The SMART Health Card framework is an open source immunity passport program designed to store and share medical information in paper or digital form. It was initially launched as a vaccine passport during the COVID-19 pandemic, but is envisioned for use for other infectious diseases. SMART Health Cards include a QR code which can be scanned and verified using the official SMART Health Card Verifier mobile app, supported by Apple and Android. It was rolled out by the Vaccination Credential Initiative (VCI) based on technology developed at Boston Children's Hospital, and standards set by Health Level Seven International (HL7) and the World Wide Web Consortium (W3C). It is recognized by the International Organization for Standardization. == History == === Founding === In February 2009, United States president Barack Obama signed an economic stimulus package which included $19 billion in funds for investment in health information technology. The following month, researchers from Boston Children's Hospital and Harvard Medical School, Kenneth Mandl and Isaac Kohane, published an article in The New England Journal of Medicine calling for the modernization of electronic health records through API integrations on mobile devices. In April 2010, the pair secured a $15 million grant through the Office of the National Coordinator for Health Information Technology's Strategic Health IT Advanced Research Projects (SHARP) program. With this federal funding, the researchers began development of an interoperable healthcare IT platform they called "Substitutable Medical Applications and Reusable Technologies" (SMART). The first iteration of the platform API was previewed later that year, and "SMART Classic" was released in 2011. In 2013, SMART adopted the open-source Fast Health Interoperability Resources (FHIR) standard developed by Health Level Seven International (HL7). The newly named SMART on FHIR platform was debuted in February 2014 at the Health Information Management Systems Society conference. === 21st Century Cures Act === According to SMART Health IT, Mandl successfully lobbied for the inclusion of a universal API requirement in the 21st Century Cures Act, signed into law on December 13, 2016. The team also advocated for a federal rule establishing SMART as the universal API. In 2019, the Office of the National Coordinator for Health Information Technology published the "final rule" specifying the SMART framework as the standard to satisfy the requirements of the 21st Century Cures Act; the rule was implemented in June 2020. === COVID-19 === The SMART Health Card framework was deployed as a "de facto standard" for vaccine passports in the COVID-19 pandemic in the United States and other international jurisdictions. On January 14, 2021, the Mitre Corporation announced the launch of a new public–private partnership called the Vaccination Credential Initiative (VCI) alongside the CARIN Alliance, Cerner, Change Healthcare, The Commons Project Foundation, Epic Systems, Evernorth, Mayo Clinic, Microsoft, Oracle, Safe Health, and Salesforce. VCI's purpose was to employ the SMART Health Card framework in order to create a unified proof-of-vaccination system for COVID-19 vaccines.The California Department of Public Health introduced a Digital Covid-19 Vaccine Record portal in June 2021, allowing individuals to verify their vaccination status using the SMART Health Card reader. On August 5, 2021, New York Governor Andrew Cuomo announced the introduction of the "Excelsior Pass Plus" which would expand its Excelsior Pass program into other states and internationally by connecting it to the SMART Health Card system. As of August 27, 2021, 415,000 citizens of Louisiana had added their COVID-19 vaccination status to their state-run, SMART Health Card enabled LA Wallet. On September 8, 2021, Hawaii governor David Ige announced the rollout of the state's Hawaiʻi SMART Health Card. County-level health departments across the United States partnered with VaccineCheck to issue SMART Health Cards by verifying vaccine cards provided by the Centers for Disease Control and Prevention. The Government of Canada spent CAD$4.6 million to develop a proof-of-vaccination credential on the SMART Health Card framework, enabling its ArriveCAN travel application to store, recognize and verify credentials from every province, territory and foreign country. Since October 2021, Canadian provinces and territories used the SMART Health Card format as a requirement by the federal government, including British Columbia, Newfoundland and Labrador, the Northwest Territories, Nova Scotia, Nunavut, Ontario, Quebec, Saskatchewan and the Yukon. On October 13, 2021, the American Immunization