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  No Thanks (app)

  No Thanks is a Palestinian boycott-awareness mobile application developed by Palestinian software engineer Ahmed Bashbash, created to assist consumers in identifying and boycotting products associated with companies linked to Israel. Launched in 13 November 2023, the app gained significant attention amid the Gaza–Israel conflict. == History == No Thanks is a mobile application developed by Ahmed Bashbash, a Palestinian software engineer from Gaza residing in Hungary. The app was conceived in October 2023 following the death of Bashbash's brother in an Israeli airstrike on October 31, 2023. His sister had previously died in 2020 due to delayed medical treatment. The app was officially launched on November 13, 2023, and quickly gained traction, got over 100,000 downloads within its first month of release. On November 30, 2023, Google removed the app from its Play Store due to a violation of its content policies. The app's home page included a description: "Welcome to No Thanks, here you can see if the product in your hand supports killing children in Palestine or not," which was deemed to contravene Google's guidelines on hate speech and sensitive content. On December 3, 2023, following changes to the app's description, Google reinstated the app.

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  Sanchar Saathi

  Sanchar Saathi (lit. 'Communication Partner' or 'Communication Companion') is an Indian state-owned app and web portal, operated by the Department of Telecommunications, designed to assist Indian mobile users in tracking and blocking stolen or lost mobile devices. In late 2025, a government order requiring Sanchar Saathi to be pre-installed on all mobile devices sold nationwide, with explicit provisions on preventing users from deleting the app or disabling any of its broad functionalities, triggered widespread backlash. The order was subsequently withdrawn. == Background == The Telecommunications Act 2023 introduced an exceptionally broad definition of the term "telecommunications" and conferred wide-ranging powers on the government. Although the Department of Telecommunications (DoT) assured reporters that this definition would not be used to justify government overreach, a November 2024 amendment to the Telecom Cyber Security Rules expanded it further and introduced the concept of the Telecommunication Identifier User Entity (TIEU), enabling users to be personally identified through their phone numbers. Sanchar Saathi was launched amid a widespread rise in cybercrime and hacking, as part of the Indian government's effort to prevent stolen phones from being used for fraud and to promote a state-backed application. In an official statement, the DoT said, "India has big second-hand mobile device market. Cases have also been observed where stolen or blacklisted devices are being re-sold. It makes the purchaser abettor in crime and causes financial loss to them." == Launch == Sanchar Saathi was originally launched as a web portal in May 2023. It was later launched as a mobile app in January 2025. Describing itself as a "citizen-centric" safety tool, Sanchar Saathi allows users to check a device's IMEI, report and block lost or stolen phones, and flag suspected fraud communications. Under Sanchar Saathi's privacy policy, it can make and manage phone calls, view and send messages, read call logs, access photos and files, access the location and camera of the device in which the app is used, as well as read and write into the device's storage. According to official government data, by December 2025, the Sanchar Saathi app had helped recover more than 700,000 lost and stolen mobile devices across India. Users report around 2,000 fraud incidents through the app each day. == Pre-installation controversy == On 28 November 2025, the Bharatiya Janata Party government, led by prime minister Narendra Modi, privately ordered phone manufacturers, including Apple, Samsung, Xiaomi, Vivo, Oppo, among others, to pre-install the Sanchar Saathi app on new devices sold in the country, alongside mandating that old devices get issued a software update for the installation of the app. The order had a 90-day deadline and further included explicit provisions to ensure that the app is to be "readily visible and accessible to the end users at the time of first use or device setup" and that users should neither be able to delete the app nor disable or restrict any of its broad functionalities. The order caused widespread political backlash. K. C. Venugopal, a general secretary of the main opposition party, the Indian National Congress (or simply the Congress), called the order "beyond unconstitutional" and said, "A pre-loaded government app that cannot be uninstalled is a dystopian tool to monitor every Indian. It is a means to watch over every movement, interaction and decision of each citizen", adding, "Big Brother cannot watch us." Another Congress general secretary, Priyanka Gandhi, termed Sanchar Saathi a "snooping app", and attacked the government for "turning this country into a dictatorship". Uddhav Thackeray, former chief minister of Maharashtra, compared Sanchar Saathi to the Pegasus spyware. Sanjay Hegde, a senior advocate at the Supreme Court of India, said "Here in the garb of security, the intrusion is vast, unfettered, unguided and is totally disproportionate. The app ought to be struck down on that account". The Internet Freedom Foundation (IFF), an Indian digital rights advocacy organisation, said, "Forcing every smartphone to carry a permanent government app for a simple verification task is excessive and violates the Puttaswamy proportionality standard", referring to Puttaswamy v. Union of India, a 2017 landmark decision of the Supreme Court, which asserted that the right to privacy should be protected as a fundamental right. The IFF further said, "For this to work in practice, the app will almost certainly need system level or root level access, similar to carrier or OEM system apps, so that it cannot be disabled. That design choice erodes the protections that normally prevent one app from peering into the data of others, and turns Sanchar Saathi into a permanent, non-consensual point of access sitting inside the operating system of every Indian smartphone user." Moreover, the organisation said that while the app was being "framed as a benign IMEI checker", a server-side update could allow the app to engage in "client side scanning for 'banned' applications, flag VPN usage, correlate SIM activity, or trawl SMS logs in the name of fraud detection. Nothing in the order constrains these possibilities." In reaction to the controversy, Jyotiraditya Scindia, the union minister of communications, said, "There is no snooping or call monitoring", adding, "Obviously you can delete it. There is no problem. This is a matter of customer protection. It is not mandatory. If you don't want to register, and don't want to use the app, don't use it; don't register, and it will lay dormant." Scindia compared the app to other pre-installed mobile apps such as Google Maps, which he said could be deleted if users wished so. However, contrary to Scindia's statement, on many phone brands, such pre-installed apps cannot be deleted, although users can disable them. Furthermore, upon enquiry, Scindia did not clarify whether his remarks applied to the app after the order took effect, making no comment on the provision in the order that would prevent users from deleting the app. When Congress member Renuka Chowdhury submitted an adjournment motion notice in the Rajya Sabha seeking the suspension of all other matters to discuss the Sanchar Saathi issue, Kiren Rijiju, the union minister of parliamentary affairs, accused the opposition of "manufacturing issues" to stall session proceedings. By 2 December, it had been reported that Apple did not plan to comply with the order, citing privacy and security concerns for the iOS ecosystem and the fact that the order would violate its internal policy against the pre-installation of third-party software in iPhones. Although it was clarified that Apple did not intend to take the matter to court or publicly oppose the government, it was said that Apple "can't do this. Period." The order would have also required Google to create a custom version of Android solely for India which would include the Sanchar Saathi app, a requirement described to "not be acceptable to the company". Following the backlash, the order was revoked on 3 December 2025. In a press release, the government said, "Given Sanchar Saathi's increasing acceptance, Government has decided not to make the pre-installation mandatory for mobile manufacturers".

