AI For Students Writing

AI For Students Writing — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  MyRadar

  MyRadar is a free weather forecasting application developed by Andy Green and his Orlando, Florida-based company ACME AtronOmatic (ACME). The app began operations in 2008 and ran on government-provided weather and radar data for its first decade. In 2019, ACME launched personal satellites to improve predictions of ongoing weather. The app received funding to improve its radar and imaging from the Federal Communications Commission (FCC), National Oceanic and Atmospheric Administration (NOAA), and the Office of Naval Research (ONR). ACME created a weather data satellite constellation named "Hyperspectral Orbital Remote Imaging Spectrometer" (HORIS), which utilizes machine learning and artificial intelligence (AI) to create a current weather map. With the introduction of additional features, including the detection of wildfires and illegal fishing, the app has more broadly become an environmental intelligence app since 2022. In 2024, the app partnered with the Total Traffic and Weather Network (TTWN) to provide traffic flow and incident data for users with paying subscriptions via CarPlay and Android Auto. == History == The app's creator, Andy Green, had created internet tech since the 1980s. His first major project was the development of a public access internet service company based in Rhode Island, which he later sold to finance the creation of ACME AtronOmatic ("ACME" for short), based in Orlando, Florida. The first major app created by ACME was called "Flightwise", which provided users with flight tracking information. In summer 2008, Green had the idea to use the animated location tracker already built-in to Flightwise to make a stand-alone weather forecasting app after wondering if a meal he was eating outdoors would get rained out. MyRadar was launched in 2012 out of an office in Orlando. Despite running solely off of free government-provided weather and radar data for the first decade after launch, Green said the app "took off like wildfire" in downloads. In December 2017, the app partnered with "TripIt" to provide users with information about flight delays and gate changes, eliminating the need for a separate app like Flightwise. In 2019, ACME launched their first personal satellite for the app, a small prototype from New Zealand, as part of an effort to provide detailed imagery and improved predictions of ongoing weather unique to the app. More satellites were eventually launched by ACME to create a weather data satellite constellation named "Hyperspectral Orbital Remote Imaging Spectrometer" (HORIS), monitored by ground stations maintained by Kongsberg Satellite Services. HORIS operates MyRadar by taking the environmental data and imagery it collects and pairing it with machine learning and artificial intelligence (AI) to create a real-time weather map. In 2022, HORIS was expanded upon after ACME won approval from the Federal Communications Commission (FCC) to improve their satellite constellation to include 250 satellites or more. The main batch of satellites were PocketQubes, which entered the atmosphere on May 2, 2022, by Rocket Lab Electron launched from New Zealand, with the additional purpose to test and validate the existing satellites in orbit. In October 2022, ACME received a US$150,000 Small Business Innovation Research (SBIR) grant from the National Oceanic and Atmospheric Administration (NOAA) to improve the app's wildfire detection and air quality measurement technology to better detect smoke, aerosols, fire hotspots using satellites and aerial drones. On August 18, 2023, phase two of the NOAA grant was approved, providing an additional US$650,000 to aid in the app's aforementioned goals by launching a pair of CubeSat satellites to provide high-definition infrared imagery. On September 8, 2023, ACME secured another US$1,200,000 in crowd funding to aid accomplishing the goals of the NOAA grant by expanding the app's workforce from 35 to 100 employees by the end of 2024. In January 2024, MyRadar partnered with Total Traffic and Weather Network (TTWN) to provide traffic data overlaid with its pre-existing weather graphics for users in the United States. The partnership allowed for the app to additionally become a tool for navigation. This officially became a feature days later on January 8, 2024, when the app was made compatible with Apple's CarPlay. On February 7, 2024, the Android equivalent Android Auto also gained the ability to display the app on car interfaces. In March 2024, the app launched a "meteorological wedding planning service" in the United States and Canada for prices between US$1,000 and US$5,000, in which users can request a personal meteorologist to provide an in-person meeting about the best dates for a wedding, and on-call local weather updates the day of. Scheduled for February 2025, four more satellites to help with the NOAA-sponsored wildfire detection are to be launched, and the first by ACME to have AI processing in the satellites themself and not computers on the ground, allowing for quicker transfer of information. == Features and general information == The app's primary function is to provide weather forecasting and prediction to users. The app includes toggleable options to track and send alerts to users for rain, wind patterns, earthquakes, tornadoes, tropical cyclones, wildfires, and more. In early 2020, a feature was added to track orbital objects such as the International Space Station. In May 2022, with the imagery improvement of HORIS, the app gained the secondary abilities to better monitor algae blooms, coral reefs, illegal fishing, and wildfires. In January and February 2024, the ability to display traffic flow and incident data in a feature called "RouteCast" was added, and can be displayed in video and 3D options via CarPlay and Android Auto for users with paying subscriptions. The app also provides annual tropical storm and tornado outlooks for their respective seasons, gathered through satellite and aerial drone data, as well as through on the ground storm chasers.

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  Hardware random number generator

