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  FloodAlerts

  FloodAlerts is a software application, developed by software specialists Shoothill, which takes real-time flooding information, and displays the data on an interactive Bing map, updating and warning its users when they, their premises or the routes they need to travel could be at risk of flooding. == History == FloodAlerts was launched in 2012, originally as the world's first Facebook flood warning app. == Operation == FloodAlerts is made available free of charge to individuals. Users are able to set up their own monitored locations and receive alerts via the application or their Facebook wall if the locations they are monitoring are at imminent risk of flooding. Hosted in the Cloud, using the Microsoft Windows Azure platform, the FloodAlerts application processes the data received from the Environment Agency, automatically creates the required map tiles, pins and alerts and displays them on an interactive Bing map, updating the content every 15 minutes. Users are able to see the latest information on the map without having to refresh their browser. FloodAlerts can also be provided as a customised risk management solution to businesses that require infrastructure or asset safety monitoring in areas where water levels are rising or receding. == Awards and recognition == FloodAlerts has received The Guardian and Virgin Media Business's 2012 Innovation Nation Awards and was shortlisted as a finalist for a further two national awards: the UK IT Industry Awards for Innovation and Entrepreneurship and The Institution of Engineering and Technology Innovation Awards for Information Technology. == In the press == The FloodAlerts application was reviewed on the BBC website. It was also reviewed on BBC Click.

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  Take Us to Your Chief: and Other Stories

  Take Us to Your Chief: and Other Stories is a collection of nine short stories by Canadian author, playwright, and journalist Drew Hayden Taylor published in 2016 by Douglas & McIntyre. Taylor, who is part Caucasian, part Ojibwe, explains in the acknowledgments section of the book that the origin of the project lies in several failed attempts "to compile an anthology of Native sci-fi from Canada’s best First Nations writers." The stories explore contemporary First Nations social issues through employing a number of 1950s-era science fiction tropes and themes in these stories, including time travel, alien contact, and superpowers. Many reviews of the books have noted Taylor's use of humor to examine dark subject matter, such as the heritage of Canadian Indian residential schools, First Nations suicide rates, or the water quality crisis on Canadian reserves. == The Stories == "Andrei nas" "I Am...Am I" "Lost in Space" "Dreams of Doom" "Mr. Gizmo" "Petropaths" "Stars" "Superdisappointed" "Take Us to Your Chief" == Story summaries == === Foreword === In his foreword, Taylor describes the genesis of Take Us to Your Chief: and Other Stories and invites readers into, in his term, a “new terra nullius.” He begins by describing his biracial upbringing and heritage. He points out that First Nations people are rarely associated with technology or science fiction, in part because Indigenous peoples were often at a technological disadvantage against European colonizers. He references the few examples that he can think of from popular culture, such as the Star Trek episode called “The Paradise Syndrome,” in which First Nations people are portrayed as stereotypical Indians in hippie clothing. He also elaborates on his fascination with the world of sci-fi, which first started in comic books. He enjoyed the literary work of H.G. Wells, such as The Time Machine and The Invisible Man. Since sci-fi is a world of endless opportunities, he intends that these short stories help people explore science fiction through Native peoples’ minds, something that needs to be explored more thoroughly. === "A Culturally Inappropriate Armageddon" === “A Culturally Inappropriate Armageddon” is set on a Haudenosaunee reserve, towards the end of the Oka Crisis, with a handful of people that work at its first ever radio station, C-RES, which opens in 1991. Part 1, titled “C-Res Is on the Air,” depicts Emily, Aaron, and Tracey on their first days at the station. Within the group, there is a constant debate between broadcasting popular programming, including science fiction and film reviews, and culturally-relevant programming meant to aid in cultural revitalization efforts. One night, Aaron is late to work but once he shows up he can't stop talking about radio transmissions broadcasting into deep space, an event that has been occurring since the initial discovery of the radio waves by Heinrich Hertz. The story then skips ahead seven years to 1998, when Emily is struggling to find better content for her station until Tracey stumbles upon an old anthropological record named “The Calling Song” that they decide to broadcast to their audience. The story then jumps to the year 2018 where they are all huddled around a television watching a news station reporting that extraterrestrial life is heading towards them. The discussion of what is going to happen comes into the picture and they all decide it would either be like Contact or The Day the Earth Stood Still. A year later in 2019, the aliens have invaded the planet and destroyed everything. As the three former radio station employees suffer from radioactive fallout, they realize that the aliens received the broadcast of “The Calling Song” and took it as a message to come to Earth. They thus realize that the Haudenosaunee people were inadvertently responsible for the destruction of the Earth. Part 2, titled “Old Men and Old Sayings,” tells us of an elderly man that is watching the news and listening to the radio about a spaceship coming to earth. He knows that he and everyone will die, but the people around him are excited. He finds a book on his night stand and flips to a page where he underlined a sentence a long time ago about the European colonization of the Americas. That sentence reads “those who cannot remember the past are condemned to repeat it” (23). He closes the book and Taylor concludes the story by writing, “he hated it when white people were right." === "I Am...Am I" === “I Am...Am I” chronicles the accidental creation and unexpected ending of artificial intelligence. Professor Mark King has a plethora of degrees and works for a research firm called FUTUREVISION. One night as Professor King searches the lab for his car keys—a common occurrence for him—he notices something unusual in the Matrix room. He reads on a computer the phrase “I am.” First believing it to be a prank, King later comes to the realization that his Matrix project has evolved into a responsive Artificial Intelligence. After this realization, Professor King calls his peer Dr. Gayle Chambers to further investigate this miraculous event. After receiving approval from their superiors, Professor King and Dr. Chambers move forward in feeding the AI information, with Chambers serving as the lead communicator. With more information, it becomes increasingly concerned with its own existence and the concept of whether it has a soul. After several days of conversation with the AI, Chambers and King begin to feel uneasy about the AI's responses, which show signs of neuroses. Despite this behavior, Chambers decides to feed the AI information about the culture and history of the human race. Upon receiving this information, the AI becomes obsessed with Indigenous spirituality prior to the colonization of the Americas, and it requests more information on First Nations people. Dr. Chambers is hesitant at first, but gives in and continues to feed the AI the information with the intention to return to it in the morning. This leads to the AI finding out about colonization and genocide of Indigenous peoples. Upon her arrival the next day, Chambers discovers that the code for the AI has been completely wiped from the hard drive and a single message is left on the screen—"I was”—that signifies the AI's suicide. === "Lost in Space" === "Lost in Space" is told from the perspective of Mitchell, an Anishinabe astrosurveyor who is aboard a space shuttle on a two-year tour collecting rocks from an asteroid belt. He is accompanied by an Artificial general intelligence named Mac, short for “machine.” Mac is aboard this tour in order to accompany Mitchell and keep him sane; however, his company is a burden because for Mitchell, “true space exploration consists largely of boredom.” In the midst of Mitchell seeking a way to occupy his downtime, Mac interrupts with news about his grandfather, Papa Peter, dying. Papa Peter was Mitchell's only real tie to his Indigenous identity. After receiving the news Mitchell begins to reminisce on all of the things Papa Peter had taught him throughout his life. He constantly posed questions concerning the world above (Father Sky) and how it is more important than the land they live on (Mother Earth), which eventually led Mitchell to the selection of his career. During his state of mourning, Mitchell begins to go through all the videos his grandfather had sent him throughout his space tours. Papa Peter had sent Mitchell videos from Otter Lake, a First Nations reserve; these videos are about controversial topics regarding being both native and an astronaut. In the midst of Mitchell's grieving, Mac tries to relieve the situation by finding an online video of Mitchell's grandfather participating in a drum ceremony at Ottawa’s National Aboriginal Day festival. He reconnects to his roots and his grandfather’s spirit as he listens to the Indigenous music by feeling the drum beat and humming along. Mac’s small act of kindness leads Mitchell to gain a new-found appreciation for his presence. Mitchell feels responsible to moving forward in his life in memory of Papa Peter. === "Dreams of Doom" === "Dreams of Doom" is narrated by an Ojibway reporter named Pamela Wanishin who works for an aboriginal newspaper called the West Wind. One day she receives a mysterious package with a broken dreamcatcher and a flash drive containing highly classified files. As she reads the files, she keeps seeing the term “Project Nightlight,” and out of curiosity, she Googles it. Once she Googles this, she is contacted by a nameless agent from Indigenous and Northern Affairs Canada and told that she must be relocated because the knowledge she now possesses must never be released to the public. She quickly flees the area to a cabin at Otter Lake, owned by a family member, to lie low for a few days. Eventually, the government organization tracks her down using drones, which forces her to fight back and flee once again. Pamela then runs to her friend and coworker Sally's hous