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  Reflection lines

  Engineers use reflection lines to judge a surface's quality. Reflection lines reveal surface flaws, particularly discontinuities in normals indicating that the surface is not C 2 {\displaystyle C^{2}} . Reflection lines may be created and examined on physical surfaces or virtual surfaces with the help of computer graphics. For example, the shiny surface of an automobile body is illuminated with reflection lines by surrounding the car with parallel light sources. Virtually, a surface can be rendered with reflection lines by modulating the surfaces point-wise color according to a simple calculation involving the surface normal, viewing direction and a square wave environment map. == Mathematical definition == Consider a point p {\displaystyle p} on a surface M {\displaystyle M} with (normalized) normal n {\displaystyle n} . If an observer views this point from infinity at view direction v {\displaystyle v} then the reflected view direction r {\displaystyle r} is: r = v − 2 ( n ⋅ v ) n . {\displaystyle r=v-2(n\cdot v)n.} (The vector v {\displaystyle v} is decomposed into its normal part v n = ( n ⋅ v ) v {\displaystyle v_{n}=(n\cdot v)v} and tangential part v t = v − v n {\displaystyle v_{t}=v-v_{n}} . Upon reflection, the tangential part is kept and the normal part is negated.) For reflection lines we consider the surface M {\displaystyle M} surrounded by parallel lines with direction a {\displaystyle a} , representing infinite, non-dispersive light sources. For each point p {\displaystyle p} on M {\displaystyle M} we determine which line is seen from direction v {\displaystyle v} . The position on each line is of no interest. Define the vector r p {\displaystyle r_{p}} to be the reflection direction r {\displaystyle r} projected onto a plane P {\displaystyle P} that is orthogonal to a {\displaystyle a} : r p = r − ( r ⋅ a ) a {\displaystyle r_{p}=r-(r\cdot a)a} and similarly let v p {\displaystyle v_{p}} be the viewing direction projected onto P {\displaystyle P} : v p = v − ( v ⋅ a ) a {\displaystyle v_{p}=v-(v\cdot a)a} Finally, define v o {\displaystyle v_{o}} to be the direction lying in P {\displaystyle P} perpendicular to a {\displaystyle a} and v p {\displaystyle v_{p}} : v o = a × v p {\displaystyle v_{o}=a\times v_{p}} Using these vectors, the reflection line function θ ( p ) : M → ( − π , π ] {\displaystyle \theta (p):M\rightarrow (-\pi ,\pi ]} is a scalar function mapping points p {\displaystyle p} on the surface to angles between v p {\displaystyle v_{p}} and r p {\displaystyle r_{p}} : θ = arctan ⁡ ( r p ⋅ v o , r p ⋅ v p ) {\displaystyle \theta =\arctan {(r_{p}\cdot v_{o},r_{p}\cdot v_{p})}} where a r c t a n ( y , x ) {\displaystyle arctan(y,x)} is the atan2 function producing a number in the range ( − π , π ] {\displaystyle (-\pi ,\pi ]} . ( v p {\displaystyle v_{p}} and v o {\displaystyle v_{o}} can be viewed as a local coordinate system in P {\displaystyle P} with x {\displaystyle x} -axis in direction v p {\displaystyle v_{p}} and y {\displaystyle y} -axis in direction v o {\displaystyle v_{o}} .) Finally, to render the reflection lines positive values θ > 0 {\displaystyle \theta >0} are mapped to a light color and non-positive values to a dark color. == Highlight lines == Highlight lines are a view-independent alternative to reflection lines. Here the projected normal is directly compared against some arbitrary vector x {\displaystyle x} perpendicular to the light source: θ = arctan ⁡ ( n a ⋅ a ⊥ , n a ⋅ x ) {\displaystyle \theta =\arctan {(n_{a}\cdot a^{\perp },n_{a}\cdot x)}} where n a {\displaystyle n_{a}} is the surface normal projected on the light source plane P {\displaystyle P} : n a ^ / | n a ^ | , n a ^ = n − ( n ⋅ a ) a {\displaystyle {\hat {n_{a}}}/|{\hat {n_{a}}}|,{\hat {n_{a}}}=n-(n\cdot a)a} The relationship between reflection lines and highlight lines is likened to that between specular and diffuse shading.