  In computing, a hardware random number generator (HRNG), true random number generator (TRNG), non-deterministic random bit generator (NRBG), or physical random number generator is a device that generates random numbers from a physical process capable of producing entropy, unlike a pseudorandom number generator (PRNG) that utilizes a deterministic algorithm and non-physical nondeterministic random bit generators that do not include hardware dedicated to generation of entropy. Many natural phenomena generate low-level, statistically random "noise" signals, including thermal and shot noise, jitter and metastability of electronic circuits, Brownian motion, and atmospheric noise. Researchers also used the photoelectric effect, involving a beam splitter, other quantum phenomena, and even nuclear decay (due to practical considerations the latter, as well as the atmospheric noise, is not viable except for fairly restricted applications or online distribution services). While "classical" (non-quantum) phenomena are not truly random, an unpredictable physical system is usually acceptable as a source of randomness, so the qualifiers "true" and "physical" are used interchangeably. A hardware random number generator is expected to output near-perfect random numbers ("full entropy"). A physical process usually does not have this property, and a practical TRNG typically includes a few blocks: a noise source that implements the physical process producing the entropy. Usually this process is analog, so a digitizer is used to convert the output of the analog source into a binary representation; a conditioner (randomness extractor) that improves the quality of the random bits; health tests. TRNGs are mostly used in cryptographical algorithms that get completely broken if the random numbers have low entropy, so the testing functionality is usually included. Hardware random number generators generally produce only a limited number of random bits per second. In order to increase the available output data rate, they are often used to generate the "seed" for a faster PRNG. PRNG also helps with the noise source "anonymization" (whitening out the noise source identifying characteristics) and entropy extraction. With a proper PRNG algorithm selected (cryptographically secure pseudorandom number generator, CSPRNG), the combination can satisfy the requirements of Federal Information Processing Standards and Common Criteria standards. == Uses == Hardware random number generators can be used in any application that needs randomness. However, in many scientific applications additional cost and complexity of a TRNG (when compared with pseudo random number generators) provide no meaningful benefits. TRNGs have additional drawbacks for data science and statistical applications: impossibility to re-run a series of numbers unless they are stored, reliance on an analog physical entity can obscure the failure of the source. The TRNGs therefore are primarily used in the applications where their unpredictability and the impossibility to re-run the sequence of numbers are crucial to the success of the implementation: in cryptography and gambling machines. === Cryptography === The major use for hardware random number generators is in the field of data encryption, for example to create random cryptographic keys and nonces needed to encrypt and sign data. In addition to randomness, there are at least two additional requirements imposed by the cryptographic applications: forward secrecy guarantees that the knowledge of the past output and internal state of the device should not enable the attacker to predict future data; backward secrecy protects the "opposite direction": knowledge of the output and internal state in the future should not divulge the preceding data. A typical way to fulfill these requirements is to use a TRNG to seed a cryptographically secure pseudorandom number generator. == History == Physical devices were used to generate random numbers for thousands of years, primarily for gambling. Dice in particular have been known for more than 5000 years (found on locations in modern Iraq and Iran), and flipping a coin (thus producing a random bit) dates at least to the times of ancient Rome. The first documented use of a physical random number generator for scientific purposes was by Francis Galton (1890). He devised a way to sample a probability distribution using a common gambling die. In addition to the top digit, Galton also looked at the face of a die closest to him, thus creating 64 = 24 outcomes (about 4.6 bits of randomness). Kendall and Babington-Smith (1938) used a fast-rotating 10-sector disk that was illuminated by periodic bursts of light. The sampling was done by a human who wrote the number under the light beam onto a pad. The device was utilized to produce a 100,000-digit random number table (at the time such tables were used for statistical experiments, like PRNG nowadays). On 29 April 1947, the RAND Corporation began generating random digits with an "electronic roulette wheel", consisting of a random frequency pulse source of about 100,000 pulses per second gated once per second with a constant frequency pulse and fed into a five-bit binary counter. Douglas Aircraft built the equipment, implementing Cecil Hasting's suggestion (RAND P-113) for a noise source (most likely the well known behavior of the 6D4 miniature gas thyratron tube, when placed in a magnetic field). Twenty of the 32 possible counter values were mapped onto the 10 decimal digits and the other 12 counter values were discarded. The results of a long run from the RAND machine, filtered and tested, were converted into a table, which originally existed only as a deck of punched cards, but was later published in 1955 as a book, 50 rows of 50 digits on each page (A Million Random Digits with 100,000 Normal Deviates). The RAND table was a significant breakthrough in delivering random numbers because such a large and carefully prepared table had never before been available. It has been a useful source for simulations, modeling, and for deriving the arbitrary constants in cryptographic algorithms to demonstrate that the constants had not been selected maliciously ("nothing up my sleeve numbers"). Since the early 1950s, research into TRNGs has been highly active, with thousands of research works published and about 2000 patents granted by 2017. == Physical phenomena with random properties == Multiple different TRNG designs were proposed over time with a large variety of noise sources and digitization techniques ("harvesting"). However, practical considerations (size, power, cost, performance, robustness) dictate the following desirable traits: use of a commonly available inexpensive silicon process; exclusive use of digital design techniques. This allows an easier system-on-chip integration and enables the use of FPGAs; compact and low-power design. This discourages use of analog components (e.g., amplifiers); mathematical justification of the entropy collection mechanisms. Stipčević & Koç in 2014 classified the physical phenomena used to implement TRNG into four groups: electrical noise; free-running oscillators; chaos; quantum effects. === Electrical noise-based RNG === Noise-based RNGs generally follow the same outline: the source of a noise generator is fed into a comparator. If the voltage is above threshold, the comparator output is 1, otherwise 0. The random bit value is latched using a flip-flop. Sources of noise vary and include: Johnson–Nyquist noise ("thermal noise"); Zener noise; avalanche breakdown. The drawbacks of using noise sources for an RNG design are: noise levels are hard to control, they vary with environmental changes and device-to-device; calibration processes needed to ensure a guaranteed amount of entropy are time-consuming; noise levels are typically low, thus the design requires power-hungry amplifiers. The sensitivity of amplifier inputs enables manipulation by an attacker; circuitry located nearby generates a lot of non-random noise thus lowering the entropy; a proof of randomness is near-impossible as multiple interacting physical processes are involved. === Chaos-based RNG === The idea of chaos-based noise stems from the use of a complex system that is hard to characterize by observing its behavior over time. For example, lasers can be put into (undesirable in other applications) chaos mode with chaotically fluctuating power, with power detected using a photodiode and sampled by a comparator. The design can be quite small, as all photonics elements can be integrated on-chip. Stipčević & Koç characterize this technique as "most objectionable", mostly due to the fact that chaotic behavior is usually controlled by a differential equation and no new randomness is introduced, thus there is a possibility of the chaos-based TRNG producing a limited subset of possible output strings. === Free-running oscillators-based RNG === The TRNGs based on a free-running oscilla

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  Social film

  A social film is a type of interactive film that is presented through the lens of social media. A social film is distributed digitally and integrates with a social networking service, such as Facebook or YouTube. It combines features of web video, social network games and social media. == Key elements == Social films are a more recent phenomenon, and, in turn, there are few precedents for their format. Although there are not many examples of this genre of film, the medium has certain identifiable elements: Casual entertainment Social media User-generated content Game mechanics Using just one of these factors or a combination of them, a social film engages viewers to interact directly with the work. This can be done through usual social media functionality like comments and ranking or adding directly to the narrative itself. Just as with memes, social film distribution relies on the viral spread enabled by social media. This is based on the viral expansion loops model, in which a viewer benefits from sharing the application with friends, exponentially creating new viewers compelled to share the application. == History == One of the first social films to be created was from the YouTube channel lonelygirl15. This social film started in 2006 and was created by Miles Beckett , Mesh Flinders, and Greg Goodfried. They used YouTube posts to create an interactive video series about a fictional character who showcased her life in a vlog format. As the videos went on, more bizarre things would keep happening to the main character, Bree, before she just stopped uploading. This channel was not only the first viral social film, but went on to be one of the first viral YouTube channels to be created. It did take a few years to see any more films in this genre, but 2011 saw many people start to try their hand at making these films. The first social film in this year was a film called Him, Her and Them which was produced and released by Murmur in April 2011. It was distributed exclusively through Facebook and promoted as the first “Facebook film.” The film is composed of short video clips and interactive slideshows, integrating Facebook's Social Graph API. Users participate via text-based additions to the story, which are viewable only by friends within their social network. In May 2011, Canon and Ron Howard teamed up to create Project Imagin8ion, which was a photo contest where photographers submitted photos and the top 8 photos would be the inspiration for a short film. This short film was called "When You Find Me" and could be found exclusively on YouTube. In July 2011, Intel and Toshiba partnered together to create Hollywood's first Social Film experience, a thriller called Inside, directed by D.J. Caruso and starring Emmy Rossum. The project is broken up into several segments across multiple social media platforms including Facebook, YouTube, and Twitter. In this instance, the audience is challenged to help Emmy Rossum's character, Christina, safely make it out of the room she's been trapped in. This particular form of social film is a major undertaking in that it combines social media activity with A-list acting talent to create a user experience that all happens in real time. Although not quite the same idea, Hollywood also started experimenting with the idea of interactive and crowd-sourced films. One of the first examples of this was a short film called "Life In A Day" directed by Kevin Macdonald and produced by Ridley Scott. Kevin asked people from all over the world to submit videos onto YouTube of what they were doing on July 24th, 2010. They combined all of the best videos that were submitted together to create one film of people doing different things all around the world, no matter how boring or simple those things seemed. They took this short to film festivals before releasing it to the public on YouTube in 2011. In August 2012, Intel and Toshiba partnered again to create The Beauty Inside, directed by Drake Doremus, starring Mary Elizabeth Winstead and Topher Grace. It's Hollywood's first social film that gives everyone in the audience a chance to play Alex, the lead role. The experience will be broken up into six filmed episodes interspersed with real-time interactive storytelling that all takes place on Alex's Facebook timeline. In August 2013, Intel and Toshiba released their third entry into the category, The Power Inside, directed by Will Speck and Josh Gordon and starring Harvey Keitel, Analeigh Tipton, and Craig Roberts. It's Hollywood's first social film that asks the audience to audition to help save or destroy the world. The experience is broken up into six filmed episodes interspersed with user-generated content and interactive storytelling on the main character's Facebook timeline. In 2015, Intel partnered with Dell for their fourth entry, What Lives Inside directed by Robert Stromberg and starring Colin Hanks, Catherine O'Hara, and J. K. Simmons. The first of four episodes was released on Hulu on March 25, 2015.

