AI Generator Character

AI Generator Character — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

Artificial general intelligence

Artificial general intelligence (AGI) is a hypothetical type of artificial intelligence that matches or surpasses human capabilities across virtually all cognitive tasks. Beyond AGI, artificial superintelligence (ASI) would outperform the best human abilities across every domain by a wide margin. Unlike artificial narrow intelligence (ANI), whose competence is confined to well‑defined tasks, an AGI system can generalise knowledge, transfer skills between domains, and solve novel problems without task‑specific reprogramming. Creating AGI is a stated goal of technology companies such as OpenAI, Google, xAI, and Meta. A 2020 survey identified 72 active AGI research and development projects across 37 countries. AGI is a common topic in science fiction and futures studies. Contention exists over whether AGI represents an existential risk. Some AI experts and industry figures have stated that mitigating the risk of human extinction posed by AGI should be a global priority. Others find the development of AGI to be in too remote a stage to present such a risk. == Terminology == AGI is also known as strong AI, full AI, human-level AI, human-level intelligent AI, or general intelligent action. The term "artificial general intelligence" was used in 1997 by Mark Gubrud in a discussion of the implications of fully automated military production and operations. A mathematical formalism of AGI named AIXI was proposed in 2000 by Marcus Hutter, who defines intelligence as "an agent’s ability to achieve goals or succeed in a wide range of environments". This type of AGI has also been called "universal artificial intelligence". The term AGI was re-introduced and popularized by Shane Legg and Ben Goertzel around 2002. Some academic sources reserve the term "strong AI" for computer programs that will experience sentience or consciousness. In contrast, weak AI (or narrow AI) can solve a specific problem but lacks general cognitive abilities. Some academic sources use "weak AI" to refer more broadly to any programs that neither experience consciousness nor have a mind in the same sense as humans. Related concepts include artificial superintelligence and transformative AI. An artificial superintelligence (ASI) is a hypothetical type of AGI that is much more generally intelligent than humans, while the notion of transformative AI relates to AI having a large impact on society, for example, similar to the agricultural or industrial revolution. A framework for classifying AGI was proposed in 2023 by Google DeepMind researchers. They define five performance levels of AGI: emerging, competent, expert, virtuoso, and superhuman. For example, a competent AGI is defined as an AI that outperforms 50% of skilled adults in a wide range of non-physical tasks, and a superhuman AGI (i.e., an artificial superintelligence) is similarly defined but with a threshold of 100%. They consider large language models like ChatGPT or LLaMA 2 to be instances of emerging AGI (comparable to unskilled humans). Regarding the autonomy of AGI and associated risks, they define five levels: tool (fully in human control), consultant, collaborator, expert, and agent (fully autonomous). == Characteristics == There is no single agreed-upon definition of intelligence as applied to computers. Computer scientist John McCarthy wrote in 2007: "We cannot yet characterize in general what kinds of computational procedures we want to call intelligent." === Intelligence traits === Researchers generally hold that a system is required to do all of the following to be regarded as an AGI: reason, use strategy, solve puzzles, and make judgments under uncertainty, represent knowledge, including common sense knowledge, plan, learn, communicate in natural language, if necessary, integrate these skills in completion of any given goal. Many interdisciplinary approaches (e.g. cognitive science, computational intelligence, and decision making) consider additional traits such as imagination (the ability to form novel mental images and concepts) and autonomy. Computer-based systems exhibiting these capabilities are now widespread, with modern large language models demonstrating computational creativity, automated reasoning, and decision support simultaneously across domains. === Physical traits === Other capabilities are considered desirable in intelligent systems, as they may affect intelligence or aid in its expression. These include: the ability to sense (e.g. see, hear, etc.), and the ability to act (e.g. move and manipulate objects, change location to explore, etc.) This includes the ability to detect and respond to hazard. === Tests for human-level AGI === Several tests meant to confirm human-level AGI have been considered. ==== Turing test ==== The Turing test was proposed by Alan Turing in his 1950 paper "Computing Machinery and Intelligence". This test involves a human judge engaging in natural language conversations with both a human and a machine designed to generate human-like responses. The machine passes the test if it can convince the judge that it is human a significant fraction of the time. Turing proposed this as a practical measure of machine intelligence, focusing on the ability to produce human-like responses rather than on the internal workings of the machine. The idea of the test is