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  Data administration

  Data administration or data resource management is an organizational function working in the areas of information systems and computer science that plans, organizes, describes and controls data resources. Data resources are usually stored in databases under a database management system or other software such as electronic spreadsheets. In many smaller organizations, data administration is performed occasionally, or is a small component of the database administrator’s work. In the context of information systems development, data administration ideally begins at system conception, ensuring there is a data dictionary to help maintain consistency, avoid redundancy, and model the database so as to make it logical and usable, by means of data modeling, including database normalization techniques. == Data resource management == According to the Data Management Association (DAMA), data resource management is "the development and execution of architectures, policies, practices and procedures that properly manage the full data lifecycle needs of an enterprise". Data Resource management may be thought of as a managerial activity that applies information system and other data management tools to the task of managing an organization’s data resource to meet a company’s business needs, and the information they provide to their shareholders. From the perspective of database design, it refers to the development and maintenance of data models to facilitate data sharing between different systems, particularly in a corporate context. Data Resource Management is also concerned with both data quality and compatibility between data models. Since the beginning of the information age, businesses need all types of data on their business activity. With each data created, when a business transaction is made, need data is created. With these data, new direction is needed that focuses on managing data as a critical resource of the organization to directly support its business activities. The data resource must be managed with the same intensity and formality that other critical resources are managed. Organizations must emphasize the information aspect of information technology, determine the data needed to support the business, and then use appropriate technology to build and maintain a high-quality data resource that provides that support. Data resource quality is a measure of how well the organization's data resource supports the current and the future business information demand of the organization. The data resource cannot support just the current business information demand while sacrificing the future business information demand. It must support both the current and the future business information demand. The ultimate data resource quality is stability across changing business needs and changing technology. A corporate data resource must be developed within single, organization-wide common data architecture. A data architecture is the science and method of designing and constructing a data resource that is business driven, based on real-world objects and events as perceived by the organization, and implemented into appropriate operating environments. It is the overall structure of a data resource that provides a consistent foundation across organizational boundaries to provide easily identifiable, readily available, high-quality data to support the business information demand. The common data architecture is a formal, comprehensive data architecture that provides a common context within which all data at an organization's disposal are understood and integrated. It is subject oriented, meaning that it is built from data subjects that represent business objects and business events in the real world that are of interest to the organization and about which data are captured and maintained.

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  Curve (tonality)

  In image editing, a curve is a remapping of image tonality, specified as a function from input level to output level, used as a way to emphasize colours or other elements in a picture. Curves can usually be applied to all channels together in an image, or to each channel individually. Applying a curve to all channels typically changes the brightness in part of the spectrum. Light parts of a picture can be easily made lighter and dark parts darker to increase contrast. Applying a curve to individual channels can be used to stress a colour. This is particularly efficient in the Lab colour space due to the separation of luminance and chromaticity, but it can also be used in RGB, CMYK or whatever other colour models the software supports.

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  Wargame (hacking)

  In hacking, a wargame (or war game) is a cyber-security challenge and mind sport in which the competitors must exploit or defend a vulnerability in a system or application, and/or gain or prevent access to a computer system. A wargame usually involves a capture the flag logic, based on pentesting, semantic URL attacks, knowledge-based authentication, password cracking, reverse engineering of software (often JavaScript, C and assembly language), code injection, SQL injections, cross-site scripting, exploits, IP address spoofing, forensics, and other hacking techniques. == Wargames for preparedness == Wargames are also used as a method of cyberwarfare preparedness. The NATO Cooperative Cyber Defence Centre of Excellence (CCDCOE) organizes an annual event, Locked Shields, which is an international live-fire cyber exercise. The exercise challenges cyber security experts through real-time attacks in fictional scenarios and is used to develop skills in national IT defense strategies. == Additional applications == Wargames can be used to teach the basics of web attacks and web security, giving participants a better understanding of how attackers exploit security vulnerabilities. Wargames are also used as a way to "stress test" an organization's response plan and serve as a drill to identify gaps in cyber disaster preparedness.