Registry Association (AIRA) published a statement encouraging adoption of SMART Health Cards as a common standard "where allowed by local law and policy." "SMARTHealth.Cards" was listed as a supporting member of AIRA through the VCI. A SMART Health Cards Global Forum was held on October 28, 2021. The event featured keynote speakers Andy Slavitt (former Senior Pandemic Advisor to President Joe Biden’s COVID-19 pandemic response team) and Mike Leavitt (former United States Secretary of Health and Human Services). On December 20, 2021, Japan's Ministry of Health, Labour and Welfare launched its COVID-19 Vaccination Certificate Application using the SMART Health Card. By January 2022, about 80% of Americans who had received a COVID-19 vaccine had access to a SMART Health Card through their state governments, local businesses, universities and healthcare systems. == Participants == === Developers === SMART Health IT is based out of the Computational Health Informatics Program (CHIP) at the Boston Children's Hospital. CHIP's related projects include Apache cTAKES, Genomic Information Commons, HealthMap, and VaccineFinder. The SMART Health Card's project sponsor is HL7 International's Public Health Work Group, consisting of representatives from Allscripts, the Altarum Institute, Tennessee Department of Health and Washington State Department of Health. === Issuers === Official registries of authorized SMART Health Card issuers are maintained by SMART Health IT, the Vaccination Credential Initiative, and the CommonTrust Network. Authorized issuers include:
Deep tomographic reconstruction
Deep Tomographic Reconstruction is a set of methods for using deep learning methods to perform tomographic reconstruction of medical and industrial images. It uses artificial intelligence and machine learning, especially deep artificial neural networks or deep learning, to overcome challenges such as measurement noise, data sparsity, image artifacts, and computational inefficiency. This approach has been applied across various imaging modalities, including CT, MRI, PET, SPECT, ultrasound, and optical imaging == Historical background == Traditional tomographic reconstruction relies on analytic methods such as filtered back-projection, or iterative methods which incrementally compute inverse transformations from measurement data (e.g., Radon or Fourier transform data). However, these approaches are not sufficient for certain imaging techniques such as low-dose CT and fast MRI, or scenarios involving metal artifacts and patient motion. == Use in imaging modalities == === Computed tomography (CT) === In CT, deep learning models can be particularly effective in reducing radiation exposure while maintaining image quality. Deep neural networks can also be able to reconstruct images of fair quality from sparsely sampled data without sacrificing diagnostic performance. Deep learning-based generative AI models can reduce CT metal artifacts. === Magnetic resonance imaging (MRI) === In magnetic resonance imaging (MRI), deep learning can lead to reduced MRI motion artifacts, and increased acquisition speed, referred to as fast MRI. Despite suffering from disadvantages such as lower signal-to-noise ratio (SNR), deep learning can enhance image quality in low field MRI, making these systems clinically viable. === Positron emission tomography (PET) and single-photon emission CT (SPECT) === For PET imaging, deep learning models can provide substantial improvements in low-dose imaging and motion artifact correction. Also, deep learning can help SPECT for generation of attenuation background. A notable technique for PET denoising involves integrating MR data through multimodal networks, which use anatomical information from MRI to enhance PET image quality. === Ultrasound imaging === Deep learning can enhance ultrasound imaging by reducing speckle noise and motion blur. For ultrasound beamforming, deep neural networks can allow superior image quality with limited data at high speed. === Optical imaging and microscopy === Diffuse optical tomography, optical coherence tomography and microscopy can be improved by deep neural networks beyond traditional methods. Furthermore, deep learning can also enhance Photoacoustic imaging (see Deep learning in photoacoustic imaging), addressing challenges like high noise, low contrast, and limited resolution. Deep learning has also been applied to label-free live-cell imaging, where convolutional neural networks predict fluorescence labels from transmitted light images, a technique known as in silico labeling. This method can enable high-throughput, non-invasive cell analysis and phenotyping without the need for traditional fluorescent dyes.