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  Data lake

  A data lake is a system or repository of data stored in its natural/raw format, usually object blobs or files. A data lake is usually a single store of data including raw copies of source system data, sensor data, social data etc., and transformed data used for tasks such as reporting, visualization, advanced analytics, and machine learning. A data lake can include structured data from relational databases (rows and columns), semi-structured data (CSV, logs, XML, JSON), unstructured data (emails, documents, PDFs), and binary data (images, audio, video). A data lake can be established on premises (within an organization's data centers) or in the cloud (using cloud services). == Background == James Dixon, then chief technology officer at Pentaho, coined the term by 2011 to contrast it with data mart, which is a smaller repository of interesting attributes derived from raw data. In promoting data lakes, he argued that data marts have several inherent problems, such as information siloing. PricewaterhouseCoopers (PwC) said that data lakes could "put an end to data silos". In their study on data lakes, they noted that enterprises were "starting to extract and place data for analytics into a single, Hadoop-based repository." == Examples == Many companies use cloud storage services such as Google Cloud Storage and Amazon S3 or a distributed file system such as Apache Hadoop distributed file system (HDFS). There is a gradual academic interest in the concept of data lakes. For example, Personal DataLake at Cardiff University is a new type of data lake which aims at managing big data of individual users by providing a single point of collecting, organizing, and sharing personal data. Early data lakes, such as Hadoop 1.0, had limited capabilities because it only supported batch-oriented processing (Map Reduce). Interacting with it required expertise in Java, map reduce and higher-level tools like Apache Pig, Apache Spark and Apache Hive (which were also originally batch-oriented). == Criticism == Poorly managed data lakes have been facetiously called data swamps. In June 2015, David Needle characterized "so-called data lakes" as "one of the more controversial ways to manage big data". PwC was also careful to note in their research that not all data lake initiatives are successful. They quote Sean Martin, CTO of Cambridge Semantics: We see customers creating big data graveyards, dumping everything into Hadoop distributed file system (HDFS) and hoping to do something with it down the road. But then they just lose track of what’s there. The main challenge is not creating a data lake, but taking advantage of the opportunities it presents. They describe companies that build successful data lakes as gradually maturing their lake as they figure out which data and metadata are important to the organization. Another criticism is that the term data lake is used with many different meanings. It may be used to refer to, for example: any tools or data management practices that are not data warehouses; a particular technology for implementation; a raw data reservoir; a hub for ETL offload; or a central hub for self-service analytics. While critiques of data lakes are warranted, in many cases they apply to other data projects as well. For example, the definition of data warehouse is also changeable, and not all data warehouse efforts have been successful. In response to various critiques, McKinsey noted that the data lake should be viewed as a service model for delivering business value within the enterprise, not a technology outcome. == Data lakehouses == Data lakehouses are a hybrid approach that can ingest a variety of raw data formats like a data lake, while also providing ACID transactions and enforced data quality like a data warehouse.

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  Showcase Workshop

  Showcase Workshop, also referred to as Showcase, is a SaaS company that develops a presentation-building application for business use. Users upload files and images to a web platform which generates presentations viewable on a suite of mobile apps. Showcase was founded in 2011. The company’s headquarters are in Wellington, New Zealand. == History == Showcase Workshop was originally developed in response to dynamically changing content being presented on iPads at the 2012 Olympics. After market-testing a beta version of the core application, Showcase Workshop launched commercially in 2012. In 2014 Showcase partnered with Vodafone Global Enterprise. == Product == Users upload pre-existing PDFs, videos, images and Microsoft Office documents to a secure server, building presentations or ‘showcases’ which can then be downloaded via the mobile apps. The presentations are used for mobile sales enablement, training, or operational/health and safety purposes. == Reception == Reviewers have praised the ease of use of Showcase, calling it a “better alternative to developing a native app” and “intuitive”. Criticisms include the lack of differing templates and a lack of complex customisation controls. Showcase was nominated for a Tabby Award in 2014 and won a Tabby Award in 2015 for its Windows app.

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  Social media use in education