that the machine has to try and pretend to be a man, by answering questions put to it, and it will only pass if the pretence is reasonably convincing. A considerable portion of a jury, who should not be experts about machines, must be taken in by the pretence. In 2014, a chatbot named Eugene Goostman, designed to imitate a 13-year-old Ukrainian boy, reportedly passed a Turing Test event by convincing 33% of judges that it was human. However, this claim was met with significant skepticism from the AI research community, who questioned the test's implementation and its relevance to AGI. A 2025 pre‑registered, three‑party Turing‑test study by Cameron R. Jones and Benjamin K. Bergen showed that GPT-4.5 was judged to be the human in 73% of five‑minute text conversations—surpassing the 67% humanness rate of real confederates and meeting the researchers' criterion for having passed the test. ==== Ikea test ==== The "Ikea test", also known as the Flat Pack Furniture Test, involves an AI controlling a robot which attempts to assemble an Ikea flat-pack furniture product after having been shown the parts and instructions. As early as 2013, MIT's IkeaBot demonstrated fully autonomous multi-robot assembly of an IKEA Lack table in ten minutes, with no human intervention and no pre-programmed assembly instructions. The robots inferred the assembly sequence from the geometry of the parts alone. ==== Coffee test ==== Steve Wozniak proposed a test where a machine is required to enter an average American home and figure out how to make coffee. It must find the coffee machine, find the coffee, add water, find a mug, and brew the coffee by pushing the proper buttons. This test has been substantially approached across multiple systems. In January 2024, Figure AI's Figure 01 humanoid learned to operate a Keurig coffee machine autonomously after watching video demonstrations, using end-to-end neural networks to translate visual input into motor actions. In 2025, researchers at the University of Edinburgh published the ELLMER framework in Nature Machine Intelligence, demonstrating a robotic arm that interprets verbal instructions, analyses its surroundings, and autonomously makes coffee in dynamic kitchen environments — adapting to unforeseen obstacles in real time rather than following pre-programmed sequences. ==== Suleyman's test ==== Mustafa Suleyman's test proposes giving an AI model US$100,000 and asking it to obtain US$1 million. ==== Use of video-games ==== Adams, et al. propose that the ability to learn and succeed in a wide range of video games can be used to test AI intelligence. This range would include games unknown to the AGI developers before the test is administered. === AI-complete problems === A problem is informally called "AI-complete" or "AI-hard" if it is believed that AGI would be needed to solve it, because the solution is beyond the capabilities of a purpose-specific algorithm. == History == === Classical AI === Modern AI research began in the mid-1950s. The first generation of AI researchers were convinced that artificial general intelligence was possible and that it would exist in just a few decades. AI pioneer Herbert A. Simon wrote in 1965: "machines will be capable, within twenty years, of doing any work a man can do". Their predictions were the inspiration for Stanley Kubrick and Arthur C. Clarke's fictional character HAL 9000, who embodied what AI researchers believed they could create by the year 2001. AI pioneer Marvin Minsky was a consultant on the project of making HAL 9000 as realistic as possible according to the consensus predictions of the time. He said in 1967, "Within a generation... the problem of
Read more →
Virtual influencer

A virtual influencer, sometimes described as a virtual persona or virtual model, is a computer-generated fictional character that can be used for a variety of marketing-related purposes, but most frequently for social media marketing, in lieu of online human "influencers". Most virtual influencers are designed using computer graphics and motion capture technology to resemble real people in realistic situations. Common derivatives of virtual influencers include VTubers, which broadly refer to online entertainers and YouTubers who represent themselves using virtual avatars instead of their physical selves. == History == Virtual influencers are fundamentally synonymous with virtual idols, which originate from Japan's anime and Japanese idol culture that dates back to the 1980s. The first virtual idol created was Lynn Minmay, a fictional singer and main character of the anime television series Super Dimension Fortress Macross (1982) and the animated film adaptation Macross: Do You Remember Love? (1984). Minmay's success led to the production of more Japanese virtual idols, such as EVE from the Japanese cyberpunk anime Megazone 23 (1985), and Sharon Apple in Macross Plus (1994). Virtual idols were not always well received – in 1995, Japanese talent agency Horipro created Kyoko Date, which was inspired by the Macross franchise and dating sim games such as Tokimeki Memorial (1994). Date failed to gain commercial success despite drawing headlines for her debut as a CGI idol, largely due to technical limitations leading to issues such as unnatural movements, an issue also known as the uncanny valley. Since their inception, many virtual idols created have achieved continual success, with notable names including the