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  TeaOnHer

  TeaOnHer is a male-oriented dating surveillance mobile app that allows men to anonymously rate and comment on women they are dating. It was set up in response to the existence of Tea, a female-oriented dating app that allowed women to rate and comment on men. In 2025, Cosmopolitian magazine described it as America's second most popular mobile app, with it being the second most popular app in the lifestyle section of Apple's App Store. The TeaOnHer app has fewer features than the rival Tea app, focusing instead on anonymous commenting. It is listed as having been developed by a company called Newville Media Corporation. TechCrunch reported in 2025 that TeaOnHer had leaked credentials of some of its users.

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  Resilience week

  Resilience week is an annual symposium established to enable cross-disciplinary and role based discussions to advance strategies and research that engenders resilience in critical infrastructure systems and communities. Damaging storms, cyber attack and the interconnection of critical infrastructure systems can lead to cascading events that not only affect local but also across regions. However, many of these interdependencies are not easily recognized and obscure and complicate the mitigation of risk. The purpose of the symposia series is hence to facilitate best practice in managing critical infrastructure risks, by bringing together businesses, government and researchers. == Background == Originally organized in 2008 as a focus on the new research area of resilient control systems, including the disciplinary areas of control system, cyber-security, cognitive psychology and any number of critical infrastructure domains. Resilience has long been recognized as an area that requires not only the contributions of multiple disciplines or multidisciplinary participation, but interdisciplinary interaction where there is a common language and familiarity of the contributors to what other disciplines (and roles) contribute. The resulting interactions developed by Resilience Week and associated activities are intended to culture this sharing environment as a safe zone for inclusion; more importantly, an environment that lends to developing the new science and practice. As the attributes of resilience are complex, the contributions and topics for the event have included both the disciplinary and the project considerations, in keynotes, panels and research presentations. Keynotes have included senior leadership in the Department of Energy, Department of Defense, Department of Homeland Security, the National Science Foundation, and other agencies in addition to National Academy and professional organization fellows and senior industry leaders. Project panels and research presentations include emergent topics in resilience to climate change, cyber attack, damaging storms and the energy assurance. Topics Areas of focus have included: Control Systems Cyber Systems Cognitive Systems Communications Systems Communities and Infrastructure Project Focus Areas have included: Dependencies and Interdependencies Cyber Resilience for Operating Technology Commercializing Research and Development Building Critical Infrastructure Resilience through Distributed Energy Resources Energy Equity and Community Resilience Proceedings are developed for each year of the event, documenting the diversity of the research and engagements within these topical areas. == Impacts for the future == Since its inception, the Resilience Week community has evolved from one that primarily included only university researchers to one that includes many government laboratories, universities and private industries in the US and internationally. This type of collaboration forms a feedback loop that informs the research with the current needs and hones best practices. The future of the event is to further advance discussions that advance investment, recognize priorities and expedite technologies and tools to proactively address our energy future, in light of the natural and manmade challenges, and rationalizing the complex relationships that exist in critical infrastructure.

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  TIMIT

  TIMIT is a corpus of phonemically and lexically transcribed speech of American English speakers of different sexes and dialects. Each transcribed element has been delineated in time. TIMIT was designed to further acoustic-phonetic knowledge and automatic speech recognition systems. It was commissioned by DARPA and corpus design was a joint effort between the Massachusetts Institute of Technology, SRI International, and Texas Instruments (TI). The speech was recorded at TI, transcribed at MIT, and verified and prepared for publishing by the National Institute of Standards and Technology (NIST). There is also a telephone bandwidth version called NTIMIT (Network TIMIT). TIMIT and NTIMIT are not freely available — either membership of the Linguistic Data Consortium, or a monetary payment, is required for access to the dataset. == Data == TIMIT contains ~5 hours of speech, of 10 sentences spoken by each of 630 speakers. The sentences were randomly sampled from a corpus of 2342 sentences. The speakers were native speakers of American English, classified under 8 major dialect regions: New England, Northern, North Midland, South Midland, Southern, New York City, Western, Army Brat (moved around). The speakers were 70% male and 30% female. Recordings were made in a noise-isolated recording booth at Texas Instrument, using a semi-automatic computer system (STEROIDS) to control the presentation of prompts to the speaker and the recording. Two-channel recordings were made using a Sennheiser HMD 414 headset-mounted microphone and a Brüel & Kjær 1/2" far-field pressure microphone (#4165). The speech was digitized at a sample rate of 20 kHz then and downsampled to 16 kHz. == History == The TIMIT telephone corpus was an early attempt to create a database with speech samples. It was published in the year 1988 on CD-ROM and consists of only 10 sentences per speaker. Two 'dialect' sentences were read by each speaker, as well as another 8 sentences selected from a larger set Each sentence averages 3 seconds long and is spoken by 630 different speakers. It was the first notable attempt in creating and distributing a speech corpus and the overall project has produced costs of 1.5 million US$. An update was released in October 1990. It included full 630-speaker corpus; checked and corrected transcriptions; word-alignment transcriptions; NIST SPHERE-headered waveform files and header manipulation software; phonemic dictionary; new test and training subsets balanced for dialectal and phonetic coverage; more extensive documentation. The full name of the project is DARPA-TIMIT Acoustic-Phonetic Continuous Speech Corpus and the acronym TIMIT stands for Texas Instruments/Massachusetts Institute of Technology. The main reason why a corpus of telephone speech was created was to train speech recognition software. In the Blizzard challenge, different software has the obligation to convert audio recordings into textual data and the TIMIT corpus was used as a standardized baseline.