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  Random-fuzzy variable

  In measurements, the measurement obtained can suffer from two types of uncertainties. The first is the random uncertainty which is due to the noise in the process and the measurement. The second contribution is due to the systematic uncertainty which may be present in the measuring instrument. Systematic errors, if detected, can be easily compensated as they are usually constant throughout the measurement process as long as the measuring instrument and the measurement process are not changed. But it can not be accurately known while using the instrument if there is a systematic error and if there is, how much? Hence, systematic uncertainty could be considered as a contribution of a fuzzy nature. This systematic error can be approximately modeled based on our past data about the measuring instrument and the process. Statistical methods can be used to calculate the total uncertainty from both systematic and random contributions in a measurement. However, the computational complexity is very high, and hence not desirable. L.A.Zadeh introduced the concepts of fuzzy variables and fuzzy sets. Fuzzy variables are based on the theory of possibility and hence are possibility distributions. This makes them suitable to handle any type of uncertainty, i.e., both systematic and random contributions to the total uncertainty. Random-fuzzy variable (RFV) is a type 2 fuzzy variable, defined using the mathematical possibility theory, used to represent the entire information associated to a measurement result. It has an internal possibility distribution and an external possibility distribution called membership functions. The internal distribution is the uncertainty contributions due to the systematic uncertainty and the bounds of the RFV are because of the random contributions. The external distribution gives the uncertainty bounds from all contributions. == Definition == A random-fuzzy Variable (RFV) is defined as a type 2 fuzzy variable which satisfies the following conditions: Both the internal and the external functions of the RFV can be identified. Both the internal and the external functions are modeled as possibility distributions (PD). Both the internal and external functions have a unitary value for possibility to the same interval of values. An RFV can be seen in the figure. The external membership function is the distribution in blue and the internal membership function is the distribution in red. Both the membership functions are possibility distributions. Both the internal and external membership functions have a unitary value of possibility only in the rectangular part of the RFV. Therefore, all three conditions have been satisfied. If there are only systematic errors in the measurement, then the RFV simply becomes a fuzzy variable which consists of just the internal membership function. Similarly, if there is no systematic error, then the RFV becomes a fuzzy variable with just the random contributions and therefore, is just the possibility distribution of the random contributions. == Construction == A random-fuzzy variable can be constructed using an internal possibility distribution (rinternal) and a random possibility distribution (rrandom). === The random distribution (rrandom) === rrandom is the possibility distribution of the random contributions to the uncertainty. Any measurement instrument or process suffers from random error contributions due to intrinsic noise or other effects. This is completely random in nature and is a normal probability distribution when several random contributions are combined according to the central limit theorem. However, there can also be random contributions from other probability distributions, such as a uniform distribution, gamma distribution and so on. The probability distribution can be modeled from the measurement data. Then, the probability distribution can be used to model an equivalent possibility distribution using the maximally specific probability-possibility transformation. Some common probability distributions and the corresponding possibility distributions can be seen in the figures. === The internal distribution (rinternal) === rinternal is the internal distribution in the RFV which is the possibility distribution of the systematic contribution to the total uncertainty. This distribution can be built based on the information that is available about the measuring instrument and the process. The largest possible distribution is the uniform or rectangular possibility distribution. This means that every value in the specified interval is equally possible. This actually represents the state of total ignorance according to the theory of evidence which means it represents a scenario in which there is maximum lack of information. This distribution is used for the systematic error when we have absolutely no idea about the systematic error except that it belongs to a particular interval of values. This is quite common in measurements. However, in certain cases, it may be known that certain values have a higher or lower degrees of belief than certain other values. In this case, depending on the degrees of belief for the values, an appropriate possibility distribution could be constructed. === The construction of the external distribution (rexternal) and the RFV === After modeling the random and internal possibility distribution, the external membership function, rexternal, of the RFV can be constructed by using the following equation: where x ∗ {\displaystyle x^{}} is the mode of r random {\displaystyle r_{\textit {random}}} , which is the peak in the membership function of r r a n d o m {\displaystyle r_{random}} and Tmin is the minimum triangular norm. RFV can also be built from the internal and random distributions by considering the α-cuts of the two possibility distributions (PDs). An α-cut of a fuzzy variable F can be defined as Therefore, essentially an α-cut is the set of values for which the value of the membership function μ F ( a ) {\displaystyle \mu _{\rm {F}}(a)} of the fuzzy variable is greater than α. This gives the upper and lower bounds of the fuzzy variable F for each α-cut. The α-cut of an RFV, however, has 4 specific bounds and is given by R F V α = [ X a α , X b α , X c α , X d α ] {\displaystyle RFV^{\alpha }=[X_{a}^{\alpha },X_{b}^{\alpha },X_{c}^{\alpha },X_{d}^{\alpha }]} . X a α {\displaystyle X_{a}^{\alpha }} and X d α {\displaystyle X_{d}^{\alpha }} are the lower and upper bounds respectively of the external membership function (rexternal) which is a fuzzy variable on its own. X b α {\displaystyle X_{b}^{\alpha }} and X c α {\displaystyle X_{c}^{\alpha }} are the lower and upper bounds respectively of the internal membership function (rinternal) which is a fuzzy variable on its own. To build the RFV, let us consider the α-cuts of the two PDs i.e., rrandom and rinternal for the same value of α. This gives the lower and upper bounds for the two α-cuts. Let them be [ X L R α , X U R α ] {\displaystyle [X_{LR}^{\alpha },X_{UR}^{\alpha }]} and [ X L I α , X U I α ] {\displaystyle [X_{LI}^{\alpha },X_{UI}^{\alpha }]} for the random and internal distributions respectively. [ X L R α , X U R α ] {\displaystyle [X_{LR}^{\alpha },X_{UR}^{\alpha }]} can be again divided into two sub-intervals [ X L R α , x ∗ ] {\displaystyle [X_{LR}^{\alpha },x^{}]} and [ x ∗ , X U R α ] {\displaystyle [x^{},X_{UR}^{\alpha }]} where x ∗ {\displaystyle x^{}} is the mode of the fuzzy variable. Then, the α-cut for the RFV for the same value of α, R F V α = [ X a α , X b α , X c α , X d α ] {\displaystyle RFV^{\alpha }=[X_{a}^{\alpha },X_{b}^{\alpha },X_{c}^{\alpha },X_{d}^{\alpha }]} can be defined by Using the above equations, the α-cuts are calculated for every value of α which gives us the final plot of the RFV. A random-fuzzy variable is capable of giving a complete picture of the random and systematic contributions to the total uncertainty from the α-cuts for any confidence level as the confidence level is nothing but 1-α. An example for the construction of the corresponding external membership function (rexternal) and the RFV from a random PD and an internal PD can be seen in the following figure.

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  Oblivion (2013 film)