  Social media in education is the use of social media to enhance education. Social media are "a group of Internet-based applications...that allow the creation and exchange of user-generated content". It is also known as the read/write web. As time went on and technology evolved, social media has been an integral part of people's lives, including students, scholars, and teachers. However, social media are controversial because, in addition to providing new means of connection, critics claim that they damage self-esteem, shorten attention spans, and increase mental health issues. A 2016 dissertation presented surveys that focused on the impact of social media. It reported that 54.6% of students believed that social media affected their studies positively (38% agree, 16.6% strongly agree). About 40% disagreed, and 4.7% of students strongly disagreed. 53% of female students reported that social media negatively impacted their studies. Among male students, 40% agreed that social media had a negative impact on studies, while 59% disagreed. A 2023 article dives deep into the rewards system of the brain in response to social media. This study compares the social rewards system in our brain to those from social media. From ages 10-12, most are receiving a cell phone, social rewards in the brain start to feel more satisfying. Leading to adulthood, the effects of social rewards are less likely to feel reliant on feedback from peers. Equivalent to a more mature prefrontal cortex, this enables a better management of their emotional reaction to these social rewards, meaning a more balanced and controlled reaction. == History == A survey from Cambridge International of nearly 20,000 teachers and students (ages 12–19) from 100 countries found that 48% of students use a desktop computer in class, 42% uses phones, 33% use interactive whiteboards and 20% use tablets. Desktop computers are more used than tablets. Teachers were abandoning the "no phones at school" rule. A 2024 research survey through Common Sense Education reported 54% of age 8-12 and 69% of ages 13-18 social media is an extensive distraction from homework. === United States === The long-running technology boom accelerated after the millennium. As of 2018, 95% of US teenage students had access to a smartphone and 45% said they were online almost constantly. In the early days of social media, access to technology was a significant issue as many students did not own not compatible devices and school budgets were often insufficient to purchase devices for student use. Despite backlash, Missouri passed a law that prohibited teachers from communicating privately with students over social media in 2011. Supporters were concerned that online communication between underage students and faculty could lead to inappropriate relationships. Some schools adopted a "Bring Your Own Device" (BYOD) policy, allowing students to bring Internet-accessing devices, such as phones or tablets to class. During the pandemic, the federal government offered funds that allowed more schools to purchase devices. Over time, more students acquired phones with social media access. Personal devices increased student satisfaction, but reduced teachers' ability to control device use in their classrooms. A 2018 Pew Research study reported that 95% of teenagers had a phone and used social media consistently. === Canada === The Peel District School Board (PDSB) in Ontario accepted the use of social media in the classroom. In 2013, the PDSB introduced BYOD and unblocked many social media sites. That was later replaced by a policy that dealt specifically with social media. == Uses == === Classroom === In the classroom, social media offers a way to systematically distribute and gather information from students. Teachers can supply documents, and audio/video media to students for immediate or later use. One study on higher education reported that devices and social media: created opportunities for interaction provided occasions for collaboration sped up information access offered more ways to learn situated learning. Frustrations included anti-technology instructors, device challenges, and devices as a distraction. Social media in classrooms can have a negative effect. A Yale University publication reported that students who used laptops in class for non-academic reasons had poorer performance. Students spent most of their time on social media, shopping, and other personal activities. Social media has helped many educators mentor their students more effectively. === Outside of class === Social media offer a venue for video calls, stories, feeds, and game playing that can enhance the learning process. Teachers can utilize social media to communicate with their students. Social media can provide students with resources that they can utilize in essays, projects, and presentations. Students can easily access comments made by teachers and peers and offer feedback to teachers. Social media can offer students the opportunity to collaborate by sharing information without requiring face to face meetings. Social media can allow students to more easily connect with experts, to go beyond course materials. Instructors in a 2010 study reported that online technologies (social media) can help students become comfortable having discussions outside the classroom better than traditional means. Teachers may face some risk when using social media outside the classroom, without appropriate work rules. Studies explores how college students' engagement with social media platforms influences their communication preferences and habits, particularly in relation to using school email for academic purposes. === Professional development === Social media can aid professional development, as teachers become students, enhancing knowledge transfer, skill master, and collaboration. === Non-academic uses === Schools can use social media to make public announcements. Teachers and administrators can communicate other important information to parents and students and to receive feedback from them. Families can keep up with school events and policies. === Ecology education === The potential of using social media in ecological, nature and forest education include: virtual nature groups can help promote good habits in forest tourism and recreation (nature ethics), by entering general rules in the regulations by administrators, e.g. "DO NOT PICK UP PLANTS UNKNOWN TO US", which is to protects rare species from pointless picking. social media activity motivates people to learn about nature in the field, allows them to gain knowledge, dispels popular myths, enables contact with scientists and practitioners, promotes valuable literature, websites, and at the same time reveals distortions and substantive errors in popular news services. contact is not only virtual. Despite financial barriers and distance, Internet users organize nature conventions. Such meetings are an opportunity not only to make friends, but also to learn about nature together and have fun. the possibility of contact between scientists and nature lovers via Facebook has become a source of cooperation in species inventory, e.g. the online campaign of the NATRIX Herpetological Society, which consists not only of collecting reports of observations of the smooth snake by Internet users, but also of drawing attention to the biology and threats to this species. Social media has become a place where ecology education quickly reaches people of different ages and social statuses. The nature groups that have been created, in which nature lovers, biologists, foresters and scientists participate, can have a real impact on the state of knowledge and data collection through citizen science. == Apps and services == Social media can allow students to participate in their field by working with organizations outside the classroom. By offering easier access to peers outside the classroom, students can broaden their perspectives and find support resources. Social media aided learning outside of the classroom through collaboration and innovation. One specific study, "Exploring education-related use of social media," called this "audience connectors". Audience connectors bring students together while studying with WhatsApp and Facebook. This study reported that "60 percent [of students in the study] agreed that technology changes education for the better." While social media can promote a beneficial education platform, downsides exist. Students may become skilled at "lifting material from the internet" rather than enhancing their personal understanding. Another downside is student attention spans decline. A concern raised by the students of this study showed how many use spell-check as a crutch and will see a trend of points taken off when spell-check is not an option. Apps like X allowed teachers to make classroom accounts where students can learn about social media in a controlled context. Teachers can post assignments on th

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  Cryptographic Service Provider

  A cryptographic service provider (CSP) is a package that "provides a concrete implementation of certain cryptographic services." A CSP offers operations and protocols to support a variety of use cases. The cryptographic application programming interface (API) provided by the CSP provides common solutions for different platforms, for example hardware and cloud services. == Microsoft Windows == In Microsoft Windows, a Cryptographic Service Provider is a software library that implements the Microsoft CryptoAPI (CAPI). CSPs implement encoding and decoding functions, which computer application programs may use, for example, to implement strong user authentication or for secure email. CSPs are independent modules that can be used by different applications. A user program calls CryptoAPI functions and these are redirected to CSPs functions. Since CSPs are responsible for implementing cryptographic algorithms and standards, applications do not need to be concerned about security details. Furthermore, each application can define which CSP it is going to use on its calls to CryptoAPI. In fact, all cryptographic activity is implemented in CSPs. CryptoAPI only works as a bridge between the application and the CSP. CSPs are implemented basically as a special type of DLL with special restrictions on loading and use. Every CSP must be digitally signed by Microsoft and the signature is verified when Windows loads the CSP. In addition, after being loaded, Windows periodically re-scans the CSP to detect tampering, either by malicious software such as computer viruses or by the user him/herself trying to circumvent restrictions (for example on cryptographic key length) that might be built into the CSP's code. To obtain a signature, non-Microsoft CSP developers must supply paperwork to Microsoft promising to obey various legal restrictions and giving valid contact information. As of circa 2000, Microsoft did not charge any fees to supply these signatures. For development and testing purposes, a CSP developer can configure Windows to recognize the developer's own signatures instead of Microsoft's, but this is a somewhat complex and obscure operation unsuitable for nontechnical end users. The CAPI/CSP architecture had its origins in the era of restrictive US government controls on the export of cryptography. Microsoft's default or "base" CSP then included with Windows was limited to 512-bit RSA public-key cryptography and 40-bit symmetric cryptography, the maximum key lengths permitted in exportable mass market software at the time. CSPs implementing stronger cryptography were available only to U.S. residents, unless the CSPs themselves had received U.S. government export approval. The system of requiring CSPs to be signed only on presentation of completed paperwork was intended to prevent the easy spread of unauthorized CSPs implemented by anonymous or foreign developers. As such, it was presented as a concession made by Microsoft to the government, in order to get export approval for the CAPI itself. After the Bernstein v. United States court decision establishing computer source code as protected free speech and the transfer of cryptographic regulatory authority from the U.S. State Department to the more pro-export Commerce Department, the restrictions on key lengths were dropped, and the CSPs shipped with Windows now include full-strength cryptography. The main use of third-party CSPs is to interface with external cryptography hardware such as hardware security modules (HSM) or smart cards. === Smart Card CSP === These cryptographic functions can be realized by a smart card, thus the Smart Card CSP is the Microsoft way of a PKCS#11. Microsoft Windows is identifying the correct Smart Card CSP, which have to be used, analyzing the answer to reset (ATR) of the smart card, which is registered in the Windows Registry. Installing a new CSP, all ATRs of the supported smart cards are enlisted in the registry. === Use of CSP in MS Office password protection === Cryptographic service providers can be used for encryption of Word, Excel, and PowerPoint documents starting from Microsoft Office XP. A standard encryption algorithm with a 40-bit key is used by default, but enabling a CSP enhances key length and thus makes decryption process more continuous. This only applies to passwords that are required to open document because this password type is the only one that encrypts a password-protected document.