Webby Awards
The Webby Awards (colloquially referred to as the Webbys) are awards for excellence on the Internet presented annually by the International Academy of Digital Arts and Sciences, a judging body composed of over three thousand industry experts and technology innovators. Categories include websites, advertising and media, online film and video, mobile sites and apps, and social. Two winners are selected in each category, one by members of The International Academy of Digital Arts and Sciences, and one by the public who cast their votes during Webby People's Voice voting. Each winner presents a five-word acceptance speech, a trademark of the annual awards show. In its early years, the award was hailed as the "Internet's highest honor" and was associated with the phrase "The Oscars of the Internet." == History == In its early years, the organization was one of several vying to be the premiere internet awards show. Both shows would compare themselves to the Oscars, as did media outlets such as The New York Times to Canada's Globe & Mail. The winners of the First Annual Webby Awards in 1995 were presented by John Brancato and Michael Ferris, writers for Columbia Pictures. It was held at the Hollywood Roosevelt Hotel. The televised Webby Awards were sponsored by the Academy of Web Design and Cool Site of the Day. The first Webby Awards were produced by Kay Dangaard at the Hollywood Roosevelt Hotel as a nod to the first site of the Academy of Motion Picture Arts and Sciences (Oscars). That first year, they were called "Webbie" Awards. The first "Site of the Year" winner was the pioneer webisodic serial The Spot. The modern Webby Awards were co-founded by Tiffany Shlain, a filmmaker, when she was hired by The Web Magazine to re-establish them, and were first held in San Francisco in 1997. They quickly became known for its requirement that winners give their acceptance speeches in five words. After this, the awards became more successful than the magazine and IDG closed the publication. Shlain and co-founder Maya Draisin Farrah continued to run The Webby Awards until 2004. The International Academy of Digital Arts and Sciences, which selects the winners of The Webby Awards, was established in 1998 by co-founders Tiffany Shlain, Spencer Ante and Maya Draisin. Members of the Academy include Kevin Spacey, Grimes, Questlove, Internet inventor Vint Cerf, Instagram's Head of Fashion Partnerships Eva Chen, comedian Jimmy Kimmel, Twitter founder Biz Stone, Vice Media co-founder and CEO Shane Smith, Tumblr's David Karp, Director of Harvard's Berkman Klein Center for Internet & Society Susan P. Crawford, Refinery29's Executive Creative Director Piera Gelardi, and CEO and co-founder of Gimlet Media Alex Blumberg. The Webby Awards is owned and operated by the Webby Media Group, a division of Recognition Media, which also owns and produces the Lovie Awards in Europe and Netted by the Webbys, a daily email publication launched in 2009. David-Michel Davies, CEO of Webby Media Group, current Executive Director of the Webby Awards and co-founder of Internet Week New York, was named Executive Director of the Webby Awards in 2005. In 2009, the 13th Annual Webby Awards received nearly 10,000 entries from all 50 US states and over 60 countries. That same year, more than 500,000 votes were cast in The Webby People's Voice Awards. In 2012, the 16th Annual Webby awards received 1.5 million votes from more than 200 countries for the People's Voice awards. In 2015, the 19th Annual Webby Awards received nearly 13,000 entries from all 50 U.S. states and over 60 countries worldwide. == Nomination process == The 2000 awards began the transition to nominee submissions. Previously, nominees had been selected by an internal committee. As early as 2017, organizations wanting to nominate themselves were charged $395 for a single entry. An "ad campaign entry" would cost $595. By 2024, those fees had risen to $495 and $675, respectively. Executive Academy Members with category-specific expertise evaluate the shortlisted entries based on the appropriate Website, Advertising & Media, Online Film & Video, Mobile Sites & Apps, and Social category criteria, and cast ballots to determine Webby Honorees, Nominees and Webby Winners. Deloitte provides vote tabulation consulting for the Webby Awards. In addition to the award given in each category by the International Academy of Digital Arts and Sciences, another winner is selected in each category as determined by the general public during People's Voice voting. Winners of both the Academy-selected and People's Voice-selected awards are invited to the Webbys. == Awards granted == The Webby Awards are presented in over a hundred categories among all four types of entries. A website can be entered in multiple categories and receive multiple awards. In each category, two awards are handed out: a Webby Award selected by The International Academy of Digital Arts and Sciences, and a People's Voice Award selected by the general public. == Ceremony == Between 2005 and 2019, the Webby Awards were presented in New York City. Many of the ceremony hosts are comedians and comedic actors. Comedian