Vocaloid singer Hatsune Miku, and the VTuber Kizuna AI. Technological advancements have also enabled production teams to use artificial intelligence and advanced techniques to customize the personalities and behavior of virtual idols. Due to modern-day advancements in technology, many virtual idols have held real-life tours and events. Notable ones include Hatsune Miku's titular tour Miku Expo and Hololive's concerts with many of their idols from their English, Japanese and Indonesian branches. Some notable events including virtual singers and influencers have included: Hatsune Miku opening for Lady Gaga in 2014 and Hoshimachi Suisei's concerts at the famous Budokan venue in Japan and her addition to the Forbes Japan list of '30 Under 30' individuals who are changing the world in their respective fields. == Benefits and criticism == From a branding perspective, virtual influencers are perceived to be much less likely to be mired in scandals. In China, celebrities caught in bad publicity such as singer Wang Leehom and entertainer Kris Wu have heightened the appeal of virtual influencers, since their existence relies entirely on computer-generated imagery and they are therefore unlikely to cause any damage to a brand's image by association. Some studies have also suggested that Generation Z consumers have a unique appetite for virtual idols and influencers, since they grew up in the age of the internet. Studies also show that human-like appearance of virtual influencers show higher message credibility than anime-like virtual influencers. Scholars and commentators have also questioned the ethics and cultural impact of virtual influencers, arguing that computer-generated personas can entrench unrealistic beauty standards while diffusing accountability for labor, identity, and consent. Business and marketing analysts have also warned that disclosure and governance remain inconsistent, recommending clearer guardrails and transparency when brands deploy synthetic spokespeople. In 2025, reporting highlighted concerns that AI-driven "virtual humans" could displace human creators and sales workers, intensifying debates over the future of creative labor and authenticity online. == Notable examples == === Virtual bands === Eternity - A South Korean virtual idol group formed by Pulse9. Gorillaz - A virtual band formed in 1998. K/DA - A virtual K-pop girl group created as part of the League of Legends video game franchise. MAVE: - A South Korean virtual girl group formed in 2023 by Metaverse Entertainment. Pentakill - A virtual heavy metal band created as part of the League of Legends video game franchise. Plave (band) - A South Korean virtual boy band formed by VLast. Squid Sisters and Off the Hook - Two virtual pop idol duos as part of the Splatoon series. Studio Killers - A Finnish-Danish-British virtual band formed in 2011. === Vocaloids === Hatsune Miku (modeled after Saki Fujita) Kagamine Rin/Len (modeled after Asami Shimoda) Megurine Luka (modeled after Yū Asakawa) Meiko (modeled after Meiko Haigō) Kaito (modeled after Naoto Fūga) === VTubers === Kano Kizuna AI Neuro-sama VShojo Ironmouse Projekt Melody Nijisanji Hololive Akai Haato Gawr Gura Hoshimachi Suisei Natsuiro Matsuri === Other examples === Ami Yamato Crazy Frog FN Meka IA Kuki AI Kyoko Date Kyra Miquela Naevis Shudu Gram
Read more →
Social network game

A social network game (sometimes simply referred to as a social media game, social gaming, or online social game) is a type of online game that is played through social networks or social media. They typically feature gamification systems with multiplayer gameplay mechanics. Social network games were originally implemented as browser games. As mobile gaming took off, the games moved to mobile as well. While they share many aspects of traditional video games, social network games often employ additional ones that make them distinct. Traditionally they are oriented to be social games and casual games. The first cross-platform "Facebook-to-Mobile" social network game was developed in 2011 by a Finnish company Star Arcade. Social network games are amongst the most popular games played in the world, with several products with tens of millions of players. (Lil) Green Patch, Happy Farm, and Mob Wars were some of the first successful games of this genre. FarmVille, Mafia Wars, Kantai Collection, and The Sims Social are more recent examples of popular social network game. Major companies that made or published social network games include Zynga, Wooga and Bigpoint Games. == Demographics == As of 2010, it was reported that 55 percent of the social network gaming demographic in the United States consisted of women while in the United Kingdom, women made up nearly 60 percent of the demographic. In addition, most social gamers were around the 30 to 59 age range, with the average social gamer being 43 years old. Social gaming may appeal more to the older demographic because it is free, easier to advance through in a short period, does not involve as much violence as traditional video games, and is easier to grasp. Other games target certain demographics that use social media, such as Pot Farm creating a community by involving elements of cannabis subculture in its gameplay. == Technology and platforms == A social network video game is a client-server application. The client in the web era was implemented with a mix of web technologies like Flash, HTML5, PHP and