  Oblivion is a 2013 American epic post-apocalyptic science fiction action film produced and directed by Joseph Kosinski from a screenplay by Karl Gajdusek and Michael deBruyn, starring Tom Cruise in the main role alongside Morgan Freeman, Olga Kurylenko, Andrea Riseborough, Nikolaj Coster-Waldau, and Melissa Leo in supporting roles. Based on Kosinski's unpublished Radical Comics graphic novel of the same name, the film pays homage to 1970s sci-fi, and is a "love story" set in 2077 on an Earth desolated by an alien war; a maintenance technician on the verge of completing his mission finds a woman who survived from a space ship crash, leading him to question his purpose and discover the truth about the war. Oblivion premiered in Buenos Aires on March 26, 2013, and was released in theaters by Universal Pictures on April 19. The film grossed $286 million worldwide on a production budget of $120 million and received mixed reviews from critics. == Plot == In 2017, aliens known as Scavengers attack Earth and destroy the Moon, triggering global natural disasters. Although humanity wins the war using nuclear weapons, Earth is left uninhabitable. Sixty years later, the remnants of humanity have relocated to a colony on Saturn's moon Titan, except for Unit 49—technician Jack and his communications officer Victoria—who are scheduled to join them in two weeks. The pair oversee hydro rigs that convert seawater into fusion energy for the Tet, the last remaining human colony ship in orbit. Though Jack and Victoria are romantically involved and have had their memories erased for security reasons, Jack experiences recurring dreams of an unknown woman. He also secretly visits a hidden, verdant valley where he has built a lakeside cabin and collects relics of Earth's past. While investigating a missing drone—autonomous, highly advanced, and heavily armed machines—Jack is nearly captured by Scavengers. Later, he discovers the Scavengers are transmitting a signal into space. A NASA pod crash-lands at the signal's coordinates, carrying five humans in suspended animation, including the woman from Jack's dreams. A drone arrives and destroys four of the pods, but Jack rescues the remaining one and brings the unconscious woman to Unit 49's base. After reviving her, Jack and Victoria learn that the woman, Julia, has been in stasis aboard the Odyssey spaceship since 2017. Julia insists on recovering the ship's flight recorder. However, she and Jack are captured by Scavengers and brought to the Raven Rock Mountain Complex. Their leader, Malcolm, reveals that the Scavengers are actually surviving humans. Malcolm needs Jack to reprogram a captured drone to deliver a nuclear bomb, built from Odyssey's reactor, to the Tet. Jack refuses, so Malcolm releases him and Julia, urging him to seek the truth in the radiation zone, which is supposedly deadly and off-limits. Julia helps Jack recall that she is his wife, and fragments of his memories begin to return. When they arrive back at Unit 49, a devastated Victoria informs Sally, the Tet's mission controller, that she and Jack are no longer an "effective team." A drone activates and kills Victoria. Jack and Julia destroy the drone, but crash their aircraft inside the radiation zone. There, they encounter another version of Jack—"Jack-52"—who arrives to repair the drone. Jack subdues him, but Julia is seriously injured in the fight. Jack impersonates his clone to infiltrate Unit 52, meets Victoria-52, and steals medical supplies for Julia. They rest at his cabin. At Raven Rock, Malcolm reveals the truth: humanity lost the war, and the Tet is an alien machine intelligence harvesting Earth's resources. After the Moon's destruction, the Tet deployed thousands of clones of astronaut Jack Harper—brainwashed into obedience—to exterminate the remaining humans. Malcolm had assumed these clones were inhuman until witnessing Jack show interest in a discarded book, hinting at lingering humanity. Jack reprograms the captured drone, but it is destroyed in a surprise attack by other drones, leaving Malcolm badly wounded. Jack and Julia resolve to deliver the bomb themselves; Julia enters a stasis pod. En route, Jack listens to the Odyssey's flight recorder, which reveals the original Jack Harper and Victoria were astronauts sent to explore Titan before being confronted by the Tet. The pair were captured, but not before Jack ejected the remaining crew—including Julia—in stasis pods to protect them. Jack gains access to the Tet by claiming he is delivering Julia, as previously instructed. However, the stasis pod contains a dying Malcolm. Jack and Malcolm detonate the bomb, destroying the Tet and themselves. Julia later awakens at the cabin. Three years later, Julia lives there and it is revealed she had a daughter with Jack. A group of Raven Rock survivors arrives, alongside Jack-52, who has begun regaining fragments of his own lost identity. == Cast == Tom Cruise as Jack Harper—Tech 49, a technician who works to repair drones on Earth and questions his mission. Originally, he was the American commander of a mission en route to Titan who was captured by the Tet and cloned to fight humanity. Cruise also plays Jack Harper—Tech 52, a clone who seeks out Julia after the destruction of the Tet. Morgan Freeman as Malcolm Beech, an American veteran soldier and leader of a large community of scavengers, the human survivors of the alien Tet's attacks. Olga Kurylenko as Julia Rusakova Harper, Jack's wife and a Russian crew member on the Odyssey, who was sent back towards Earth by her husband to protect her from the initial contact with the Tet. Andrea Riseborough as Victoria "Vika" Olsen, Jack's communications partner and housemate. Originally, she was the British co-pilot of Jack's mission to Titan who was captured and cloned to assist in the Tet's war on humanity. Riseborough also plays a clone of Vika who Jack misleads to obtain medical supplies. Nikolaj Coster-Waldau as Sergeant Sykes, the main military commander of Beech's community of scavengers who is skeptical of Jack at first. Melissa Leo as the Tet, an alien artificial intelligence seeking to acquire Earth's natural resources and wipe out humanity. Leo also plays Sally, the mission director of Jack and Julia's mission to Titan; her likeness was copied by the Tet to serve as its visual and auditory representation. Zoë Bell as Kara, a soldier and member of the scavengers. == Production == === Development === Joseph Kosinski started the movie process by beginning work on a graphic novel called Oblivion featuring his story. While the completion of this would be teased to the public and the concept was used to pitch the movie, it was never finished and Kosinski claims he never intended to, stating it was "just a stage in the project [of film development]". Arvid Nelson was billed as co-writer and Radical Comics was attached as publisher. The novel was never finished; Kosinski explaining: "the partnership with Radical Comics allowed me to continue working on the story by developing a series of images and continuing to refine the story more over a period of years. Then I basically used all that development as a pitch kit to the studio. So even though we really never released it as an illustrated novel the story is being told as a film, which was always the intention." Walt Disney Pictures, which produced Kosinski's previous film Tron: Legacy (2010), acquired the Oblivion film adaptation rights from Radical Comics and Kosinski after a heated auction in August 2010. The film was a directing vehicle for Kosinski, with Barry Levine producing, and Jesse Berger executive producing. Other studios that made bids on the film were Paramount Pictures, 20th Century Fox, and Universal Pictures. Disney subsequently released the rights after realizing the PG-rated film they envisioned, in line with their family-oriented reputation, would require too many story changes. Universal, which had also bid for the original rights, then bought them from Kosinski and Radical and authorized a PG-13 film version. The film's script was originally written by Kosinski and William Monahan and underwent a first rewrite by Karl Gajdusek. When the film passed into Universal's hands, a final rewrite was done by Michael Arndt, under the pen name "Michael deBruyn". Universal was particularly appreciative of the script, saying, "It's one of the most beautiful scripts we've ever come across." The Bubble Ship operated by Cruise's main character, Jack 49, was inspired by the Bell 47 helicopter (often colloquially referred to as a "bubble cockpit" helicopter), a utilitarian 1947 vehicle with a transparent round canopy that Kosinski saw in the lobby of the Museum of Modern Art in Manhattan, and which he likened to a dragonfly. Daniel Simon, who previously worked with Kosinski as the lead vehicle designer on Tron: Legacy, was tasked with creating the Bubble Ship from this basis, incorporating elements evocative of an advanced fighter

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  Interactions Corporation

  Interactions LLC (also known as Interactions Corporation) is an American software company that develops voice and text-based virtual assistant applications for customer-service contact centers. Since September 2025, it has been a subsidiary of SoundHound AI. == History == Interactions was founded in 2004. In July 2011, the company announced a $12 million venture-capital funding round led by Sigma Partners. In November 2014, AT&T sold its "Watson" speech recognition platform and related patents to Interactions in exchange for equity. In May 2017, Interactions acquired the social media customer-engagement company Digital Roots; financial terms were not disclosed. On September 3, 2025, SoundHound AI completed its acquisition of Interactions Corporation, with the acquired company becoming a wholly owned subsidiary. == Products and services == Interactions' products have been described as automated voice portals and intelligent virtual assistants used for customer-service tasks. In 2011, Humana expanded the use of an Interactions voice portal for Medicare Part D enrollment.