Rob Corddry hosted the ceremony from 2005 to 2007. Seth Meyers of Saturday Night Live hosted in 2008 and 2009, B.J. Novak of the sitcom The Office in 2010, and Lisa Kudrow in 2011. Comedian, actor, and writer Patton Oswalt hosted from 2012 to 2014. Comedian Hannibal Buress hosted in 2015. The Webbys are famous for limiting recipients to five-word speeches, which are often humorous, although some exceed the limit. In 2005 when accepting his Lifetime Achievement Award, former Vice President Al Gore's speech was "Please don't recount this vote." He was introduced by Vint Cerf who used the same format to state, "We all invented the Internet." In 2013, the creator of the Graphics Interchange Format (GIF), Steve Wilhite, accepted his Webby and delivered his now famous five-word speech, "It's pronounced 'Jif' not 'Gif'." == Criticism == The Webbys have been criticized for their pay-to-enter and pay-to-attend policy (winners and nominees also have to pay to attend the award ceremony), and thus for not taking most websites into consideration before distributing their awards. Gawker, its Valleywag column, and others, have called the awards a scam, with Valleywag saying, "...somewhere along the way, the organizers figured out that this goofy charade could be milked for profit." In response, Webby Awards executive director David-Michel Davies told the Wall Street Journal that entry fees "provide the best and most sustainable model for ensuring that our judging process remains consistent and rigorous and is not dependent on things like sponsorships that can fluctuate from year to year." == Anthem Awards == In 2021, the Webby organization started a new line of awards, the Anthem Awards, to honor the purpose and mission-driven work of people, companies and organizations worldwide. The finalists and winners are selected by the International Academy of Digital Arts and Sciences.
KKday
KKday is an online travel e-commerce platform focused on connecting independent travelers with authentic, curated local experiences, tours, activities, and attraction tickets. == History == KKday was founded in 2014 in Taipei, Taiwan, by CEO Ming Chen, who previously started and led both Star Travel and Ezfly to IPO. In March of 2016, the company raised US$4.5 million in a Series A round led by AppWorks Ventures with participation by 91Capital. The raise allowed KKday to open offices and expand into Hong Kong, Japan, South Korea and Singapore by 2016. By the end of 2016, KKday offered over 6,000 travel experiences across 53 countries and 174 cities, marking early international expansion with its official launch in Singapore in October 2016, accompanied by promotional campaigns to attract regional users. Expansion into Malaysia, Thailand, Vietnam and the Philippines continued throughout 2017 and into 2018, with the company opening offices in Indonesia and mainland China. KKday rapidly expanded its inventory, reaching over 10,000 experiences in more than 500 cities across 80 countries by 2018, with key markets in Taiwan, Hong Kong, and South Korea. In February 2018, KKday raised $10.5 million in a funding round led by Japanese travel giant H.I.S., allowing integration with larger travel networks and further global growth. Forbes reports that by the end of 2018, the company operated in 11 countries and regions, employed around 400 staff, and recorded over 4 million weekly website views with more than 1 million app downloads. A combination of a Japanese and South Korean trade dispute, along with the Covid-19 pandemic in 2020, lead KKday to pivot quickly toward domestic staycations and local experiences while initially raising $70m in their Series C which, was later extended to $95m. The Series C funds were partially used to accelerate and expand Rezio. Launched in 2019, Rezio is KKday's B2B SaaS booking management platform for travel providers, allowing them to track inventory, manage reservations and sell tickets. FineDayClub was launched in 2020 by KKday as a personalized luxury subscription travel service to cater to high end clients. KKday’s CFO, Jenny Tsai pivoted to lead KKday’s new venture. KKday was able to successfully navigate and adapt to travel patterns during the Covid-19 pandemic by reducing user acquisition costs by two thirds and focusing on domestic travel experiences to drive bookings and revenue. KKday was particularly successful in Vietnam, with bookings increased by 2,000% through 2022 and the company's travel operator platform Rezio, onboarding over 1,200 operators inside the country. In 2021, KKday acquired Activity Japan, a domestic focused travel company, founded by Kimiharu Obuchi in 2014. The successful acquisition, a key factor in KKday’s rapid expansion in the Japanese market, was facilitated by H.I.S., a common early investor in both platforms. In 2023 KKday inked a partnership with Rail Europe to create an all-in-one platform for 150 rail lines over 33 European countries with the intent of increasing ridership across Europe. In late 2024, KKday completed its Series D at $70M, bringing the total amount of capital raised to over $250M. The funds are to be earmarked for continued global