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  Nice (app)

  Nice is a photo-sharing mobile app developed by Nice App Mobile Technology Co., Ltd. (Chinese: 北京极赞科技有限公司) in China. The app allows users to tag specific locations on images, enabling detailed labeling of items such as clothing and accessories. The company received a $36 million investment in C-round funding in 2014. Nice had 30 million registered users and 12 million active users as of late 2015. As of January 2024, it remained a popular app, the 6th most-downloaded in the iOS App Store for China. == Official website == Official website

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  Genigraphics

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

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  Sprite (computer graphics)

  In computer graphics, a sprite is a two-dimensional bitmap that is integrated into a larger scene, most often in a 2D video game. Originally, the term sprite referred to fixed-sized objects composited together, by hardware, with a background. Use of the term has since become more general. Systems with hardware sprites include arcade video games of the 1970s and 1980s; game consoles including as the Atari VCS (1977), ColecoVision (1982), Famicom (1983), Genesis/Mega Drive (1988); and home computers such as the TI-99/4 (1979), Atari 8-bit computers (1979), Commodore 64 (1982), MSX (1983), Amiga (1985), and X68000 (1987). Hardware varies in the number of sprites supported, the size and colors of each sprite, and special effects such as scaling or reporting pixel-precise overlap. Hardware composition of sprites occurs as each scan line is prepared for the video output device, such as a cathode-ray tube, without involvement of the main CPU and without the need for a full-screen frame buffer. Sprites can be positioned or altered by setting attributes used during the hardware composition process. The number of sprites which can be displayed per scan line is often lower than the total number of sprites a system supports. For example, the Texas Instruments TMS9918 chip supports 32 sprites, but only four can appear on the same scan line. The CPUs in modern computers, video game consoles, and mobile devices are fast enough that bitmaps can be drawn into a frame buffer without special hardware assistance. Beyond that, GPUs can render vast numbers of scaled, rotated, anti-aliased, partially translucent, very high resolution images in parallel with the CPU. == Etymology == According to Karl Guttag, one of two engineers for the 1979 Texas Instruments TMS9918 video display processor, this use of the word sprite came from David Ackley, a manager at TI. It was also used by Danny Hillis at Texas Instruments in the late 1970s. The term was derived from the fact that sprites "float" on top of the background image without overwriting it, much like a ghost or mythological sprite. Some hardware manufacturers used different terms, especially before sprite became common: Player/Missile Graphics was a term used by Atari, Inc. for hardware sprites in the Atari 8-bit computers (1979) and Atari 5200 console (1982). The term reflects the use for both characters ("players") and smaller associated objects ("missiles") that share the same color. The earlier Atari Video Computer System and some Atari arcade games used player, missile, and ball. Stamp was used in some arcade hardware in the early 1980s, including Ms. Pac-Man. Movable Object Block, or MOB, was used in MOS Technology's graphics chip literature. Commodore, the main user of MOS chips and the owner of MOS for most of the chip maker's lifetime, instead used the term sprite for the Commodore 64. OBJs (short for objects) is used in the developer manuals for the NES, Super NES, and Game Boy. The region of video RAM used to store sprite attributes and coordinates is called OAM (Object Attribute Memory). This also applies to the Game Boy Advance and Nintendo DS. == History == === Arcade video games === The use of sprites originated with arcade video games. Nolan Bushnell came up with the original concept when he developed the first arcade video game, Computer Space (1971). Technical limitations made it difficult to adapt the early mainframe game Spacewar! (1962), which performed an entire screen refresh for every little movement, so he came up with a solution to the problem: controlling each individual game element with a dedicated transistor. The rockets were essentially hardwired bitmaps that moved around the screen independently of the background, an important innovation for producing screen images more efficiently and providing the basis for sprite graphics. The earliest video games to represent player characters as human player sprites were arcade sports video games, beginning with Taito's TV Basketball, released in April 1974 and licensed to Midway Manufacturing for release in North America. Designed by Tomohiro Nishikado, he wanted to move beyond simple Pong-style rectangles to character graphics, by rearranging the rectangle shapes into objects that look like basketball players and basketball hoops. Ramtek released another sports video game in October 1974, Baseball, which similarly displayed human-like characters. The Namco Galaxian arcade system board, for the 1979 arcade game Galaxian, displays animated, multi-colored sprites over a scrolling background. It became the basis for Nintendo's Radar Scope and Donkey Kong arcade hardware and home consoles such as the Nintendo Entertainment System. According to Steve Golson from General Computer Corporation, the term "stamp" was used instead of "sprite" at the time. === Home systems === Signetics devised the first chips capable of generating sprite graphics (referred to as objects by Signetics) for home systems. The Signetics 2636 video processors were first used in the 1978 1292 Advanced Programmable Video System and later in the 1979 Elektor TV Games Computer. The Atari VCS, released in 1977, has a hardware sprite implementation where five graphical objects can be moved independently of the game playfield. The term sprite was not in use at the time. The VCS's sprites are called movable objects in the programming manual, further identified as two players, two missiles, and one ball. These each consist of a single row of pixels that are displayed on a scan line. To produce a two-dimensional shape, the sprite's single-row bitmap is altered by software from one scan line to the next. The 1979 Atari 400 and 800 home computers have similar, but more elaborate, circuitry capable of moving eight single-color objects per scan line: four 8-bit wide players and four 2-bit wide missiles. Each is the full height of the display—a long, thin strip. DMA from a table in memory automatically sets the graphics pattern registers for each scan line. Hardware registers control the horizontal position of each player and missile. Vertical motion is achieved by moving the bitmap data within a player or missile's strip. The feature was called player/missile graphics by Atari. Texas Instruments developed the TMS9918 chip with sprite support for its 1979 TI-99/4 home computer. An updated version is used in the 1981 TI-99/4A. === In 2.5D and 3D games === Sprites remained popular with the rise of 2.5D games (those which recreate a 3D game space from a 2D map) in the late 1980s and early 1990s. A technique called billboarding allows 2.5D games to keep onscreen sprites rotated toward the player view at all times. Some 2.5D games, such as 1993's Doom, allow the same entity to be represented by different sprites depending on its rotation relative to the viewer, furthering the illusion of 3D. Fully 3D games usually present world objects as 3D models, but sprites are supported in some 3D game engines, such as GoldSrc and Unreal, and may be billboarded or locked to fixed orientations. Sprites remain useful for small details, particle effects, and other applications where the lack of a third dimension is not a major detriment. == Systems with hardware sprites == These are base hardware specs and do not include additional programming techniques, such as using raster interrupts to repurpose sprites mid-frame.