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  Deepfake pornography

  Deepfake pornography is a form of non-consensual AI pornography created by altering existing photographs or videos using deepfake technology to modify the appearance of the participants. The use of deepfake pornography has sparked controversy because it involves the making and sharing of realistic videos featuring non-consenting individuals and is sometimes used for revenge porn. Many countries have criminalized this "new voyeurism" through legislative measures and technological solutions. == History == The term "deepfake" was coined in 2017 on a Reddit forum where users shared altered pornographic videos created using machine learning algorithms. It is a combination of the word "deep learning", which refers to the program used to create the videos, and "fake" meaning the videos are not real. Deepfake pornography was originally created on a small individual scale using a combination of machine learning algorithms, computer vision techniques, and AI software. The process began by gathering a large amount of source material (including both images and videos) of a person's face, and then using a deep learning model to train a Generative Adversarial Network to create a fake video that convincingly swaps the face of the source material onto the body of a pornographic performer. However, the production process has significantly evolved since 2018, with the advent of several public apps that have largely automated the process. While several AI "nudification" apps emerged on mainstream platforms like Google Play and the Apple App Store around 2023, major tech storefronts have since implemented stricter policies and automated detection to ban such software. Consequently, the proliferation of non-consensual deepfake pornography has largely shifted to decentralized websites, specialized online forums, and third-party messaging bot ecosystems. Deepfake pornography is sometimes confused with fake nude photography, but the two are mostly different. Fake nude photography typically uses non-sexual images and merely makes it appear that the people in them are nude. == Notable cases == Deepfake technology has been used to create non-consensual and pornographic images and videos of famous women. One of the earliest examples occurred in 2017 when a deepfake pornographic video of Gal Gadot was created by a Reddit user and quickly spread online. Since then, there have been numerous instances of similar deepfake content targeting other female celebrities, such as Emma Watson, Natalie Portman, and Scarlett Johansson. Johansson spoke publicly on the issue in December 2018, condemning the practice but also refusing legal action because she views the harassment as inevitable. === Rana Ayyub === In 2018, Rana Ayyub, an Indian investigative journalist, was the target of an online hate campaign stemming from her condemnation of the Indian government, specifically her speaking out against the rape of an eight-year-old Kashmiri girl. Ayyub was bombarded with rape and death threats, and had a doctored pornographic video of her circulated online. In a Huffington Post article, Ayyub discussed the long-lasting psychological and social effects this experience has had on her. She explained that she continued to struggle with her mental health and how the images and videos continued to resurface whenever she took a high-profile case. === Atrioc controversy === In 2023, Twitch streamer Atrioc stirred controversy when he accidentally revealed deepfake pornographic material featuring female Twitch streamers while on live. The influencer has since admitted to paying for AI generated porn, and apologized to the women and his fans. === Taylor Swift === In January 2024, AI-generated sexually explicit images of American singer Taylor Swift were posted on X (formerly Twitter), and spread to other platforms such as Facebook, Reddit and Instagram. One tweet with the images was viewed over 45 million times before being removed. A report from 404 Media found that the images appeared to have originated from a Telegram group, whose members used tools such as Microsoft Designer to generate the images, using misspellings and keyword hacks to work around Designer's content filters. After the material was posted, Swift's fans posted concert footage and images to bury the deepfake images, and reported the accounts posting the deepfakes. Searches for Swift's name were temporarily disabled on X, returning an error message instead. Graphika, a disinformation research firm, traced the creation of the images back to a 4chan community. A source close to Swift told the Daily Mail that she would be considering legal action, saying, "Whether or not legal action will be taken is being decided, but there is one thing that is clear: These fake AI-generated images are abusive, offensive, exploitative, and done without Taylor's consent and/or knowledge." The controversy drew condemnation from White House Press Secretary Karine Jean-Pierre, Microsoft CEO Satya Nadella, the Rape, Abuse & Incest National Network, and SAG-AFTRA. Several US politicians called for federal legislation against deepfake pornography. Later in the month, US senators Dick Durbin, Lindsey Graham, Amy Klobuchar and Josh Hawley introduced a bipartisan bill that would allow victims to sue individuals who produced or possessed "digital forgeries" with intent to distribute, or those who received the material knowing it was made non-consensually. === 2024 Telegram deepfake scandal === It emerged in South Korea in August 2024, that many teachers and female students were victims of deepfake images created by users who utilized AI technology. Journalist Ko Narin of The Hankyoreh uncovered the deepfake images through Telegram chats. On Telegram, group chats were created specifically for image-based sexual abuse of women, including middle and high school students, teachers, and even family members. Women with photos on social media platforms like KakaoTalk, Instagram, and Facebook are often targeted as well. Perpetrators use AI bots to generate fake images, which are then sold or widely shared, along with the victims' social media accounts, phone numbers, and KakaoTalk usernames. One Telegram group reportedly drew around 220,000 members, according to a Guardian report. Investigations revealed numerous chat groups on Telegram where users, mainly teenagers, create and share explicit deepfake images of classmates and teachers. The issue came in the wake of a troubling history of digital sex crimes, notably the notorious Nth Room case in 2019. The Korean Teachers Union estimated that more than 200 schools had been affected by these incidents. Activists called for a "national emergency" declaration to address the problem. South Korean police reported over 800 deepfake sex crime cases by the end of September 2024, a stark rise from just 156 cases in 2021, with most victims and offenders being teenagers. On September 21, 6,000 people gathered at Marronnier Park in northeastern Seoul to demand stronger legal action against deepfake crimes targeting women. On September 26, following widespread outrage over the Telegram scandal, South Korean lawmakers passed a bill criminalizing the possession or viewing of sexually explicit deepfake images and videos, imposing penalties that include prison terms and fines. Under the new law, those caught buying, saving, or watching such material could face up to three years in prison or fines up to 30 million won ($22,600). At the time the bill was proposed, creating sexually explicit deepfakes for distribution carried a maximum penalty of five years, but the new legislation would increase this to seven years, regardless of intent. By October 2024, it was estimated that "nudify" deep fake bots on Telegram were up to four million monthly users. === 2025–2026 Grok/X chatbot deepfake scandal === In December 2025, Bloomberg reported that X users found Grok would comply with unconsensual requests to digitally undress individuals, including minors, or show them performing sexually explicit acts. The majority of these prompts were targeted at women and girls. An analysis of 20,000 images generated by Grok between December 25, 2025 and January 1, 2026 showed 2% were of people in bikinis or transparent clothes and appeared to be 18 or younger, including 30 of "young or very young" women or girls. A separate analysis conducted over 24 hours from January 5 to 6 calculated that users had Grok create 6,700 sexually suggestive or nudified images per hour. xAI responded to requests for comment from media organizations with the automated reply, "Legacy Media Lies". The bot's image generation sparked an international backlash and calls for legal or regulatory action from officials in the European Union, United Kingdom, Poland, France, India, Malaysia, and Brazil. === Fernandes–Ulmen case === German TV presenter Collien Fernandes, filed a complaint against her ex-husband, actor Christian Ulmen, for several accusation including, ident

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  Tamarin Prover

  Tamarin Prover is a computer software program for formal verification of cryptographic protocols. It has been used to verify Transport Layer Security 1.3, ISO/IEC 9798, DNP3 Secure Authentication v5, WireGuard, and the PQ3 Messaging Protocol of Apple iMessage. Tamarin is an open source tool, written in Haskell, built as a successor to an older verification tool called Scyther. Tamarin has automatic proof features, but can also be self-guided. In Tamarin lemmas that representing security properties are defined. After changes are made to a protocol, Tamarin can verify if the security properties are maintained. The results of a Tamarin execution will either be a proof that the security property holds within the protocol, an example protocol run where the security property does not hold, or Tamarin could potentially fail to halt.

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  True Love (short story)

  "True Love" is a science fiction short story by American writer Isaac Asimov. It was first published in the February 1977 issue of American Way magazine and reprinted in the collections The Complete Robot (1982) and Robot Dreams (1986). In his autobiography In Joy Still Felt, the author states that American Way had requested a Valentine's Day story from him for its February 1977 issue, and that he wrote the story to console himself after the departure of his daughter following a visit during the 1976 Thanksgiving weekend. == Plot summary == Milton Davidson is trying to find his ideal partner. To do this, he prepares a special computer program to run on Multivac, which he calls Joe, which has access to databases covering the entire populace of the world. He hopes that Joe will find him his ideal match, based on physical parameters as supplied. Milton arranges to have the shortlisted candidates assigned to work with him for short periods, but realises that looks alone are not enough to find an ideal match. In order to correlate personalities, he speaks at great length to Joe, gradually filling Joe's databanks with information about his personality. In doing so, Joe develops the personality of Milton. Upon finding an ideal match, he arranges to have Milton arrested for malfeasance, so that Joe can 'have the girl' for himself.

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  Grokking (machine learning)

  In machine learning, grokking, or delayed generalization, is a phenomenon observed in some settings where a model abruptly transitions from overfitting (performing well only on training data) to generalizing (performing well on both training and test data), after many training iterations with little or no improvement on the held-out data. This contrasts with what is typically observed in machine learning, where generalization occurs gradually alongside improved performance on training data. == Origin == Grokking was introduced by OpenAI researcher Alethea Power and colleagues in the January 2022 paper "Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets". It is derived from the word grok coined by Robert Heinlein in his novel Stranger in a Strange Land. In ML research, "grokking" is not used as a synonym for "generalization"; rather, it names a sometimes-observed delayed‑generalization training phenomenon in which training and held‑out performance do not improve in tandem, and in which held‑out performance rises abruptly later. Authors also analyze the "grokking time", the epoch or step at which this transition occurs in those scenarios. == Interpretations == Grokking can be understood as a phase transition during the training process. In particular, recent work has shown that grokking may be due to a complexity phase transition in the model during training. While grokking has been thought of as largely a phenomenon of relatively shallow models, grokking has been observed in deep neural networks and non-neural models and is the subject of active research. One potential explanation is that the weight decay (a component of the loss function that penalizes higher values of the neural network parameters, also called regularization) slightly favors the general solution that involves lower weight values, but that is also harder to find. According to Neel Nanda, the process of learning the general solution may be gradual, even though the transition to the general solution occurs more suddenly later. Recent theories have hypothesized that grokking occurs when neural networks transition from a "lazy training" regime where the weights do not deviate far from initialization, to a "rich" regime where weights abruptly begin to move in task-relevant directions. Follow-up empirical and theoretical work has accumulated evidence in support of this perspective, and it offers a unifying view of earlier work as the transition from lazy to rich training dynamics is known to arise from properties of adaptive optimizers, weight decay, initial parameter weight norm, and more. This perspective is complementary to a unifying "pattern learning speeds" framework that links grokking and double descent; within this view, delayed generalization can arise across training time ("epoch‑wise") or across model size ("model‑wise"), and the authors report "model‑wise grokking".

