie Gouraud. The 3D model was a simple wire-frame model and he applied the Gouraud shader he is most known for to produce the first known representation of human-likeness on computer. The 1972 short film A Computer Animated Hand by Edwin Catmull and Fred Parke was the first time that computer-generated imagery was used in film to simulate moving human appearance. The film featured a computer simulated hand and face (watch film here). The 1976 film Futureworld reused parts of A Computer Animated Hand on the big screen. The 1983 music video for song Musique Non-Stop by German band Kraftwerk aired in 1986. Created by the artist Rebecca Allen, it features non-realistic looking, but clearly recognizable computer simulations of the band members. The 1994 film The Crow was the first film production to make use of digital compositing of a computer simulated representation of a face onto scenes filmed using a body double. Necessity was the muse as the actor Brandon Lee portraying the protagonist was tragically killed accidentally on-stage. In 1999 Paul Debevec et al. of USC captured the reflectance field of a human face with their first version of a light stage. They presented their method at the SIGGRAPH 2000 In 2003 audience debut of photo realistic human-likenesses in the 2003 films The Matrix Reloaded in the burly brawl sequence where up-to-100 Agent Smiths fight Neo and in The Matrix Revolutions where at the start of the end showdown Agent Smith's cheekbone gets punched in by Neo leaving the digital look-alike unnaturally unhurt. The Matrix Revolutions bonus DVD documents and depicts the process in some detail and the techniques used, including facial motion capture and limbal motion capture, and projection onto models. In 2003 The Animatrix: Final Flight of the Osiris a state-of-the-art want-to-be human likenesses not quite fooling the watcher made by Square Pictures. In 2003 digital likeness of Tobey Maguire was made for movies Spider-man 2 and Spider-man 3 by Sony Pictures Imageworks. In 2005 the Face of the Future project was an established. by the University of St Andrews and Perception Lab, funded by the EPSRC. The website contains a "Face Transformer", which enables users to transform their face into any ethnicity and age as well as the ability to transform their face into a painting (in the style of either Sandro Botticelli or Amedeo Modigliani). This process is achieved by combining the user's photograph with an average face. In 2009 Debevec et al. presented new digital likenesses, made by Image Metrics, this time of actress Emily O'Brien whose reflectance was captured with the USC light stage 5 Motion looks fairly convincing contrasted to the clunky run in the Animatrix: Final Flight of the Osiris which was state-of-the-art in 2003 if photorealism was the intention of the animators. In 2009 a digital look-alike of a younger Arnold Schwarzenegger was made for the movie Terminator Salvation though the end result was critiqued as unconvincing. Facial geometry was acquired from a 1984 mold of Schwarzenegger. In 2010 Walt Disney Pictures released a sci-fi sequel entitled Tron: Legacy with a digitally rejuvenated digital look-alike of actor Jeff Bridges playing the antagonist CLU. In SIGGGRAPH 2013 Activision and USC presented a real-time "Digital Ira" a digital face look-alike of Ari Shapiro, an ICT USC research scientist, utilizing the USC light stage X by Ghosh et al. for both reflectance field and motion capture. The end result both precomputed and real-time rendering with the modernest game GPU shown here and looks fairly realistic. In 2014 The Presidential Portrait by USC Institute for Creative Technologies in conjunction with the Smithsonian Institution was made using the latest USC mobile light stage wherein President Barack Obama had his geometry, textures and reflectance captured. In 2014 Ian Goodfellow et al. presented the principles of a generative adversarial network. GANs made the headlines in early 2018 with the deepfakes controversies. For the 2015 film Furious 7 a digital look-alike of actor Paul Walker who died in an accident during the filming was done by Weta Digital to enable the completion of the film. In 2016 techniques which allow near real-time counterfeiting of facial expressions in existing 2D video have been believably demonstrated. In 2016 a digital look-alike of Peter Cushing was made for the Rogue One film where its appearance would appear to be of same age as the actor was during the filming of the original 1977 Star Wars film. In SIGGRAPH 2017 an audio driven digital look-alike of upper torso of Barack Obama was presented by researchers from University of Washington. It was driven only by a voice track as source data for the animation after the training phase to acquire lip sync and wider facial information from training material consisting 2D videos with audio had been completed. Late 2017 and early 2018 saw the surfacing of the deepfakes controversy where porn videos were doctored using deep machine learning so that the face