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  Hierarchical navigable small world

  Hierarchical navigable small world (HNSW) is an algorithm for approximate nearest neighbor search. It is used to find items that are similar to a query item in a large collection, without comparing the query with every item one by one. The algorithm is commonly used for searching vector data. In these systems, an item such as a document, image, song, or user profile is represented by a list of numbers called a vector. Items with similar vectors are treated as similar according to the model that produced the vectors. HNSW provides a way to search these vectors quickly, especially in large datasets. HNSW stores vectors in a graph. Each vector is a node, and links connect it to some nearby vectors. The graph has several layers: upper layers contain fewer nodes and act like a rough map, while the bottom layer contains all nodes and gives a more detailed view. A search starts in an upper layer, follows links toward nodes that are closer to the query, and then repeats the process in lower layers until it finds a set of likely nearest neighbors. == Background == The nearest neighbor search problem asks which items in a dataset are closest to a query item. A direct search can compare the query with every item in the dataset, but this becomes slow when the dataset is large. Exact search methods based on spatial trees, such as the k-d tree and R-tree, can also become less effective for high-dimensional data, a problem often associated with the curse of dimensionality. Approximate nearest neighbor methods trade some exactness for speed or lower resource use. Instead of always guaranteeing the exact closest item, they try to return close items quickly. Other approximate methods include locality-sensitive hashing and product quantization. HNSW builds on research into small-world networks and navigable graphs. In a small-world graph, most nodes can be reached from other nodes through a short chain of links. In a navigable graph, a search procedure can use local information to move toward a target. Jon Kleinberg's work on navigation in small-world networks is an important example of this research area. Later work studied ways to add links that make graphs easier to navigate greedily. The HNSW algorithm extends earlier navigable small world methods for similarity search by adding a hierarchy of graph layers. This hierarchy helps the algorithm find a good region of the graph before doing a more detailed search in the bottom layer. == Algorithm == HNSW is based on a proximity graph. In this graph, nearby vectors are connected by edges. The algorithm uses these edges to move through the dataset, rather than scanning every vector. The graph is hierarchical. Every vector appears in the bottom layer. Some vectors are also placed in higher layers, with fewer vectors appearing as the layers go upward. The upper layers allow long-range movement across the dataset, while the lower layers allow a more detailed search near promising candidates. A typical search proceeds as follows: The search begins from an entry point in the highest layer. At each step, the algorithm looks at neighboring nodes and moves to a neighbor that is closer to the query. When it cannot find a closer neighbor in that layer, it moves down to the next layer. In the bottom layer, it explores a wider set of candidate nodes and returns the nearest candidates found. This search strategy is often described as greedy navigation. The algorithm repeatedly chooses locally better nodes, using the graph structure to approach the query point. == Construction and parameters == The HNSW graph is built incrementally. When a new vector is inserted, the algorithm assigns it a maximum layer, searches for nearby existing nodes, and connects the new node to selected neighbors in each layer where it appears. Implementations usually expose parameters that control the trade-off between speed, accuracy, memory use, and construction time. A higher number of graph connections can improve recall but requires more memory. A larger search candidate list can improve accuracy but makes queries slower. A larger construction candidate list can improve the quality of the graph but makes index building slower. Because HNSW is approximate, its results are not always identical to a full exact search. Its practical performance depends on the dataset, distance measure, implementation, and parameter settings. Benchmarking studies have found HNSW-based libraries to be strong performers among approximate nearest neighbor methods, although worst-case performance can differ from performance on common benchmark datasets. == Use in vector search systems == HNSW is used as an index in systems that store and search high-dimensional vectors. These systems include vector databases, search engines, and database extensions. Typical uses include semantic search, recommender systems, image similarity search, and retrieval-augmented generation. Several software projects implement or support HNSW. Libraries include hnswlib, which is associated with the original HNSW authors, and FAISS. Database and search systems that document HNSW support include Apache Lucene, Chroma, ClickHouse, DuckDB, MariaDB, Milvus, pgvector, Qdrant, and Redis.