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  WYSIWYS

  In cryptography, What You See Is What You Sign (WYSIWYS) is a property of digital signature systems that ensures the semantic content of signed messages can not be changed, either by accident or intent. == Mechanism of WYSIWYS == When digitally signing a document, the integrity of the signature relies not just on the soundness of the digital signature algorithms that are used, but also on the security of the computing platform used to sign the document. The WYSIWYS property of digital signature systems aims to tackle this problem by defining a desirable property that the visual representation of a digital document should be consistent across computing systems, particularly at the points of digital signature and digital signature verification. It is relatively easy to change the interpretation of a digital document by implementing changes on the computer system where the document is being processed, and the greater the semantic distance, the easier it gets. From a semantic perspective this creates uncertainty about what exactly has been signed. WYSIWYS is a property of a digital signature system that ensures that the semantic interpretation of a digitally signed message cannot be changed, either by accident or by intent. This property also ensures that a digital document to be signed can not contain hidden semantic content that can be revealed after the signature has been applied. Though a WYSIWYS implementation is only as secure as the computing platform it is running on, various methods have been proposed to make WYSIWYS more robust. The term WYSIWYS was coined by Peter Landrock and Torben Pedersen to describe some of the principles in delivering secure and legally binding digital signatures for Pan-European projects.

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  AI Security Institute

  The AI Security Institute (AISI) is a research organisation under the Department for Science, Innovation and Technology, UK, that aims "to equip governments with a scientific understanding of the risks posed by advanced AI". It conducts research and develop and test mitigations. Previously, it was known as the AI Safety Institute. Its creation followed world's first major AI Safety Summit that was held in Bletchley Park in 2023. The institute's professed goal is "building the world's leading understanding of advanced AI risks and solutions, to inform governments so they can keep the public safe". It is designed like a startup in the government "combining the authority of government with the expertise and agility of the private sector". AISI has made access agreements with Anthropic, Google and OpenAI to test their models before release. It has an open source platform called Inspect that permits companies, governments and academics to run standardised safety tests for AI usage. Among the works AISI has done is the reported detection of multiple serious vulnerabilities that could enable development of biological weapons; the vulnerabilities were fixed before the model was launched. It conducts research on diverse fields of AI application. One study by AISI found that LLMs post-trained for political persuasiveness became systematically less accurate and up to 51% more persuasive on political issues. AISI has also worked on the usage of AI for emotional needs. It found that nearly 10 percent of UK citizens used systems like chatbots for emotional purposes on a weekly basis. It found that "systems are now outperforming PhD-level researchers on scientific knowledge tests and helping non-experts succeed at lab work that would previously have been out of reach" in a report published in December 2025. Former chief AI officer of GCHQ Adam Beaumont is the institution's interim director. UK prime minister's AI advisor Jade Leung is the chief technology officer.

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  Death of Molly Russell

  In November 2017, Molly Russell, a fourteen-year-old British schoolgirl from Harrow, London, was found dead in her bedroom by her parents. In an inquest, the coroner stated that she had died from an act of self-harm following depression and the results of social media consumption, including material on Instagram and Pinterest. She also had a Twitter account in which she documented her growing depression. == Life == Russell had been a pupil at Hatch End High School. At the inquest, the school's head teacher expressed shock that she was able to access distressing online content. Her parents stated that she had never shown any previous signs of struggle and was doing very well in school. It was revealed at the inquest that in the six months prior to her death, 2,100 of 16,300 pieces of content she had interacted with on Instagram were on topics such as self-harm, depression, and suicide. It was also noted that throughout her experience on social media, there were never any warning signs about the information she viewed on these platforms. == Subsequent events == Dr. Navin Venugopal, the child psychiatrist assigned to the case investigating her death, called the material she viewed "disturbing and distressing" and said he was unable to sleep well for weeks after viewing it. The coroner Andrew Walker concluded that Molly's death was "an act of self harm suffering from depression and the negative effects of online content". He issued a prevention of future deaths report regarding her death, in which he made a number of recommendations for operators of online platforms, including: separating platforms for adults and children age verification changes in policy on filtering of age-specific content adding features for parental supervision and control data retention of material viewed by children He suggested that this could be accomplished by either legislation or self-regulation. The lawyer representing her family at the inquest stated that the findings "captured all of the elements of why this material is so harmful." The case has been cited as a motivator for the passage of the Online Safety Act. A charity, the Molly Rose Foundation, was set up in her memory, with the goal of suicide prevention for young people. Meta and Pinterest are believed to have made substantial donations to the charity.