of the actress was replaced by the software's opinion of what another persons face would look like in the same pose and lighting. In 2018 Game Developers Conference Epic Games and Tencent Games demonstrated "Siren", a digital look-alike of the actress Bingjie Jiang. It was made possible with the following technologies: CubicMotion's computer vision system, 3Lateral's facial rigging system and Vicon's motion capture system. The demonstration ran in near real time at 60 frames per second in the Unreal Engine 4. In 2018 at the World Internet Conference in Wuzhen the Xinhua News Agency presented two digital look-alikes made to the resemblance of its real news anchors Qiu Hao (Chinese language) and Zhang Zhao (English language). The digital look-alikes were made in conjunction with Sogou. Neither the speech synthesis used nor the gesturing of the digital look-alike anchors were good enough to deceive the watcher to mistake them for real humans imaged with a TV camera. In September 2018 Google added "involuntary synthetic pornographic imagery" to its ban list, allowing anyone to request the search engine block results that falsely depict them as "nude or in a sexually explicit situation." In February 2019 Nvidia open sources StyleGAN, a novel generative adversarial network. Right after this Phillip Wang made the website ThisPersonDoesNotExist.com with StyleGAN to demonstrate that unlimited amounts of often photo-realistic looking facial portraits of no-one can be made automatically using a GAN. Nvidia's StyleGAN was presented in a not yet peer reviewed paper in late 2018. At the June 2019 CVPR the MIT CSAIL presented a system titled "Speech2Face: Learning the Face Behind a Voice" that synthesizes likely faces based on just a recording of a voice. It was trained with massive amounts of video of people speaking. Since 1 July 2019 Virginia has criminalized the sale and dissemination of unauthorized synthetic pornography, but not the manufacture., as § 18.2–386.2 titled 'Unlawful dissemination or sale of images of another; penalty.' became part of the Code of Virginia. The law text states: "Any person who, with the intent to coerce, harass, or intimidate, maliciously disseminates or sells any videographic or still image created by any means whatsoever that depicts another person who is totally nude, or in a state of undress so as to expose the genitals, pubic area, buttocks, or female breast, where such person knows or has reason to know that he is not licensed or authorized to disseminate or sell such videographic or still image is guilty of a Class 1 misdemeanor.". The identical bills were House Bill 2678 presented by Delegate Marcus Simon to the Virginia House of Delegates on 14 January 2019 and three-day later an identical Senate bill 1736 was introduced to the Senate of Virginia by Senator Adam Ebbin. Since 1 September 2019 Texas senate bill SB 751 amendments to the election code came into effect, giving candidates in elections a 30-day protection period to the elections during which making and distributing digital look-alikes or synthetic fakes of the candidates is an offense. Th
Digital video effect
Digital video effects (DVEs) are visual effects that provide comprehensive live video image manipulation, in the same form as optical printer effects in film. DVEs differ from standard video switcher effects (often referred to as analog effects) such as wipes or dissolves, in that they deal primarily with resizing, distortion or movement of the image. Modern video switchers often contain internal DVE functionality. Modern DVE devices are incorporated in high-end broadcast video switchers. Early examples of DVE devices found in the broadcast post-production industry include the Ampex Digital Optics (ADO), Quantel DPE-5000, Vital Squeezoom, NEC E-Flex and the Abekas A5x series of DVEs. By 1988, Grass Valley Group caught up with the competition with their Kaleidoscope, which integrated ADO-type effects with their widely used line of broadcast switching gear. DVEs are used by the broadcast television industry in live television production environments like television studios and outside broadcasts. They are commonly used in video post-production.
Tensor glyph
In scientific visualization a tensor glyph is an object that can visualize all or most of the nine degrees of freedom, such as acceleration, twist, or shear – of a 3 × 3 {\displaystyle 3\times 3} matrix. It is used for tensor field visualization, where a data-matrix is available at every point in the grid. "Glyphs, or icons, depict multiple data values by mapping them onto the shape, size, orientation, and surface appearance of a base geometric primitive." Tensor glyphs are a particular case of multivariate data glyphs. There are certain types of glyphs that are commonly used: Ellipsoid Cuboid Cylindrical Superquadrics According to Thomas Schultz and Gordon Kindlmann, specific types of tensor fields "play a central role in scientific and biomedical studies as well as in image analysis and feature-extraction methods."