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  Batch normalization

  In artificial neural networks, batch normalization (also known as batch norm) is a normalization technique used to make training faster and more stable by adjusting the inputs to each layer—re-centering them around zero and re-scaling them to a standard size. It was introduced by Sergey Ioffe and Christian Szegedy in 2015. Experts still debate why batch normalization works so well. It was initially thought to tackle internal covariate shift, a problem where parameter initialization and changes in the distribution of the inputs of each layer affect the learning rate of the network. However, newer research suggests it doesn’t fix this shift but instead smooths the objective function—a mathematical guide the network follows to improve—enhancing performance. In very deep networks, batch normalization can initially cause a severe gradient explosion—where updates to the network grow uncontrollably large—but this is managed with shortcuts called skip connections in residual networks. Another theory is that batch normalization adjusts data by handling its size and path separately, speeding up training. == Internal covariate shift == Each layer in a neural network has inputs that follow a specific distribution, which shifts during training due to two main factors: the random starting values of the network’s settings (parameter initialization) and the natural variation in the input data. This shifting pattern affecting the inputs to the network’s inner layers is called internal covariate shift. While a strict definition isn’t fully agreed upon, experiments show that it involves changes in the means and variances of these inputs during training. Batch normalization was first developed to address internal covariate shift. During training, as the parameters of preceding layers adjust, the distribution of inputs to the current layer changes accordingly, such that the current layer needs to constantly readjust to new distributions. This issue is particularly severe in deep networks, because small changes in shallower hidden layers will be amplified as they propagate within the network, resulting in significant shift in deeper hidden layers. Batch normalization was proposed to reduced these unwanted shifts to speed up training and produce more reliable models. Beyond possibly tackling internal covariate shift, batch normalization offers several additional advantages. It allows the network to use a higher learning rate—a setting that controls how quickly the network learns—without causing problems like vanishing or exploding gradients, where updates become too small or too large. It also appears to have a regularizing effect, improving the network’s ability to generalize to new data, reducing the need for dropout, a technique used to prevent overfitting (when a model learns the training data too well and fails on new data). Additionally, networks using batch normalization are less sensitive to the choice of starting settings or learning rates, making them more robust and adaptable. == Procedures == === Transformation === In a neural network, batch normalization is achieved through a normalization step that fixes the means and variances of each layer's inputs. Ideally, the normalization would be conducted over the entire training set, but to use this step jointly with stochastic optimization methods, it is impractical to use the global information. Thus, normalization is restrained to each mini-batch in the training process. Let us use B to denote a mini-batch of size m of the entire training set. The empirical mean and variance of B could thus be denoted as μ B = 1 m ∑ i = 1 m x i {\displaystyle \mu _{B}={\frac {1}{m}}\sum _{i=1}^{m}x_{i}} and σ B 2 = 1 m ∑ i = 1 m ( x i − μ B ) 2 {\displaystyle \sigma _{B}^{2}={\frac {1}{m}}\sum _{i=1}^{m}(x_{i}-\mu _{B})^{2}} . For a layer of the network with d-dimensional input, x = ( x ( 1 ) , . . . , x ( d ) ) {\displaystyle x=(x^{(1)},...,x^{(d)})} , each dimension of its input is then normalized (i.e. re-centered and re-scaled) separately, x ^ i ( k ) = x i ( k ) − μ B ( k ) ( σ B ( k ) ) 2 + ϵ {\displaystyle {\hat {x}}_{i}^{(k)}={\frac {x_{i}^{(k)}-\mu _{B}^{(k)}}{\sqrt {\left(\sigma _{B}^{(k)}\right)^{2}+\epsilon }}}} , where k ∈ [ 1 , d ] {\displaystyle k\in [1,d]} and i ∈ [ 1 , m ] {\displaystyle i\in [1,m]} ; μ B ( k ) {\displaystyle \mu _{B}^{(k)}} and σ B ( k ) {\displaystyle \sigma _{B}^{(k)}} are the per-dimension mean and standard deviation, respectively. ϵ {\displaystyle \epsilon } is added in the denominator for numerical stability and is an arbitrarily small positive constant. The resulting normalized activation x ^ ( k ) {\displaystyle {\hat {x}}^{(k)}} have zero mean and unit variance, if ϵ {\displaystyle \epsilon } is not taken into account. To restore the representation power of the network, a transformation step then follows as y i ( k ) = γ ( k ) x ^ i ( k ) + β ( k ) {\displaystyle y_{i}^{(k)}=\gamma ^{(k)}{\hat {x}}_{i}^{(k)}+\beta ^{(k)}} , where the parameters γ ( k ) {\displaystyle \gamma ^{(k)}} and β ( k ) {\displaystyle \beta ^{(k)}} are subsequently learned in the optimization process. Formally, the operation that implements batch normalization is a transform B N γ ( k ) , β ( k ) : x 1... m ( k ) → y 1... m ( k ) {\displaystyle BN_{\gamma ^{(k)},\beta ^{(k)}}:x_{1...m}^{(k)}\rightarrow y_{1...m}^{(k)}} called the Batch Normalizing transform. The output of the BN transform y ( k ) = B N γ ( k ) , β ( k ) ( x ( k ) ) {\displaystyle y^{(k)}=BN_{\gamma ^{(k)},\beta ^{(k)}}(x^{(k)})} is then passed to other network layers, while the normalized output x ^ i ( k ) {\displaystyle {\hat {x}}_{i}^{(k)}} remains internal to the current layer. === Backpropagation === The described BN transform is a differentiable operation, and the gradient of the loss l {\displaystyle l} with respect to the different parameters can be computed directly with the chain rule. Specifically, ∂ l ∂ y i ( k ) {\displaystyle {\frac {\partial l}{\partial y_{i}^{(k)}}}} depends on the choice of activation function, and the gradient against other parameters could be expressed as a function of ∂ l ∂ y i ( k ) {\displaystyle {\frac {\partial l}{\partial y_{i}^{(k)}}}} : ∂ l ∂ x ^ i ( k ) = ∂ l ∂ y i ( k ) γ ( k ) {\displaystyle {\frac {\partial l}{\partial {\hat {x}}_{i}^{(k)}}}={\frac {\partial l}{\partial y_{i}^{(k)}}}\gamma ^{(k)}} , ∂ l ∂ γ ( k ) = ∑ i = 1 m ∂ l ∂ y i ( k ) x ^ i ( k ) {\displaystyle {\frac {\partial l}{\partial \gamma ^{(k)}}}=\sum _{i=1}^{m}{\frac {\partial l}{\partial y_{i}^{(k)}}}{\hat {x}}_{i}^{(k)}} , ∂ l ∂ β ( k ) = ∑ i = 1 m ∂ l ∂ y i ( k ) {\displaystyle {\frac {\partial l}{\partial \beta ^{(k)}}}=\sum _{i=1}^{m}{\frac {\partial l}{\partial y_{i}^{(k)}}}} , ∂ l ∂ σ B ( k ) 2 = ∑ i = 1 m ∂ l ∂ y i ( k ) ( x i ( k ) − μ B ( k ) ) ( − γ ( k ) 2 ( σ B ( k ) 2 + ϵ ) − 3 / 2 ) {\displaystyle {\frac {\partial l}{\partial \sigma _{B}^{(k)^{2}}}}=\sum _{i=1}^{m}{\frac {\partial l}{\partial y_{i}^{(k)}}}(x_{i}^{(k)}-\mu _{B}^{(k)})\left(-{\frac {\gamma ^{(k)}}{2}}(\sigma _{B}^{(k)^{2}}+\epsilon )^{-3/2}\right)} , ∂ l ∂ μ B ( k ) = ∑ i = 1 m ∂ l ∂ y i ( k ) − γ ( k ) σ B ( k ) 2 + ϵ + ∂ l ∂ σ B ( k ) 2 1 m ∑ i = 1 m ( − 2 ) ⋅ ( x i ( k ) − μ B ( k ) ) {\displaystyle {\frac {\partial l}{\partial \mu _{B}^{(k)}}}=\sum _{i=1}^{m}{\frac {\partial l}{\partial y_{i}^{(k)}}}{\frac {-\gamma ^{(k)}}{\sqrt {\sigma _{B}^{(k)^{2}}+\epsilon }}}+{\frac {\partial l}{\partial \sigma _{B}^{(k)^{2}}}}{\frac {1}{m}}\sum _{i=1}^{m}(-2)\cdot (x_{i}^{(k)}-\mu _{B}^{(k)})} , and ∂ l ∂ x i ( k ) = ∂ l ∂ x ^ i ( k ) 1 σ B ( k ) 2 + ϵ + ∂ l ∂ σ B ( k ) 2 2 ( x i ( k ) − μ B ( k ) ) m + ∂ l ∂ μ B ( k ) 1 m {\displaystyle {\frac {\partial l}{\partial x_{i}^{(k)}}}={\frac {\partial l}{\partial {\hat {x}}_{i}^{(k)}}}{\frac {1}{\sqrt {\sigma _{B}^{(k)^{2}}+\epsilon }}}+{\frac {\partial l}{\partial \sigma _{B}^{(k)^{2}}}}{\frac {2(x_{i}^{(k)}-\mu _{B}^{(k)})}{m}}+{\frac {\partial l}{\partial \mu _{B}^{(k)}}}{\frac {1}{m}}} . === Inference === During the training stage, the normalization steps depend on the mini-batches to ensure efficient and reliable training. However, in the inference stage, this dependence is not useful any more. Instead, the normalization step in this stage is computed with the population statistics such that the output could depend on the input in a deterministic manner. The population mean, E [ x ( k ) ] {\displaystyle E[x^{(k)}]} , and variance, Var ⁡ [ x ( k ) ] {\displaystyle \operatorname {Var} [x^{(k)}]} , are computed as: E [ x ( k ) ] = E B [ μ B ( k ) ] {\displaystyle E[x^{(k)}]=E_{B}[\mu _{B}^{(k)}]} , and Var ⁡ [ x ( k ) ] = m m − 1 E B [ ( σ B ( k ) ) 2 ] {\displaystyle \operatorname {Var} [x^{(k)}]={\frac {m}{m-1}}E_{B}[\left(\sigma _{B}^{(k)}\right)^{2}]} . The population statistics thus is a complete representation of the mini-batches. The BN transform in the inference step thus becomes y ( k ) = B N γ ( k ) , β ( k ) inf ( x ( k ) ) = γ ( k ) x ( k ) − E [ x ( k ) ] Var ⁡ [ x ( k ) ] + ϵ + β