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  TRAME

  TRAME (TRAnsmission of MEssages) was the name of the second computer network in the world similar to the internet to be used in an electric utility. Like the internet, the base technology was packet switching; it was developed by the electric utility ENHER in Barcelona. It was deployed by the same utility, first in Catalonia and Aragón, Spain, and later in other places. Its development started in 1974 and the first routers, called nodes at that time, were deployed by 1978. The network was in operation until 2016 (38 years) with successive technological software and hardware updates. == Beginnings == In 1974, packet switching was a technology known only in research circles. The concept began in 1968 in association with the United States' Advanced Research Projects Agency (ARPA) research project ARPANET. The idea of applying the packet switching concept to electric utilities control communication networks first appeared in 1974 when the Swedish power utility Vattenfall started to create its TIDAS packet-switching network and was followed by the Spanish electric utility ENHER, which aimed to telecontrol and automate its high-voltage power grid. For this purpose, ENHER created a specific team of people to develop both the packet-switching network and the supervisory control and data acquisition (SCADA) system, also called the telecontrol system. By 1978 the first four TRAME routers were available and by 1980, eight of them were deployed and operating. The printed circuit boards (PCBs) controlling the communication lines were connected to a shared memory PCB allowing them to exchange data and messages. The project was developed together with its main initial application, the Telecontrol or SCADA system SICL (Sistema Integral de Control Local) with which initially they shared a very similar hardware. The maximum link capacity was 9600 bit/s, which in 1980 was the maximum possible on a 4 kHz wide voice channel at the time. These channels were the basic unit of the then-analog communication systems in use. By that time power utilities used either telephone calls or low speed (below 1200bit/s) dedicated links for telecontrol, typically shared among ten high-voltage electrical substations. == Services == The basic service provided by the TRAME network was SCADA or Telecontrol to automate the high-voltage power grid, thus improving operational efficiency, which was until then operated manually with telephone communication between human operators. Each TRAME router was associated with one or more remote terminal units (RTUs) of the SICL telecontrol system. It also had connected screens, and later PCs, located in electrical substations to interchange messages between them and with the Control Center located in the well-known Casa Fuster in Barcelona. It was a kind of predecessor to today's e-mail. Later, in the 1990s, other protocols (X.25, IP) were developed to include corporate information technology (IT) terminals, company physical surveillance systems and other services. Additionally, applications and terminals were developed for the transmission of voice and video over the TRAME network. == Protocols == The TRAME routing system, like that of the original ARPANET, was based on the Bellman-Ford algorithm but with "split-horizon" as in the Swedish TIDAS network, but with an original improvement. This protocol allows optimal paths to be found in meshed networks for each packet to be transmitted, allowing the shared use of the same network by multiple services. In contrast, traditional circuit-switched technology used to establish dedicated circuits for each service or communication. The addressing of routers and terminals used a proprietary system with a 16-bit address; it would be the equivalent of the well-known IP (Internet Protocol) version 4 (IPv4), still in use on the internet today, which uses 32-bit addresses. It is necessary to take into account that in 1978, the IPv4 protocol did not yet exist since the IPv4 version used on the internet did not appear until 1981, and in fact, did not reach the general public until much later. The line protocols were also proprietary and were called UCL (Unidad de Control de Línea, 'line control unit'), which linked the routers together, and UTR (Unión TRAME-Remotas), the access protocol. They were designed to offer the highest quality of service required by the telecontrol/SCADA function in terms of data integrity and availability set by the International Electrotechnical Commission (IEC) IEC-870-5-1 and ANSI C37.1. standards, and because the protocol used at the time in corporate computer networks, HDLC (high-level data link control), did not offer enough quality for critical industrial applications. Later on, other protocols like X.25 and IP were also made compatible with the aforementioned TRAME protocols. In 2000, the UTR protocol was replaced by the international standard IEC 60870- 5-101/104. Initially network flow control was based on the management of eight data priorities in head-of-the-line (HOL) waiting queues. Later and after some experimentation, a flow control method based on a bit indicating route congestion and management of the gap between packets when accessing the network was adopted. This required measuring the capacity of the route bottleneck. An end-to-end protocol was also added for some flows requiring order preservation like X.25. == Evolution == To last for 38 years, the technology had to endure intense evolution. There were essentially four TRAME generations which are summarized in the table. A description of the four generations of TRAME is provided below. === TRAME 1 === The project began in 1974 and in 1978 a first network with four routers was already installed and in operation at the electric utility ENHER. In 1980, the network had eight nodes in operation (see Figure I). The hardware was based on the Zilog Z80 processor and had a multiprocessor structure with 16 processors sharing a common memory. The software was developed at ENHER's headquarters located in the well-known Casa Fuster, Passeig de Gràcia, 132, Barcelona, using the Z80 assembly language. Beyond 1980 the software began to be written in C programming language and an HP64000 Logic Development System emulator was used for the purpose. The hardware was produced by ISEL, an INI (Instituto Nacional de Indústria) company. The routing system was a variant of Bellman-Ford with split-horizon. It was an improvement of the original ARPA network routing system consisting of an original update procedure which allowed for a faster reaction to changes. The distance function was the number of packets in the output waiting queues plus one. The line protocols (UCL for internal lines linking routers and UTR for accessing the network) were designed to meet the stringent requirements set for telecontrol (SCADA) of high-voltage power networks (IEC-870-5-1 and ANSI C37.1 standards). At the OSI transport layer, windows with a width of 1 to 8, depending on the required service, residing in the terminals were used. Initially, addresses were only 14 bits long to address both the routers (called nodes by then) and the devices connected to them. They were made up of two fields, an 8-bit field to address the router and a 6-bit sub-address to address the terminals connected to it. The node address was assigned to the nodes and not to the ends of the links as in the internet. The basic advantages of TRAME over other technologies used in electric utilities at the time were in part due to the packet technology itself: ability to manage any network topology, automatic adaptability to topological and traffic changes, integration of different link technologies (digital or analog) and capacities in a single network, open and decentralized intercommunicability between users and devices, simultaneous communication with several users and locations from a single physical connection, and integrated network supervision. In fact, the network was provided from its inception with a supervision center consisting of a computer and a synoptic board located at the company's headquarters (see Figure II). But other advantages were due to the specific design of TRAME: high data integrity, priority support for packets, and ease of including special protocols such as the many SCADA protocols in use at that time. All of the above resulted in improved quality of service, especially with respect to data availability and data integrity, and in the integration of services in a single network. Part of the evolution of its deployment can be seen in Figures II to IV. === TRAME 2 === In 1990, TRAME 2 was fully deployed and TRAME 1 was replaced. The processor of the new hardware was Intel 80286 and the hardware structure and external appearance of the routers was very similar to that of TRAME 1. The software was written in C and the above-mentioned emulator continued to be used. Improvements over TRAME 1 were the introduction of the standardized X.25 access protocol

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  Social collaboration

  Social collaboration refers to processes that help multiple people or groups interact and share information to achieve common goals. Such processes find their 'natural' environment on the Internet, where collaboration and social dissemination of information are made easier by current innovations and the proliferation of the web. Sharing concepts on a digital collaboration environment often facilitates a "brainstorming" process, where new ideas may emerge due to the varied contributions of individuals. These individuals may hail from different walks of life, different cultures and different age groups, their diverse thought processes help in adding new dimensions to ideas, dimensions that previously may have been missed. A crucial concept behind social collaboration is that 'ideas are everywhere.' Individuals are able to share their ideas in an unrestricted environment as anyone can get involved and the discussion is not limited to only those who have domain knowledge. Social collaboration is also known as enterprise social networking, and the products to support it are often branded enterprise social networks (ESNs). It is important that we understand the rhythm of social collaboration. There needs to be a balance, with ease to move from focused solitary work to brainstorming for problem solving in group work. This critical balance can be achieved by creating structures or a work environment where it is not too rigid to prevent brainstorming in group work nor too loose to result in total chaos. Social collaboration should happen at the edge of chaos. Work practices should support social collaboration. The most effective environment is one that supports opportunistic planning. Opportunistic planning provides a general plan but then gives enough room for flexibility to change activities and tasks until the last moment. This way, people are able to cope up with unforeseen developments and not throwing away everything with one grand plan. == Comparison to social networking == Social collaboration is related to social networking, with the distinction that while social networking is individual-centric, social collaboration is entirely group-centric. Generally speaking, social networking means socializing for personal, professional or entertainment purposes, for example, LinkedIn and Facebook. Social collaboration, on the other hand, means working socially to achieve a common goal, for example, GitHub and Quora. Social networking services generally focus on individuals sharing messages in a more-or-less undirected way and receiving messages from many sources into a single personalized activity feed. Social collaboration services, on the other hand, focus on the identification of groups and collaboration spaces in which messages are explicitly directed at the group and the group activity feed is seen the same way by everyone. Social collaboration may refer to time-bound collaborations with an explicit goal to be completed or perpetual collaborations in which the goal is knowledge sharing (e.g. community of practice, online community). == Comparison to crowdsourcing == Social collaboration is similar to crowdsourcing as it involves individuals working together towards a common goal. Crowdsourcing is a method for harnessing specific information from a large, diverse group of people. Unlike social collaboration, which involves much communication and cooperation among a large group of people, crowdsourcing is more like individuals working towards the common goal relatively independently. Therefore, the process of working involves less communication. Andrea Grover, curator of a crowdsourcing art show, explained that collaboration among individuals is an appealing experience, because participation is "a low investment, with the possibility of a high return." == Social collaboration software == Notable social collaboration software includes Glip messaging, Google Apps, Knowledge Plaza Electronic Document System and Social Intranet, Microsoft Lync social collaboration tool for businesses, Slack, Weekdone for managers, and Wrike. == Future == Social collaboration is going to be used as a tool in companies to enhance productivity. Social workers could be able to use social collaboration tools to manage personal tasks, professional projects and social networks with other colleagues within the same organization. Social collaboration will serve as a platform to get people involved and connected. This kind of platform provides a spiritual training practice for social workers. Social collaboration software could help enhance the communication between customers and employees and build trust in the organization. When we need real-time chat, it would be excellent to include every participant in a shared and archived forum which keeps a record of important information and logs. So collaborators need not worry about losing important records while working towards the common goal. The interactive communication and synchronous environment promote understanding among colleagues. Collaboration helps in building strong relationships between workers, which in turn leads to faster problem solving. The close connection between workers and customers creates a scalable organization which naturally increases the trust and faith that customers have in the company. Therefore, the interactive customer relationship levels up customer satisfaction in ways that traditional collaboration methods cannot. Apart from its effect on the way work will be conducted in the future, social collaboration will also affect society. In the coming years social collaboration will be the driving force in societal change as more and more people work together to get their vision across to governments and governing agencies. An example of this is Change.org, an online petition tool where users can help bring their government's attention to pressing social issues that need to be addressed.