Umoove
Umoove is a high tech startup company that has developed and patented a software-only face and eye tracking technology. The idea was first conceived as an attempt to aid people with disabilities but has since evolved. The only compatibility qualification for tablet computers and smartphones to run Umoove software is a front-facing camera. Umoove headquarters are in Israel on Jerusalem’s Har Hotzvim. Umoove has 15 employees and received two million dollars in financing in 2012. The company's original founders invested around $800,000 to start the business in 2010. In 2013 Umoove was named one of the top three most promising Israeli start ups by Newsgeeks magazine. The company also participated in the 2013 LeWeb conference in Paris, France, where innovative technology startups are showcased. == Technology == The technology uses information extracted from previous frames, such as the angle of the user's head to predict where to look for facial targets in the next frame. This anticipation minimizes the amount of computation needed to scan each image. Umoove accounts for variances in environment, lighting conditions and user hand shake/movement. The technology is designed to provide a consistent experience, whether you're in a brightly lit area or a darkened basement, and to work fluidly between them by adapting its processing when it detects color and brightness shifts. It uses an active stabilization technique to filter out natural body movements from an unstable camera in order to minimize false-positive motion detection. Running the Umoove software on a Samsung Galaxy S3 is said to take up only 2% CPU. Umoove works exclusively with software and there is no hardware add-on necessary. It can be run on any smartphone or tablet computer that has a front-facing camera. Umoove claims that even a low-quality camera on an old device will run their software flawlessly. == Umoove Experience == In January 2014 Umoove released its first game onto the app store. The Umoove Experience game lets users control where they are 'flying' in the game through simple gestures and motions with their head. The avatar will basically go toward wherever the user looks. The game was created to showcase the technology for game developers but that did not stop some from criticizing its simplicity. Umoove also announced that they raised another one million dollars and that they are opening offices in Silicon Valley, California. In February 2014, Umoove announced that their face-tracking software development kit is available for Android developers as well as iOS. == Reviews == The Umoove Experience garnered mostly positive reviews from bloggers and mainstream media with some predicting that it could be the future of mobile gaming. Mashable wrote that Umoove's technology could be the emergence of gesture recognition technology in the mobile space, similar to Kinect with console gaming and what Leap Motion has done with desktop computers. Some, however, remain skeptical. CNET, for example, did not give the game a positive review and called the eye tracking technology 'freaky but cool'. They also noted that pioneering technologies have been known to fall short of expectations, citing Apple Inc’s Siri as an example. The technology blog GigaOM said that the Umoove Experience is ’awesome’ and technology evangelist Robert Scoble has called Umoove "brilliant". == uHealth == In January 2015, Umoove released uHealth, a mobile application that uses eye tracking game-like exercise to challenge the user's ability to be attentive, continuously focus, follow commands and avoid distractions. The app is designed in the form of two games, one to improve attention and another that hones focus. uHealth is a training tool, not a diagnostic. Umoove has stated that they want to use their technology for diagnosing neurological disorders but this will depend on clinical tests and FDA approval. The company cites the direct relationship between eye movements and brain activity as well as various vision-based therapies have been backed by many scientific studies conducted over the past decades. uHealth is the first time this type of therapy is delivered right to the end user through a simple download. == Collaboration rumors == In March 2013 there were rumors on the internet that Umoove would be the functioning software embedded into the Samsung Galaxy S4, which was due to launch that month. This rumor was perpetrated by, among others, New York Times, Techcrunch and Yahoo. Once Samsung launched without the Umoove technology rumors about a potential collaboration with Apple Inc hit the web. It has been said that due to the fact that Apple Inc is losing market share and stock value to Samsung they will be more aggressive and eye tracking is a logical place to make that move.