expansion, artificial intelligence integration and enhanced partnerships, similar to the partnership with Tablelog, which now allows users to book restaurant reservations at 42,000 restaurants in Japan through the platform. == Platform == KKDay is an e-commerce online travel agency operating in 92 countries with over 350,000 travel experiences available for booking. The company started with focus on authentic local travel experiences in the Asian Pacific market and has expanded to a more global focus. KKday connects travelers with travel services and experiences such as attraction tickets, theme parks, cultural experiences, and seasonal events. KKday has positioned itself as an all-in-one travel super app with booking for hotels, rental cars, flights, sim cards, rail passes, dining and tickets. === Rezio === Rezio is a cloud-based SaaS booking management platform developed by KKday specifically for tour operators, activity providers, and attractions in the travel industry. It serves as an all-in-one system designed to help these businesses digitize their operations, particularly those previously relying on offline processes. Features include a mobile app for on-the-go order management, customer information checks, and voucher scanning, as well as channel management, analytics for customer data, and integrations with multiple OTAs and payment providers. Unlike KKday, which is an OTA marketplace for consumer exposure (with commissions), Rezio focuses on backend operations for suppliers, allowing brand independence, operational efficiency, and direct customer relationships while optionally connecting to OTAs like KKday. Rezio supports over 5,000 merchants, 30,000 experiences, and 10 million travelers worldwide, with a strong presence in Asia. One of the brands successful implementations was at the Nikko Toshogu Shrine where Rezio was implemented to help with long lines and wait times due to over-tourism. The shrine was able to implement the inventory management features to allow online booking and cashless payments onsite. === FineDayClub === FineDayClub is a membership-based travel concierge service launched in late 2020 by KKday. It is aimed at families, and organizations seeking customized travel experiences. It offers one-on-one advisory services. === ActivityJapan === ActivityJapan is a Japanese comprehensive online travel site that specializes in authentic Japanese travel experiences. It was purchased by KKday in 2021 but continues to operate independently.
Feature detection (web development)
Feature detection (also feature testing) is a technique used in web development for handling differences between runtime environments (typically web browsers or user agents), by programmatically testing for clues that the environment may or may not offer certain functionality. This information is then used to make the application adapt in some way to suit the environment: to make use of certain APIs, or tailor for a better user experience. Its proponents claim it is more reliable and future-proof than other techniques like user agent sniffing and browser-specific CSS hacks. == Techniques == A feature test can take many forms. It is essentially any snippet of code which gives some level of confidence that a required feature is indeed supported. However, in contrast to other techniques, feature detection usually focuses on performing actions which directly relate to the feature to be detected, rather than heuristics. === JavaScript === JavaScript feature detection can inspect the DOM and the local JavaScript environment to test whether browser features or APIs are supported. The simplest technique is to check for the existence of a relevant object or property. For example, the Geolocation API (used for accessing the device's knowledge of its geographical location, possibly obtained from a GPS navigation device) exposes a geolocation property on the navigator object in the DOM; the presence of which implies the Geolocation API is supported: if ('geolocation' in navigator) { // Geolocation API is supported } For a higher level of confidence, some feature tests will attempt to invoke the feature then look for clues that it behaved properly. For example, a test for support for cookies might attempt to set a value as a cookie and then verify it can be read back. === CSS === In CSS, the at-rule @supports introduced in 2015 allows to test if a given feature is supported. For instance the following code activates the declarations only if the user agent supports display: flex: == Undetectables == Some browser features are considered undetectable, because no clues are known to give sufficient confidence that a feature is supported. These are often because of limited information available to the JavaScript environment in the browser; generally features must be exposed via the DOM in some way in order to be detectable using JavaScript. When undetectables are encountered, it is common to turn to user agent sniffing as an alternative mechanism, or to employ defensive coding to minimise the impact if the feature turns out not to be supported. The Modernizr project maintains a record of known undetectables on their wiki.