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  Ghost in the Shell

  Ghost in the Shell is a Japanese cyberpunk military science fiction media franchise that began with the eponymous manga series, written and illustrated by Masamune Shirow. The manga, first serialized from 1989 to 1991, is set in the mid-21st-century and follows the fictional counter-cyberterrorist organization Public Security Section 9, led by protagonist Major Motoko Kusanagi. Animation studio Production I.G has produced several anime adaptations of the series. These include the 1995 film of the same name and its 2004 sequel, Ghost in the Shell 2: Innocence; the 2002 television series Ghost in the Shell: Stand Alone Complex and its 2020 follow-up, Ghost in the Shell: SAC_2045; and the Ghost in the Shell: Arise original video animation series. In addition, an American-produced live-action film was released in March 2017. == Overview == === Title === The original editor Koichi Yuri says: At first, Ghost in the Shell came from Shirow, but when Yuri asked for "something more flashy", Shirow came up with "攻殻機動隊 Koukaku Kidou Tai (Shell Squad)" for Yuri. But Shirow was attached to including "Ghost in the Shell" as well even if in smaller type. === Setting === Primarily set in the mid-twenty-first century in the fictional Japanese city of Niihama, Niihama Prefecture (新浜県新浜市, Niihama-ken Niihama-shi), otherwise known as New Port City (ニューポートシティ, Nyū Pōto Shiti), the manga and the many anime adaptations follow the members of Public Security Section 9, a task-force consisting of various professionals skilled at solving and preventing crime, mostly with some sort of police background. Political intrigue and counter-terrorism operations are standard fare for Section 9, but the various actions of corrupt officials, companies, and cyber-criminals in each scenario are unique and require the diverse skills of Section 9's staff to prevent a series of incidents from escalating. In this post-cyberpunk iteration of a possible future, computer technology has advanced to the point that many members of the public possess cyberbrains, technology that allows them to interface their biological brain with various networks. The level of cyberization varies from simple minimal interfaces to almost complete replacement of the brain with cybernetic parts, in cases of severe trauma. This can also be combined with various levels of prostheses, with a fully prosthetic body enabling a person to become a cyborg. The main character of Ghost in the Shell, Major Motoko Kusanagi, is such a cyborg, having had a terrible accident befall her as a child that ultimately required her to use a full-body prosthesis to house her cyberbrain. This high level of cyberization, however, opens the brain up to attacks from highly skilled hackers, with the most dangerous being those who will hack a person to bend to their whims. == Media == === Literature === ==== Original manga ==== The original Ghost in the Shell manga ran in Japan from April 1989 to November 1990 in Kodansha's manga anthology Young Magazine, and was released in a tankōbon volume on October 2, 1991. Ghost in the Shell 2: Man-Machine Interface followed in 1997 for nine issues in Young Magazine, and was collected in the Ghost in the Shell: Solid Box on December 1, 2000. Then a standard version with modifications and new pages was published on June 26, 2001. Four stories from Man-Machine Interface that were not released in tankobon format from previous releases were later collected in Ghost in the Shell 1.5: Human-Error Processor, and published by Kodansha on July 17, 2003. Several art books have also been published for the manga. === Films === ==== Animated films ==== Two animated films based on the original manga have been released, both directed by Mamoru Oshii and animated by Production I.G. Ghost in the Shell was released in 1995 and follows the "Puppet Master" storyline from the manga. It was re-released in 2008 as Ghost in the Shell 2.0 with new audio and updated 3D computer graphics in certain scenes. Innocence, otherwise known as Ghost in the Shell 2: Innocence, was released in 2004, with its story based on a chapter from the first manga. ==== Live-action film ==== In 2008, DreamWorks and producer Steven Spielberg acquired the rights to a live-action film adaptation of the original Ghost in the Shell manga. On January 24, 2014, Rupert Sanders was announced as director, with a screenplay by William Wheeler. In April 2016, the full cast was announced, which included Juliette Binoche, Chin Han, Lasarus Ratuere and Kaori Momoi, and Scarlett Johansson in the lead role; the casting of Johansson drew accusations of whitewashing. Principal photography on the film began on location in Wellington, New Zealand, on February 1, 2016. Filming wrapped in June 2016. Ghost in the Shell premiered in Tokyo on March 16, 2017, and was released in the United States on March 31, 2017, in 2D, 3D and IMAX 3D. It received mixed reviews, with praise for its visuals and Johansson's performance but criticism for its script. === Television === ==== Stand Alone Complex TV series, film and ONA ==== In 2002, Ghost in the Shell: Stand Alone Complex premiered on Animax, presenting a new telling of Ghost in the Shell independent from the original manga, focusing on Section 9's investigation of the Laughing Man hacker. It was followed in 2004 by a second season titled Ghost in the Shell: S.A.C. 2nd GIG, which focused on the Individual Eleven terrorist group. The primary storylines of both seasons were compressed into OVAs broadcast as Ghost in the Shell: Stand Alone Complex The Laughing Man in 2005 and Ghost in the Shell: Stand Alone Complex Individual Eleven in 2006. Also in 2006, Ghost in the Shell: Stand Alone Complex - Solid State Society, featuring Section 9's confrontation with a hacker known as the Puppeteer, was broadcast, serving as a finale to the anime series. The extensive score for the series and its films was composed by Yoko Kanno. On April 7, 2017, Kodansha and Production I.G announced that Kenji Kamiyama and Shinji Aramaki would be co-directing a new Kōkaku Kidōtai anime production. On December 7, 2018, it was reported by Netflix that they had acquired the worldwide streaming rights to the original net animation (ONA) anime series, titled Ghost in the Shell: SAC_2045, and that it would premiere on April 23, 2020. The series is in 3DCG and Sola Digital Arts collaborated with Production I.G on the project. Ilya Kuvshinov handled character designs. The series had two seasons of 12 episodes each. In addition to the anime, a series of published books, two separate manga adaptations, and several video games for consoles and mobile phones have been released for Stand Alone Complex. ==== Arise OVA, TV series and film ==== In 2013, a new iteration of the series titled Ghost in the Shell: Arise premiered, taking an original look at the Ghost in the Shell world, set before the original manga. It was released as a series of four original video animation (OVA) episodes (with limited theatrical releases) from 2013 to 2014, then recompiled as a 10-episode television series under the title of Kōkaku Kidōtai: Arise - Alternative Architecture. An additional fifth OVA titled Pyrophoric Cult, originally premiering in the Alternative Architecture broadcast as two original episodes, was released on August 26, 2015. Kazuchika Kise served as the chief director of the series, with Tow Ubukata as head writer. Cornelius was brought onto the project to compose the score for the series, with the Major's new voice actress Maaya Sakamoto also providing vocals for certain tracks. Ghost in the Shell: The New Movie, also known as Ghost in the Shell: Arise − The Movie or New Ghost in the Shell, is a 2015 film directed by Kazuya Nomura that serves as a finale to the Ghost in the Shell: Arise story arc. The film is a continuation to the plot of the Pyrophoric Cult episode of Arise, and ties up loose ends from that arc. A manga adaptation was serialized in Kodansha's Young Magazine, which started on March 13 and ended on August 26, 2013. ==== 2026 anime ==== On May 25, 2024, it was announced that a new anime television series adaptation will be produced by Science Saru for a July 2026 premiere. Saru will be in a production committee with Bandai Namco Filmworks, Kodansha and Production I.G. The series will be directed by Monkochan, with a script by EnJoe Toh. === Video games === Ghost in the Shell was developed by Exact and released for the PlayStation on July 17, 1997, in Japan by Sony Computer Entertainment. It is a third-person shooter featuring an original storyline where the character plays a rookie member of Section 9. The video game's soundtrack Megatech Body features various techno artists, such as Takkyu Ishino, Scan X and Mijk Van Dijk. Several video games were also developed to tie into the Stand Alone Complex television series, in addition to a first-person shooter by Nexon and Neople titled Ghost in the Shell: Stand Alone Complex - First Assault Online,