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  Weak supervision

  Weak supervision (also known as semi-supervised learning) is a paradigm in machine learning, the relevance and notability of which increased with the advent of large language models due to the large amount of data required to train them. It is characterized by using a combination of a small amount of human-labeled data (exclusively used in more expensive and time-consuming supervised learning paradigm), followed by a large amount of unlabeled data (used exclusively in unsupervised learning paradigm). In other words, the desired output values are provided only for a subset of the training data. The remaining data is unlabeled or imprecisely labeled. Intuitively, it can be seen as an exam and labeled data as sample problems that the teacher solves for the class as an aid in solving another set of problems. In the transductive setting, these unsolved problems act as exam questions. In the inductive setting, they become practice problems of the sort that will make up the exam. == Problem == The acquisition of labeled data for a learning problem often requires a skilled human agent (e.g. to transcribe an audio segment) or a physical experiment (e.g. determining the 3D structure of a protein or determining whether there is oil at a particular location). The cost associated with the labeling process thus may render large, fully labeled training sets infeasible, whereas acquisition of unlabeled data is relatively inexpensive. In such situations, semi-supervised learning can be of great practical value. Semi-supervised learning is also of theoretical interest in machine learning and as a model for human learning. == Technique == More formally, semi-supervised learning assumes a set of l {\displaystyle l} independently identically distributed examples x 1 , … , x l ∈ X {\displaystyle x_{1},\dots ,x_{l}\in X} with corresponding labels y 1 , … , y l ∈ Y {\displaystyle y_{1},\dots ,y_{l}\in Y} and u {\displaystyle u} unlabeled examples x l + 1 , … , x l + u ∈ X {\displaystyle x_{l+1},\dots ,x_{l+u}\in X} are processed. Semi-supervised learning combines this information to surpass the classification performance that can be obtained either by discarding the unlabeled data and doing supervised learning or by discarding the labels and doing unsupervised learning. Semi-supervised learning may refer to either transductive learning or inductive learning. The goal of transductive learning is to infer the correct labels for the given unlabeled data x l + 1 , … , x l + u {\displaystyle x_{l+1},\dots ,x_{l+u}} only. The goal of inductive learning is to infer the correct mapping from X {\displaystyle X} to Y {\displaystyle Y} . It is unnecessary (and, according to Vapnik's principle, imprudent) to perform transductive learning by way of inferring a classification rule over the entire input space; however, in practice, algorithms formally designed for transduction or induction are often used interchangeably. == Assumptions == In order to make any use of unlabeled data, some relationship to the underlying distribution of data must exist. Semi-supervised learning algorithms make use of at least one of the following assumptions: === Continuity / smoothness assumption === Points that are close to each other are more likely to share a label. This is also generally assumed in supervised learning and yields a preference for geometrically simple decision boundaries. In the case of semi-supervised learning, the smoothness assumption additionally yields a preference for decision boundaries in low-density regions, so few points are close to each other but in different classes. === Cluster assumption === The data tend to form discrete clusters, and points in the same cluster are more likely to share a label (although data that shares a label may spread across multiple clusters). This is a special case of the smoothness assumption and gives rise to feature learning with clustering algorithms. === Manifold assumption === The data lie approximately on a manifold of much lower dimension than the input space. In this case learning the manifold using both the labeled and unlabeled data can avoid the curse of dimensionality. Then learning can proceed using distances and densities defined on the manifold. The manifold assumption is practical when high-dimensional data are generated by some process that may be hard to model directly, but which has only a few degrees of freedom. For instance, human voice is controlled by a few vocal folds, and images of various facial expressions are controlled by a few muscles. In these cases, it is better to consider distances and smoothness in the natural space of the generating problem, rather than in the space of all possible acoustic waves or images, respectively. == History == The heuristic approach of self-training (also known as self-learning or self-labeling) is historically the oldest approach to semi-supervised learning, with examples of applications starting in the 1960s. The transductive learning framework was formally introduced by Vladimir Vapnik in the 1970s. Interest in inductive learning using generative models also began in the 1970s. A probably approximately correct learning bound for semi-supervised learning of a Gaussian mixture was demonstrated by Ratsaby and Venkatesh in 1995. == Methods == === Generative models === Generative approaches to statistical learning first seek to estimate p ( x | y ) {\displaystyle p(x|y)} , the distribution of data points belonging to each class. The probability p ( y | x ) {\displaystyle p(y|x)} that a given point x {\displaystyle x} has label y {\displaystyle y} is then proportional to p ( x | y ) p ( y ) {\displaystyle p(x|y)p(y)} by Bayes' rule. Semi-supervised learning with generative models can be viewed either as an extension of supervised learning (classification plus information about p ( x ) {\displaystyle p(x)} ) or as an extension of unsupervised learning (clustering plus some labels). Generative models assume that the distributions take some particular form p ( x | y , θ ) {\displaystyle p(x|y,\theta )} parameterized by the vector θ {\displaystyle \theta } . If these assumptions are incorrect, the unlabeled data may actually decrease the accuracy of the solution relative to what would have been obtained from labeled data alone. However, if the assumptions are correct, then the unlabeled data necessarily improves performance. The unlabeled data are distributed according to a mixture of individual-class distributions. In order to learn the mixture distribution from the unlabeled data, it must be identifiable, that is, different parameters must yield different summed distributions. Gaussian mixture distributions are identifiable and commonly used for generative models. The parameterized joint distribution can be written as p ( x , y | θ ) = p ( y | θ ) p ( x | y , θ ) {\displaystyle p(x,y|\theta )=p(y|\theta )p(x|y,\theta )} by using the chain rule. Each parameter vector θ {\displaystyle \theta } is associated with a decision function f θ ( x ) = argmax y p ( y | x , θ ) {\displaystyle f_{\theta }(x)={\underset {y}{\operatorname {argmax} }}\ p(y|x,\theta )} . The parameter is then chosen based on fit to both the labeled and unlabeled data, weighted by λ {\displaystyle \lambda } : argmax Θ ( log ⁡ p ( { x i , y i } i = 1 l | θ ) + λ log ⁡ p ( { x i } i = l + 1 l + u | θ ) ) {\displaystyle {\underset {\Theta }{\operatorname {argmax} }}\left(\log p(\{x_{i},y_{i}\}_{i=1}^{l}|\theta )+\lambda \log p(\{x_{i}\}_{i=l+1}^{l+u}|\theta )\right)} === Low-density separation === Another major class of methods attempts to place boundaries in regions with few data points (labeled or unlabeled). One of the most commonly used algorithms is the transductive support vector machine, or TSVM (which, despite its name, may be used for inductive learning as well). Whereas support vector machines for supervised learning seek a decision boundary with maximal margin over the labeled data, the goal of TSVM is a labeling of the unlabeled data such that the decision boundary has maximal margin over all of the data. In addition to the standard hinge loss ( 1 − y f ( x ) ) + {\displaystyle (1-yf(x))_{+}} for labeled data, a loss function ( 1 − | f ( x ) | ) + {\displaystyle (1-|f(x)|)_{+}} is introduced over the unlabeled data by letting y = sign ⁡ f ( x ) {\displaystyle y=\operatorname {sign} {f(x)}} . TSVM then selects f ∗ ( x ) = h ∗ ( x ) + b {\displaystyle f^{}(x)=h^{}(x)+b} from a reproducing kernel Hilbert space H {\displaystyle {\mathcal {H}}} by minimizing the regularized empirical risk: f ∗ = argmin f ( ∑ i = 1 l ( 1 − y i f ( x i ) ) + + λ 1 ‖ h ‖ H 2 + λ 2 ∑ i = l + 1 l + u ( 1 − | f ( x i ) | ) + ) {\displaystyle f^{}={\underset {f}{\operatorname {argmin} }}\left(\displaystyle \sum _{i=1}^{l}(1-y_{i}f(x_{i}))_{+}+\lambda _{1}\|h\|_{\mathcal {H}}^{2}+\lambda _{2}\sum _{i=l+1}^{l+u}(1-|f(x_{i})|)_{+}\right)} An exact solution is intractable due to the non-convex term ( 1 − | f ( x ) | ) + {\displayst