Voice activity detection
Voice activity detection (VAD), also known as speech activity detection or speech detection, is the detection of the presence or absence of human speech, used in speech processing. The main uses of VAD are in speaker diarization, speech coding and speech recognition. It can facilitate speech processing, and can also be used to deactivate some processes during non-speech section of an audio session: it can avoid unnecessary coding/transmission of silence packets in Voice over Internet Protocol (VoIP) applications, saving on computation and on network bandwidth. VAD is an important enabling technology for a variety of speech-based applications. Therefore, various VAD algorithms have been developed that provide varying features and compromises between latency, sensitivity, accuracy and computational cost. Some VAD algorithms also provide further analysis, for example whether the speech is voiced, unvoiced or sustained. Voice activity detection is usually independent of language. It was first investigated for use on time-assignment speech interpolation (TASI) systems. == Algorithm overview == The typical design of a VAD algorithm is as follows: There may first be a noise reduction stage, e.g. via spectral subtraction. Then some features or quantities are calculated from a section of the input signal. A classification rule is applied to classify the section as speech or non-speech – often this classification rule finds when a value exceeds a certain threshold. There may be some feedback in this sequence, in which the VAD decision is used to improve the noise estimate in the noise reduction stage, or to adaptively vary the threshold(s). These feedback operations improve the VAD performance in non-stationary noise (i.e. when the noise varies a lot). A representative set of recently published VAD methods formulates the decision rule on a frame by frame basis using instantaneous measures of the divergence distance between speech and noise. The different measures which are used in VAD methods include spectral slope, correlation coefficients, log likelihood ratio, cepstral, weighted cepstral, and modified distance measures. Independently from the choice of VAD algorithm, a compromise must be made between having voice detected as noise, or noise detected as voice (between false positive and false negative). A VAD operating in a mobile phone must be able to detect speech in the presence of a range of very diverse types of acoustic background noise. In these difficult detection conditions it is often preferable that a VAD should fail-safe, indicating speech detected when the decision is in doubt, to lower the chance of losing speech segments. The biggest difficulty in the detection of speech in this environment is the very low signal-to-noise ratios (SNRs) that are encountered. It may be impossible to distinguish between speech and noise using simple level detection techniques when parts of the speech utterance are buried below the noise. == Applications == VAD is an integral part of different speech communication systems such as audio conferencing, echo cancellation, speech recognition, speech encoding, speaker recognition and hands-free telephony. In the field of multimedia applications, VAD allows simultaneous voice and data applications. Similarly, in Universal Mobile Telecommunications Systems (UMTS), it controls and reduces the average bit rate and enhances overall coding quality of speech. In cellular radio systems (for instance GSM and CDMA systems) based on Discontinuous Transmission (DTX) mode, VAD is essential for enhancing system capacity by reducing co-channel interference and power consumption in portable digital devices. In speech processing applications, voice activity detection plays an important role since non-speech frames are often discarded. For a wide range of applications such as digital mobile radio, Digital Simultaneous Voice and Data (DSVD) or speech storage, it is desirable to provide a discontinuous transmission of speech-coding parameters. Advantages can include lower average power consumption in mobile handsets, higher average bit rate for simultaneous services like data transmission, or a higher capacity on storage chips. However, the improvement depends mainly on the percentage of pauses during speech and the reliability of the VAD used to detect these intervals. On the one hand, it is advantageous to have a low percentage of speech activity. On the other hand, clipping, that is the loss of milliseconds of active speech, should be minimized to preserve quality. This is the crucial problem for a VAD algorithm under heavy noise conditions. === Use in telemarketing === One controversial application of VAD is in conjunction with predictive dialers used by telemarketing firms. In order to maximize agent productivity, telemarketing firms set up predictive dialers to call more numbers than they have agents available, knowing most calls will end up in either "Ring – No Answer" or answering machines. When a person answers, they typically speak briefly ("Hello", "Good evening", etc.) and then there is a brief period of silence. Answering machine messages are usually 3–15 seconds of continuous speech. By setting VAD parameters correctly, dialers can determine whether a person or a machine answered the call and, if it's a person, transfer the call to an available agent. If it detects an answering machine message, the dialer hangs up. Often, even when the system correctly detects a person answering the call, no agent may be available, resulting in a "silent call". Call screening with a multi-second message like "please say who you are, and I may pick up the phone" will frustrate such automated calls. == Performance evaluation == To evaluate