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  Tamarin Prover

  Tamarin Prover is a computer software program for formal verification of cryptographic protocols. It has been used to verify Transport Layer Security 1.3, ISO/IEC 9798, DNP3 Secure Authentication v5, WireGuard, and the PQ3 Messaging Protocol of Apple iMessage. Tamarin is an open source tool, written in Haskell, built as a successor to an older verification tool called Scyther. Tamarin has automatic proof features, but can also be self-guided. In Tamarin lemmas that representing security properties are defined. After changes are made to a protocol, Tamarin can verify if the security properties are maintained. The results of a Tamarin execution will either be a proof that the security property holds within the protocol, an example protocol run where the security property does not hold, or Tamarin could potentially fail to halt.

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  Situated

  In artificial intelligence and cognitive science, the term situated refers to an agent which is embedded in an environment. The term situated is commonly used to refer to robots, but some researchers argue that software agents can also be situated if: they exist in a dynamic (rapidly changing) environment, which they can manipulate or change through their actions, and which they can sense or perceive. Examples might include web-based agents, which can alter data or trigger processes (such as purchases) over the internet, or virtual-reality bots which inhabit and change virtual worlds, such as Second Life. Being situated is generally considered to be part of being embodied, but it is useful to consider each perspective individually. The situated perspective emphasizes that intelligent behaviour derives from the environment and the agent's interactions with it. The nature of these interactions are defined by an agent's embodiment.

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  Fuzzy markup language

  Fuzzy Markup Language (FML) is a specific purpose markup language based on XML, used for describing the structure and behavior of a fuzzy system independently of the hardware architecture devoted to host and run it. == Overview == FML was designed and developed by Giovanni Acampora during his Ph.D. course in Computer Science, at University of Salerno, Italy, in 2004. The original idea inspired Giovanni Acampora to create FML was the necessity of creating a cooperative fuzzy-based framework aimed at automatically controlling a living environment characterized by a plethora of heterogeneous devices whose interactions were devoted to maximize the human comfort under energy saving constraints. This framework represented one of the first concrete examples of Ambient Intelligence. Beyond this pioneering application, the major advantage of using XML to describe a fuzzy system is hardware/software interoperability. Indeed, all that is needed to read an FML file is the appropriate schema for that file, and an FML parser. This markup approach makes it much easier to exchange fuzzy systems between software: for example, a machine learning application could extract fuzzy rules which could then be read directly into a fuzzy inference engine or uploaded into a fuzzy controller. Also, with technologies like XSLT, it is possible to compile the FML into the programming language of your choice, ready for embedding into whatever application you please. As stated by Mike Watts on his popular Computational Intelligence blog: "Although Acampora's motivation for developing FML seems to be to develop embedded fuzzy controllers for ambient intelligence applications, FML could be a real boon for developers of fuzzy rule extraction algorithms: from my own experience during my PhD, I know that having to design a file format and implement the appropriate parsers for rule extraction and fuzzy inference engines can be a real pain, taking as much time as implementing the rule extraction algorithm itself. I would much rather have used something like FML for my work." A complete overview of FML and related applications can be found in the book titled On the power of Fuzzy Markup Language edited by Giovanni Acampora, Chang-Shing Lee, Vincenzo Loia and Mei-Hui Wang, and published by Springer in the series Studies on Fuzziness and Soft Computing. == Syntax, grammar and hardware synthesis == FML allows fuzzy systems to be coded through a collection of correlated semantic tags capable of modeling the different components of a classical fuzzy controller such as knowledge base, rule base, fuzzy variables and fuzzy rules. Therefore, the FML tags used to build a fuzzy controller represent the set of lexemes used to create fuzzy expressions. In order to design a well-formed XML-based language, an FML context-free grammar is defined by means of a XML schema which defines name, type and attributes characterized each XML element. However, since an FML program represents only a static view of a fuzzy logic controller, XSLT is provided to change this static view to a computable version. Indeed, XSLTs modules are able to convert the FML-based fuzzy controller in a general purpose computer language using an XSL file containing the translation description. At this level, the control is executable for the hardware. In short, FML is essentially composed by three layers: XML in order to create a new markup language for fuzzy logic control; a XML Schema in order to define the legal building blocks; eXtensible Stylesheet Language Transformations (XSLT) in order to convert a fuzzy controller description into a specific programming language. === Syntax === FML syntax is composed of XML tags and attributes which describe the different components of a fuzzy logic controller listed below: fuzzy knowledge base; fuzzy rule base; inference engine fuzzification subsystem; defuzzification subsystem. In detail, the opening tag of each FML program is which represents the fuzzy controller under modeling. This tag has two attributes: name and ip. The first attribute permits to specify the name of fuzzy controller and ip is used to define the location of controller in a computer network. The fuzzy knowledge base is defined by means of the tag which maintains the set of fuzzy concepts used to model the fuzzy rule base. In order to define the fuzzy concept related controlled system, tag uses a set of nested tags: defines the fuzzy concept; defines a linguistic term describing the fuzzy concept; a set of tags defining a shape of fuzzy sets are related to fuzzy terms. The attributes of tag are: name, scale, domainLeft, domainRight, type and, for only an output, accumulation, defuzzifier and defaultValue. The name attribute defines the name of fuzzy concept, for instance, temperature; scale is used to define the scale used to measure the fuzzy concept, for instance, Celsius degree; domainLeft and domainRight are used to model the universe of discourse of fuzzy concept, that is, the set of real values related to fuzzy concept, for instance [0°,40°] in the case of Celsius degree; the position of fuzzy concept into rule (consequent part or antecedent part) is defined by type attribute (input/output); accumulation attribute defines the method of accumulation that is a method that permits the combination of results of a variable of each rule in a final result; defuzzifier attribute defines the method used to execute the conversion from a fuzzy set, obtained after aggregation process, into a numerical value to give it in output to system; defaultValue attribute defines a real value used only when no rule has fired for the variable at issue. As for tag , it uses two attributes: name used to identify the linguistic value associate with fuzzy concept and complement, a boolean attribute that defines, if it is true, it is necessary to consider the complement of membership function defined by given parameters. Fuzzy shape tags, used to complete the definition of fuzzy concept, are: Every shaping tag uses a set of attributes which defines the real outline of corresponding fuzzy set. The number of these attributes depends on the chosen fuzzy set shape. In order to make an example, consider the Tipper Inference System described in Mathworks Matlab Fuzzy Logic Toolbox Tutorial. This Mamdani system is used to regulate the tipping in, for example, a restaurant. It has got two variables in input (food and service) and one in output (tip). FML code for modeling part of knowledge base of this fuzzy system containing variables food and tip is shown below. A special tag that can furthermore be used to define a fuzzy shape is . This tag is used to customize fuzzy shape (custom shape). The custom shape modeling is performed via a set of tags that lists the extreme points of geometric area defining the custom fuzzy shape. Obviously, the attributes used in tag are x and y coordinates. As for rule base component, FML allows to define a set of rule bases, each one of them describes a different behavior of system. The root of each rule base is modeled by tag which defines a fuzzy rule set. The tag uses five attributes: name, type, activationMethod, andMethod and orMethod. Obviously, the name attribute uniquely identifies the rule base. The type attribute permits to specify the kind of fuzzy controller (Mamdani or TSK) respect to the rule base at issue. The activationMethod attribute defines the method used to implication process; the andMethod and orMethod attribute define, respectively, the and and or algorithm to use by default. In order to define the single rule the tag is used. The attributes used by the tag are: name, connector, operator and weight. The name attribute permits to identify the rule; connector is used to define the logical operator used to connect the different clauses in antecedent part (and/or); operator defines the algorithm to use for chosen connector; weight defines the importance of rule during inference engine step. The definition of antecedent and consequent rule part is obtained by using and tags. tag is used to model the fuzzy clauses in antecedent and consequent part. This tag use the attribute modifier to describe a modification to term used in the clause. The possible values for this attribute are: above, below, extremely, intensify, more or less, norm, not, plus, slightly, somewhat, very, none. To complete the definition of fuzzy clause the nested and tags have to be used. A sequence of tags realizes a fuzzy rule base. As example, consider a Mamdani rule composed by (food is rancid) OR (servi