Security awareness
Security awareness is the knowledge and attitude members of an organization possess regarding the protection of the physical, and especially informational, assets of that organization. However, it is very tricky to implement because organizations are not able to impose such awareness directly on employees as there are no ways to explicitly monitor people's behavior. That being said, the literature does suggest several ways that such security awareness could be improved. Many organizations require formal security awareness training for all workers when they join the organization and periodically thereafter, usually annually. Another main force that is found to have a strong correlation with employees' security awareness is managerial security participation. It also bridges security awareness with other organizational aspects. == Relationship between Security Awareness and Human Factors == Employees' behavior, cognitive biases, and decision-making processes influence the effectiveness of security measures. Research indicates that psychological factors, such as optimism bias, overconfidence, and habitual behaviors, can undermine security awareness initiatives. To address these challenges, organizations are increasingly using behavioral analytics and security nudges—subtle prompts like password reminders and phishing warnings—to encourage secure behavior. Human error remains the leading cause of cybersecurity incidents. A 2023 IBM Security report found that 95% of breaches are due to human mistakes, including falling for phishing emails, using weak passwords, and mishandling sensitive data. Organizations emphasize security awareness training as a key strategy to mitigate this risk. It is particularly important for leadership to foster a culture of cybersecurity and to provide targeted training to increase security awareness among all employees across the organization. == Coverage == Topics covered in security awareness training include: The nature of sensitive material and physical assets they may come in contact with, such as trade secrets, privacy concerns and government classified information Employee and contractor responsibilities in handling sensitive information, including review of employee nondisclosure agreements Requirements for proper handling of sensitive material in physical form, including marking, transmission, storage and destruction Proper methods for protecting sensitive information on computer systems, including password policy and use of two-factor authentication Other computer security concerns, including malware, phishing, social engineering, etc. Workplace security, including building access, wearing of security badges, reporting of Incidents, forbidden articles, etc. Consequences of failure to properly protect information, including potential loss of employment, economic consequences to the firm, damage to individuals whose private records are divulged, and possible civil and criminal penalties Security awareness means understanding that there is the potential for some people to deliberately or accidentally steal, damage, or misuse the data that is stored within a company's computer systems and throughout its organization. Therefore, it would be prudent to support the assets of the institution (information, physical, and personal) by trying to stop that from happening. According to the European Network and Information Security Agency, "Awareness of the risks and available safeguards is the first line of defence for the security of information systems and networks." "The focus of Security Awareness consultancy should be to achieve a long term shift in the attitude of employees towards security, whilst promoting a cultural and behavioural change within an organisation. Security policies should be viewed as key enablers for the organisation, not as a series of rules restricting the efficient working of your business." == Role of Gamification and Interactive Training == Modern security awareness programs increasingly utilize gamification, phishing simulations, and interactive learning modules. Studies have shown that engaging employees through serious games, reward systems, and real-world attack simulations improves retention and application of security practices. One example is phishing simulation training, where employees receive simulated phishing emails to test their ability to recognize threats. Research indicates that repeated exposure to such exercises leads to long-term improvements in security awareness. == Legislation and Compliance Requirements == Many industries mandate security awareness training to comply with regulations such as: General Data Protection Regulation (GDPR) – requires organizations to ensure data protection awareness among employees. Health Insurance Portability and Accountability Act (HIPAA) – mandates security awareness programs for healthcare providers. Payment Card Industry Data Security Standard (PCI-DSS) – enforces security training for businesses handling payment card information. == Measuring security awareness == In a 2016 study, researchers developed a method of measuring security awareness. Specifically they measured "understanding about circumventing security protocols, disrupting the intended functions of systems or collecting valuable information, and not getting caught" (p. 38). The researchers created a method that could distinguish between experts and novices by having people organize different security scenarios into groups. Experts will organize these scenarios based on centralized security themes where novices will organize the scenarios based on superficial themes. Security awareness is also assessed through real-time security metrics, such as tracking phishing click rates, password reuse tendencies, and policy adherence rates. Organizations are adopting continuous monitoring strategies to provide immediate feedback to employees about risky behavior and suggest corrective actions. == Evolving cyber threats and security awareness strategies == As cyber threats continue to evolve, security awareness programs must adapt to new attack vectors, such as AI-driven cyberattacks, deepfakes, and insider threats. ENISA's Threat Landscape report highlights the increasing prominence of these emerging threats, stressing the need for security measures that address both traditional attacks like ransomware and malware, as well as more sophisticated techniques such as Living Off Trusted Sites (LOTS) and advanced evasion methods used by cybercriminals.