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  Tea (app)

  Tea, officially Tea Dating Advice, is a dating surveillance mobile phone application that allows women to post personal data about men they are interested in or are currently dating. Founded by Sean Cook, the app rose to prominence in July 2025 after it was the subject of three major data leaks in July and August 2025. It was removed from Apple's App Store in October 2025, but remains available on the Google Play Store. == History == The app enables its users to upload, view, and comment on photos of men, check men's public records, and perform image searches. It also provides the ability to rate and review men, as well as a group chat function. The app uses artificial intelligence to verify that the user is a woman through facial analysis and other personal information to preserve the app as a women-only space. Users are required to submit their photo and an ID to access the app. The company that created the app was founded by businessman and tech capitalist Sean Cook, who stated in July 2025 that he was inspired to create the app because of his mother's experiences from online dating. According to the company, users remain anonymous, and the requirement to upload an ID was removed in 2023. An August 2025 investigation by 404 Media suggested that much of the information given by Cook on the historical background of the company was inaccurate. In July 2025, private messages, other personally identifying information, and approximately 72,000 images were leaked via 4chan. A further 1.1 million private messages were subsequently leaked using a separate security vulnerability; these included intimate conversations about controversial topics such as adultery and other forms of infidelity to their partners, discussions of abortion, phone numbers, meeting locations, and other confidential communications. The app's publishers subsequently revoked the ability to private message users in the app. Shortly after, the app was hidden from search on Android and an interactive, unverified map was also created of those in the files. By 7 August 2025, ten class action lawsuits had been filed. A further leak was reported later that month. Proponents have praised the app as an aid for women's safety by helping them check men for adultery, catfishing, criminal convictions and other "red flag" behaviors. Critics have described the app as a doxing tool and a violation of privacy, an opportunity for defamation against innocent individuals, and a witch hunt. Cook has stated that the company's legal team receives about three legal threats per day. Another mobile app, called TeaOnHer, was created in response of the app’s popularity. It was described as the male version of the Tea app. The app also reported a data breach in August 2025. In October 2025, Apple removed the app from their app store, telling journalists that the removal was due to a failure to meet company terms regarding content moderation and user privacy. Apple also mentioned an excessive amount of complaints, including allegations that the personal information of minors was being shared. The app remains on the Google Play Store.