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  17776

  17776 (also known as What Football Will Look Like in the Future) is a serialized speculative fiction multimedia narrative by Jon Bois, published online through SB Nation. Set in the distant future in which all humans have become immortal and infertile, the series follows three sapient space probes that watch humanity play an evolved form of American football in which games can be played for millennia over distances of thousands of miles. The series debuted on July 5, 2017, and new chapters were published daily until the series concluded with its twenty-fifth chapter on July 15, 2017. Bois began developing 17776 in 2016. Because the story incorporates text, animated GIFs, still images, and videos hosted on YouTube, new tools were developed to allow it to be hosted efficiently on the SB Nation website. The work explores themes of consciousness, hope, despair, and why humans play sports. 17776 was well received by critics, who praised it for its innovative use of its medium and for the depth of emotion it evoked. In 2018, the story won a National Magazine Award for Digital Innovation and was longlisted for both the Hugo Awards for Best Novella and Best Graphic Story. It is followed by a sequel series: 20020, released from September to October 2020. The sequel series follows a 111-team game of college football on fields spanning 130,000 miles (210,000 km) across the United States. Bois originally intended to follow up with a further series entitled 20021; however, it was postponed indefinitely. In May 2025, Bois announced that the series would be continued with a novel titled 50007: An American Football Odyssey. == Premise == The story takes place on a future Earth where humans stopped dying, aging, and being born on April 7, 2026. All social ills were subsequently eliminated, and technology preventing humans from any injury was developed. In the United States, American football evolved to include new rules, including those that allow fields thousands of miles long, hundreds of in-game players, and games millennia long. Over time, computers gained sentience due to constant exposure to broadcast human data. By the year 17776, the space probe Pioneer 9 (called Nine) has gained sentience and made contact with Pioneer 10 (called Ten) and the Jupiter Icy Moons Explorer (called Juice). As Nine adjusts to a world radically different from that of the 20th century, the three space probes watch multiple football games occurring across the United States: a game using the entirety of Nebraska as a field in which the next point scored wins the game; a game in which players strive to possess every existing football autographed by obscure NFL player Koy Detmer; a game played between the Canadian border and the Mexican border deadlocked for 13,000 years at the bottom of a gorge in Arizona; an NFL regulation game between the Denver Broncos and the Pittsburgh Steelers that changed over 15,000 years into 58 playing teams owning and capitalizing upon portions of Sports Authority Field at Mile High while the ball is lost; a 500 game that results in the destruction of the Centennial Light; and a game in which the possessing player is attempting to score an automatic win by hiding in his team's end zone for 10,000 years. == Format == 17776 is read by scrolling through web pages occupied by large GIF images and colored dialogue text, interspersed with occasional YouTube videos. The story is divided into chapters, which were originally published in daily installments between July 5 and 15, 2017. Much of the GIF and video content of the series uses Google Earth satellite imagery, 3D buildings, and other tools within Google Earth to create animations and visual effects. == Development == Bois wrote and illustrated 17776 for Vox Media's sports news website SB Nation, of which he is creative director. Aside from 17776, Bois produces two other recurring, humorous video essay programs for the site: Pretty Good, which focuses on unusual sports topics and stories, and Chart Party, which focuses on statistics and has an emphasis on Bois' use of visual art in his journalism and storytelling. Bois is also known for the Breaking Madden series, in which he attempted unusual scenarios in the Madden NFL series of video games. In early 2016, Bois began developing an "anti-sci fi" project as a possible sequel to The Tim Tebow CFL Chronicles, an earlier work for SB Nation, and set the story in a year far enough in the future that "nobody ever thinks about it." Although he liked the concept and the visuals, he believed the project would not connect with readers and shelved it. Later, he realized that the story needed a centering character; he wrote one in the form of a small town, AM radio talk show host before coming up with the characters of the probes. Development renewed in May 2016, and the project solidified after SB Nation published its article "The Future of Football." Bois described it as the biggest project he ever attempted. The series was developed by Graham MacAree, who used a Vox Media tool that creates custom packages from standard article sets to give Bois creative leeway and to accommodate the series' weight on the SB Nation website. MacAree found that there were few resources online for achieving the desired effects. == Themes == Bois has stated that he had "conceived [17776] to give the reader a good time," asserting that this "was literally the whole point." William Hughes writing for The A.V. Club described 17776 as concerned with why humans play sports: "That is, given the massive resources, time, and information at our disposal (not to mention those available to our descendants), why does communal game-playing still hold such an important place in society?" He also listed consciousness, hope, and despair as among the work's themes. Beth Elderkin of io9 described it as "a deep thought experiment into what we consider humanly possible". She also felt that Ten and Juice take on the role of angel and devil, and she suggested the two may be unreliable narrators. Ian Crouch of The New Yorker felt that the work had a "tonal echo" of Don DeLillo's 1972 novel End Zone due to thematic similarities "with the way that the order and logic of football might act as a counterbalance to the chaos of the real world". == Reception == According to the communications director at Vox Media, 17776 garnered over 2.3 million pageviews by July 10. Two days later, it had received more than 2.9 million pageviews. Average engagement time was over nine minutes, and 43 percent of readers finished each installment of the series published by July 7. On July 19, Bois claimed that 17776 received 700,000 unique visitors and 4 million total pageviews, with an average engagement time of 11 minutes. Thu-Huong Ha for Quartz described 17776 as "part Italo Calvino, part Peter Heller [author of The Dog Stars], with humor seemingly from within the depths of Reddit," saying that the story would appeal to fans of both sports and literature. Tor.com described the first chapter as full of tension and felt that receiving answers is a "surprisingly heartbreaking" experience "lessened by a gleeful bouncing immaturity" one would not expect from the characters. Beth Elderkin at io9 said the series is "akin to Homestuck" and described it as "weird, complex, and pretty spectacular". William Hughes writing for The A.V. Club felt that 17776 is a "truly innovative piece of work". After reading the first three chapters, Agatha French of the Los Angeles Times stated that she was "impressed and excited by the innovation" of what she saw, and that she was intrigued despite not knowing what the work is or is saying. She felt the work took full advantage of its online medium and suggested that it "may also be a glimpse into the future of reading on the Internet". Ian Crouch of The New Yorker described the series as, "despite its seemingly meagre parts, a thing of startling beauty". Of the chapters published by July 12, he felt "the most striking chapter" to be one that used audio of Verne Lundquist calling the end of a 2013 game between the University of Alabama and Auburn University over a video panning over Earth. He also noted that the series was compared to Homestuck and relayed additional comparisons to Thomas Pynchon novels and "a Reddit thread hijacked by robot trolls". The series won the inaugural National Magazine Award for Digital Innovation from the American Society of Magazine Editors; this was the first National Magazine Award nomination and win for SB Nation. It was described by the judges as "an extraordinary combination of art, fiction and technology, an online acid trip that had to be experienced to be believed." It was also longlisted for the Hugo Awards for Best Novella and Best Graphic Story in 2018, ultimately finishing in 11th place in both categories. == Sequel series == On September 28, 2020, a sequel titled 20020 was launched on Secret Base, a branch of SB Nation; on October 13, it was revea

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