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  Blinding (cryptography)

  In cryptography, blinding first became known in the context of blind signatures, where the message author blinds the message with a random blinding factor, the signer then signs it and the message author "unblinds" it; signer and message author are different parties. Since the late 1990s, blinding mostly refers to countermeasures against side-channel attacks on encryption devices, where the random blinding and the "unblinding" happen on the encryption devices. The techniques used for blinding signatures were adapted to prevent attackers from knowing the input to the modular exponentiation function for Diffie-Hellman or RSA. Blinding must be applied with care, for example Rabin–Williams signatures. If blinding is applied to the formatted message but the random value does not honor Jacobi requirements on p and q, then it could lead to private key recovery. A demonstration of the recovery can be seen in CVE-2015-2141 discovered by Evgeny Sidorov. Side-channel attacks allow an adversary to recover information about the input to a cryptographic operation within an asymmetric encryption scheme, by measuring something other than the algorithm's result, e.g., power consumption, computation time, or radio-frequency emanations by a device. Typically these attacks depend on the attacker knowing the characteristics of the algorithm, as well as (some) inputs. In this setting, blinding serves to alter the algorithm's input into some unpredictable state. Depending on the characteristics of the blinding function, this can prevent some or all leakage of useful information. Note that security depends also on the resistance of the blinding functions themselves to side-channel attacks. == Examples == In RSA blinding involves computing the blinding operation E(x) = (xr)e mod N, where r is a random integer between 1 and N and relatively prime to N (i.e. gcd(r, N) = 1), x is the plaintext, e is the public RSA exponent and N is the RSA modulus. As usual, the decryption function f(z) = zd mod N is applied thus giving f(E(x)) = (xr)ed mod N = xr mod N. Finally it is unblinded using the function D(z) = zr−1 mod N. Multiplying xr mod N by r−1 mod N yields x, as desired. When decrypting in this manner, an adversary who is able to measure time taken by this operation would not be able to make use of this information (by applying timing attacks RSA is known to be vulnerable to) as they does not know the constant r and hence has no knowledge of the real input fed to the RSA primitives. Blinding in GPG 1.x

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  Cryptographic Service Provider

  A cryptographic service provider (CSP) is a package that "provides a concrete implementation of certain cryptographic services." A CSP offers operations and protocols to support a variety of use cases. The cryptographic application programming interface (API) provided by the CSP provides common solutions for different platforms, for example hardware and cloud services. == Microsoft Windows == In Microsoft Windows, a Cryptographic Service Provider is a software library that implements the Microsoft CryptoAPI (CAPI). CSPs implement encoding and decoding functions, which computer application programs may use, for example, to implement strong user authentication or for secure email. CSPs are independent modules that can be used by different applications. A user program calls CryptoAPI functions and these are redirected to CSPs functions. Since CSPs are responsible for implementing cryptographic algorithms and standards, applications do not need to be concerned about security details. Furthermore, each application can define which CSP it is going to use on its calls to CryptoAPI. In fact, all cryptographic activity is implemented in CSPs. CryptoAPI only works as a bridge between the application and the CSP. CSPs are implemented basically as a special type of DLL with special restrictions on loading and use. Every CSP must be digitally signed by Microsoft and the signature is verified when Windows loads the CSP. In addition, after being loaded, Windows periodically re-scans the CSP to detect tampering, either by malicious software such as computer viruses or by the user him/herself trying to circumvent restrictions (for example on cryptographic key length) that might be built into the CSP's code. To obtain a signature, non-Microsoft CSP developers must supply paperwork to Microsoft promising to obey various legal restrictions and giving valid contact information. As of circa 2000, Microsoft did not charge any fees to supply these signatures. For development and testing purposes, a CSP developer can configure Windows to recognize the developer's own signatures instead of Microsoft's, but this is a somewhat complex and obscure operation unsuitable for nontechnical end users. The CAPI/CSP architecture had its origins in the era of restrictive US government controls on the export of cryptography. Microsoft's default or "base" CSP then included with Windows was limited to 512-bit RSA public-key cryptography and 40-bit symmetric cryptography, the maximum key lengths permitted in exportable mass market software at the time. CSPs implementing stronger cryptography were available only to U.S. residents, unless the CSPs themselves had received U.S. government export approval. The system of requiring CSPs to be signed only on presentation of completed paperwork was intended to prevent the easy spread of unauthorized CSPs implemented by anonymous or foreign developers. As such, it was presented as a concession made by Microsoft to the government, in order to get export approval for the CAPI itself. After the Bernstein v. United States court decision establishing computer source code as protected free speech and the transfer of cryptographic regulatory authority from the U.S. State Department to the more pro-export Commerce Department, the restrictions on key lengths were dropped, and the CSPs shipped with Windows now include full-strength cryptography. The main use of third-party CSPs is to interface with external cryptography hardware such as hardware security modules (HSM) or smart cards. === Smart Card CSP === These cryptographic functions can be realized by a smart card, thus the Smart Card CSP is the Microsoft way of a PKCS#11. Microsoft Windows is identifying the correct Smart Card CSP, which have to be used, analyzing the answer to reset (ATR) of the smart card, which is registered in the Windows Registry. Installing a new CSP, all ATRs of the supported smart cards are enlisted in the registry. === Use of CSP in MS Office password protection === Cryptographic service providers can be used for encryption of Word, Excel, and PowerPoint documents starting from Microsoft Office XP. A standard encryption algorithm with a 40-bit key is used by default, but enabling a CSP enhances key length and thus makes decryption process more continuous. This only applies to passwords that are required to open document because this password type is the only one that encrypts a password-protected document.

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