a VAD, its output using test recordings is compared with those of an "ideal" VAD – created by hand-annotating the presence or absence of voice in the recordings. The performance of a VAD is commonly evaluated on the basis of the following four parameters: FEC (Front End Clipping): clipping introduced in passing from noise to speech activity; MSC (Mid Speech Clipping): clipping due to speech misclassified as noise; OVER: noise interpreted as speech due to the VAD flag remaining active in passing from speech activity to noise; NDS (Noise Detected as Speech): noise interpreted as speech within a silence period. Although the method described above provides useful objective information concerning the performance of a VAD, it is only an approximate measure of the subjective effect. For example, the effects of speech signal clipping can at times be hidden by the presence of background noise, depending on the model chosen for the comfort noise synthesis, so some of the clipping measured with objective tests is in reality not audible. It is therefore important to carry out subjective tests on VADs, the main aim of which is to ensure that the clipping perceived is acceptable. In VoIP applications, front-end clipping can be reduced by rewinding to shortly before the detection and sending very slightly delayed data. This kind of test requires a certain number of listeners to judge recordings containing the processing results of the VADs being tested, giving marks to several speech sequences on the following features: Quality; Comprehension difficulty; Audibility of clipping. These marks are then used to calculate average results for each of the features listed above, thus providing a global estimate of the behavior of the VAD being tested. To conclude, whereas objective methods are very useful in an initial stage to evaluate the quality of a VAD, subjective methods are more significant. As they require the participation of several people for a few days, increasing cost, they are generally only used when a proposal is about to be standardized. == Implementations == One early standard VAD is that developed by British Telecom for use in the Pan-European digital cellular mobile telephone service in 1991. It uses inverse filtering trained on non-speech segments to filter out background noise, so that it can then more reliably use a simple power-threshold to decide if a voice is present. The G.729 standard calculates the following features for its VAD: line spectral frequencies, full-band energy, low-band energy (<1 kHz), and zero-crossing rate. It applies a simple classification using a fixed decision boundary in the space defined by these features, and then applies smoothing and adaptive correction to improve the estimate. The GSM standard includes two VAD options developed by ETSI. Option 1 computes the SNR in nine bands and applies a threshold to these values. Option 2 calculates different parameters: channel power, voice metrics, and noise power. It then thresholds the voice metrics using a threshold that varies according to the estimated SNR. The Speex audio compression library uses a procedure named Improved Minima Controlled Recursive Averaging, which uses a smoothed representation of spectral pow
Digital anthropology
Digital anthropology is the anthropological study of the relationship between humans and digital-era technology. The field is new, and thus has a variety of names with a variety of emphases. These include techno-anthropology, digital ethnography, cyberanthropology, and virtual anthropology. == Definition and scope == Most anthropologists who use the phrase "digital anthropology" are specifically referring to online and Internet technology. The study of humans' relationship to a broader range of technology may fall under other subfields of anthropological study, such as cyborg anthropology. The Digital Anthropology Group (DANG) is classified as an interest group in the American Anthropological Association. DANG's mission includes promoting the use of digital technology as a tool of anthropological research, encouraging anthropologists to share research using digital platforms, and outlining ways for anthropologists to study digital communities. Cyberspace or the "virtual world" itself can serve as a "field" site for anthropologists, allowing the observation, analysis, and interpretation of the sociocultural phenomena springing up and taking place in any interactive space. National and transnational communities, enabled by digital technology, establish a set of social norms, practices, traditions, storied history and associated collective memory, migration periods, internal and external conflicts, potentially subconscious language features and memetic dialects comparable to those of traditional, geographically confined communities. This includes the various communities built around free and open-source software, online platforms such as Facebook, Twitter/X, Instagram, 4chan and Reddit and their respective sub-sites, and politically motivated groups like Anonymous, WikiLeaks, or the Occupy movement. A number of academic anthropologists have conducted traditional ethnographies of virtual worlds, such as Bonnie Nardi's study of World of Warcraft or Tom Boellstorff's study of Second Life. Academic Gabriella Coleman has done ethnographic work on the Debian software community and the Anonymous hacktivist network. Theorist Nancy Mauro-Flude conducts ethnographic field work on computing arts and computer subcultures such as systerserver.net a part of the communities of feminist web servers and the Feminist Internet network. Eitan Y. Wilf examines the intersection of artists' creativity and digital technology and artificial intelligence. Yongming Zhou studied how in