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  Texture filtering

  In computer graphics, texture filtering or texture smoothing is the method used to determine the texture color for a texture mapped pixel, using the colors of nearby texels (ie. pixels of the texture). Filtering describes how a texture is applied at many different shapes, size, angles and scales. Depending on the chosen filter algorithm, the result will show varying degrees of blurriness, detail, spatial aliasing, temporal aliasing and blocking. Depending on the circumstances, filtering can be performed in software (such as a software rendering package) or in hardware, eg. with either real time or GPU accelerated rendering circuits, or in a mixture of both. For most common interactive graphical applications, modern texture filtering is performed by dedicated hardware which optimizes memory access through memory cacheing and pre-fetch, and implements a selection of algorithms available to the user and developer. There are two main categories of texture filtering: magnification filtering and minification filtering. Depending on the situation, texture filtering is either a type of reconstruction filter where sparse data is interpolated to fill gaps (magnification), or a type of anti-aliasing (AA) where texture samples exist at a higher frequency than required for the sample frequency needed for texture fill (minification). There are many methods of texture filtering, which make different trade-offs between computational complexity, memory bandwidth and image quality. == The need for filtering == During the texture mapping process for any arbitrary 3D surface, a texture lookup takes place to find out where on the texture each pixel center falls. For texture-mapped polygonal surfaces composed of triangles typical of most surfaces in 3D games and movies, every pixel (or subordinate pixel sample) of that surface will be associated with some triangle(s) and a set of barycentric coordinates, which are used to provide a position within a texture. Such a position may not lie perfectly on the "pixel grid," necessitating some function to account for these cases. In other words, since the textured surface may be at an arbitrary distance and orientation relative to the viewer, one pixel does not usually correspond directly to one texel. Some form of filtering has to be applied to determine the best color for the pixel. Insufficient or incorrect filtering will show up in the image as artifacts (errors in the image), such as 'blockiness', jaggies, or shimmering. There can be different types of correspondence between a pixel and the texel/texels it represents on the screen. These depend on the position of the textured surface relative to the viewer, and different forms of filtering are needed in each case. Given a square texture mapped on to a square surface in the world, at some viewing distance the size of one screen pixel is exactly the same as one texel. Closer than that, the texels are larger than screen pixels, and need to be scaled up appropriately — a process known as texture magnification. Farther away, each texel is smaller than a pixel, and so one pixel covers multiple texels. In this case an appropriate color has to be picked based on the covered texels, via texture minification. Graphics APIs such as OpenGL allow the programmer to set different choices for minification and magnification filters. Note that even in the case where the pixels and texels are exactly the same size, one pixel will not necessarily match up exactly to one texel. It may be misaligned or rotated, and cover parts of up to four neighboring texels. Hence some form of filtering is still required. == Mipmapping == Mipmapping is a standard technique used to save some of the filtering work needed during texture minification. It is also highly beneficial for cache coherency - without it the memory access pattern during sampling from distant textures will exhibit extremely poor locality, adversely affecting performance even if no filtering is performed. During texture magnification, the number of texels that need to be looked up for any pixel is always four or fewer; during minification, however, as the textured polygon moves farther away potentially the entire texture might fall into a single pixel. This would necessitate reading all of its texels and combining their values to correctly determine the pixel color, a prohibitively expensive operation. Mipmapping avoids this by prefiltering the texture and storing it in smaller sizes down to a single pixel. As the textured surface moves farther away, the texture being applied switches to the prefiltered smaller size. Different sizes of the mipmap are referred to as 'levels', with Level 0 being the largest size (used closest to the viewer), and increasing levels used at increasing distances. == Filtering methods == This section lists the most common texture filtering methods, in increasing order of computational cost and image quality. === Nearest-neighbor interpolation === Nearest-neighbor interpolation is the simplest and crudest filtering method — it simply uses the color of the texel closest to the pixel center for the pixel color. While simple, this results in a large number of artifacts - texture 'blockiness' during magnification, and aliasing and shimmering during minification. This method is fast during magnification but during minification the stride through memory becomes arbitrarily large and it can often be less efficient than MIP-mapping due to the lack of spatially coherent texture access and cache-line reuse. === Nearest-neighbor with mipmapping === This method still uses nearest neighbor interpolation, but adds mipmapping — first the nearest mipmap level is chosen according to distance, then the nearest texel center is sampled to get the pixel color. This reduces the aliasing and shimmering significantly during minification but does not eliminate it entirely. In doing so it improves texture memory access and cache-line reuse through avoiding arbitrarily large access strides through texture memory during rasterization. This does not help with blockiness during magnification as each magnified texel will still appear as a large rectangle. === Linear mipmap filtering === Less commonly used, OpenGL and other APIs support nearest-neighbor sampling from individual mipmaps whilst linearly interpolating the two nearest mipmaps relevant to the sample. === Bilinear filtering === In Bilinear filtering, the four nearest texels to the pixel center are sampled (at the closest mipmap level), and their colors are combined by weighted average according to distance. This removes the 'blockiness' seen during magnification, as there is now a smooth gradient of color change from one texel to the next, instead of an abrupt jump as the pixel center crosses the texel boundary. Bilinear filtering for magnification filtering is common. When used for minification it is often used with mipmapping; though it can be used without, it would suffer the same aliasing and shimmering problems as nearest-neighbor filtering when minified too much. For modest minification ratios, however, it can be used as an inexpensive hardware accelerated weighted texture supersample. The Nintendo 64 used an unusual version of bilinear filtering where only three pixels are used known as 3-point texture filtering, instead of four due to hardware optimization concerns. This introduces a noticeable "triangulation bias" in some textures. === Trilinear filtering === Trilinear filtering is a remedy to a common artifact seen in mipmapped bilinearly filtered images: an abrupt and very noticeable change in quality at boundaries where the renderer switches from one mipmap level to the next. Trilinear filtering solves this by doing a texture lookup and bilinear filtering on the two closest mipmap levels (one higher and one lower quality), and then linearly interpolating the results. This results in a smooth degradation of texture quality as distance from the viewer increases, rather than a series of sudden drops. Of course, closer than Level 0 there is only one mipmap level available, and the algorithm reverts to bilinear filtering. === Anisotropic filtering === Anisotropic filtering is the highest quality filtering available in current consumer 3D graphics cards. Simpler, "isotropic" techniques use only square mipmaps which are then interpolated using bi– or trilinear filtering. (Isotropic means same in all directions, and hence is used to describe a system in which all the maps are squares rather than rectangles or other quadrilaterals.) When a surface is at a high angle relative to the camera, the fill area for a texture will not be approximately square. Consider the common case of a floor in a game: the fill area is far wider than it is tall. In this case, none of the square maps are a good fit. The result is blurriness and/or shimmering, depending on how the fit is chosen. Anisotropic filtering corrects this by sampling the texture as a non-square shape. The goal is

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