China the internet is used to participate in politics. Eve M. Zucker and colleagues study the shift to digital memorialization of mass atrocities and the emergent role of artificial intelligence in these processes. Victoria Bernal conducted ethnographic research on the themes of nationalism and citizenship among Eritreans participating in online political engagement with their homeland. Anthropological research can help designers adapt and improve technology. Australian anthropologist Genevieve Bell did extensive user experience research at Intel that informed the company's approach to its technology, users, and market. == Methodology == === Digital fieldwork === Many digital anthropologists who study online communities use traditional methods of anthropological research. They participate in online communities in order to learn about their customs and worldviews, and back their observations with private interviews, historical research, and quantitative data. Their product is an ethnography, a qualitative description of their experience and analyses. Other anthropologists and social scientists have conducted research that emphasizes data gathered by websites and servers. However, academics often have trouble accessing user data on the same scale as social media corporations like Facebook and data mining companies like Acxiom. In terms of method, there is a disagreement in whether it is possible to conduct research exclusively online or if research will only be complete when the subjects are studied holistically, both online and offline. Tom Boellstorff, who conducted a three-year research as an avatar in the virtual world Second Life, defends the first approach, stating that it is not just possible, but necessary to engage with subjects “in their own terms”. Others, such as Daniel Miller, have argued that an ethnographic research should not exclude learning about the subject's life outside the internet. === Digital technology as a tool of anthropology === The American Anthropological Association offers an online guide for students using digital technology to store and share data. Data can be uploaded to digital databases to be stored, shared, and interpreted. Text and numerical analysis software can help produce metadata, while a codebook may help organize data. == Ethics == Online fieldwork offers new ethical challenges. According to the American Anthropological Association's ethics guidelines, anthropologists researching a community must make sure that all members of that community know they are being studied and have access to data the anthropologist produces. However, many online communities' interactions are publicly available for anyone to read, and may be preserved online for years. Digital anthropologists debate the extent to which lurking in online communities and sifting through public archives is ethical. The Association also asserts that anthropologists' ability to collect and store data at all is "a privilege", and researchers have an ethical duty to store digital data responsibly. This means protecting the identity of participants, sharing data with other anthropologists, and making backup copies of all data. == Prominent figures == Genevieve Bell is an Australian cultural anthropologist credited for pioneering the User Experience field. During her time working for Intel Corporation, Bell studied how various cultures from around the world interacted with and experienced technology. Researching and improving user experience allows companies and designers to gather data regarding how users utilize their digital products and what requires improvement or expansion. Tom Boellstorff is an anthropologist known for Coming of Age in Second Life: An Anthropologist Explores the Virtually Human where he conducted research on how engaging in virtual worlds affects the player’s sense of self. Gabriella Coleman is an American anthropologist concerned with the politics, ethics, and culture of hacking and online activism. Coleman’s most notable ethnography features the hacktivist collective Anonymous, where she argues that various genres of hacking exist according to the social conditions at play. Coleman is dedicated to making her ethnography accessible to a diverse audience, including academics and non-academics. Diana E. Forsythe was an American anthropologist of science and technology and the author of the essays featured in Studying Those Who Study Us: An Anthropologist in the World of Artificial Intelligence. She asked relevant questions such as how should humans interact with computers and how gender roles are maintained in technology-oriented occupations. Heather Horst is a sociocultural anthropologist interested in the relationship between digital social relations and material culture. Nancy Mauro-Flude is a design anthropologist whose work explores the tacit relations between embodied cognition, computational materiality, maker culture, self-hosted webserver cooperatives, creative practice, and artistic research in digital infrastructure and Internet publishing. Mizuko Ito is a Japanese cultural anthropologist specializing in technology use and the intersection between computers and the social sciences. Her primary interest is in how young people utilize media technology and how it can be used to engage students in education. Daniel Miller is an anthropologist with a concentration in digital anthropology. His research includes the smartphone and perpetual opportunism, the intent and consequences of posting on social media in various geographical locations, and how hospice patients use media to socialize in the last stage of their lives. Mike Wesch is a cultural anthropologist interested in how people share their lives, cultures, and beliefs through digital media.