JavaScript. When mobile games moved to mobile, social game front ends were developed using mobile platform technologies like Java, Objective-C, Swift and C++. The back end was a mix of programming languages and systems, including PHP, Ruby, C++ and go. Where social network video games diverged from traditional game development was the combination of real-time analytics to continuously optimize game mechanics to drive growth, revenue, and engagement. == Distinct features == The following table outlines common characteristics of social games, mentioned by Björk at the 2010 GCO Games Convention Online: A social network game may employ any of the following features: asynchronous gameplay, which allows rules to be resolved without needing players to play at the same time. gamification, which video game mechanics such as achievements and points are applied to those experienced when playing games in order to motivate and engage users. community, as one of the most distinct features of social video games is in leveraging the player's social network. Quests or game goals may only be possible if a player "shares" with friends connected by the social network hosting the game or gets them to play, as well as "neighbors" or "allies". a lack of victory conditions: there are generally no victory conditions since most developers count on users playing their games often. The game never ends and no one is ever declared winner. Instead, many casual games have "quests" or "missions" for players to complete. This is not true for board game-like social games, such as Scrabble. a virtual currency which players usually must purchase with real-world money. With the in-game currency, players can buy upgrades that would otherwise take much longer to earn through in-game achievements. In many cases, some upgrades are only available with the virtual currency. == Engagement strategies == Since social network games are often less challenging than console games and they have relatively shorter game play, they use different techniques to stretch game play and tools to retain users. Continuous goals: The games assign specific goals for users to achieve. As they advance in the game, the goals become more challenging and time-consuming. They also provide frequent feedback with their performance. Every action will translate towards a certain goal that will be used to attain higher gaming capitals. Gaming capitals: Players are encouraged to earn different badges, trophies, and accolades that indicate their progress and accomplishments. Some achievements are unlocked just by advancing in the game while others may significantly alter the rationale behind the game and require extensive investment from players. The ways of gaining gaming capital are not limited to playing games but the games-related productive activities that are appreciated in the player's social circle too. By accumulating gaming capitals, they provide an intrinsic benefit to gamers as there is an avenue to boost their accomplishment and showcase their expertise of the game. The achievements are visible to their network of friends. Gaming capitals are a way for developers to increase replay value provides extended play time, and players get more value from the game. Motivation for collecting gaming capitals: 1. Legitimization: refers to society's willingness to approve or condone certain behavior. Collecting is about channeling one's materialistic desires into more meaningful pursuits. Game achievements serve a similar purpose, allowing players to justify the hours spent playing the game. 2. Self-extension: Gathering and controlling meaningful objects or experiences can work to gain one an improved sense of self. The collector's goal to complete a collection is symbolically about completing the self too. Events timed to real world: Popular games such as Dragon City and Wild Ones require users to wait a certain time period before their "energy bars" replenish. Without energy, they are unable to conduct any form of action. Gamers are forced to wait and return after their energy replenishes to continue playing. == Monetization == Social network games frequently monetize based on virtual good transactions, but other games are emerging that utilize newer economic models. === Virtual goods === Gamers will be able to purchase in game items like power-ups, avatar accessories, or decorative items users purchase within the game itself. This is realized by monetize products that do not technically exist. Virtual goods account for over 90% of all revenue generated by the world's top social game developers. Designers optimize user experience through additional gameplay, missions, and quests, without having to worry about overhead or unused stock. == Advertising == The following are common ways of advertising in social network games: === Banner advertisements === As banner ads within social networks tend to be where ad response is low, they tend to be priced at bottom-of-the-barrel CPMs of around $2. However, because social games generate so many page views, they are the biggest part of advertising revenue for the social gaming industry. === Video ads === Videos are the ad format with the most revenue per view. They tend to be higher-priced, either by CPMs ($35+ CPM in social games) or cost-per-completed-view. According to studies, video ads result in highest brand recall thus a good return on investment for advertisers. Video ads are shown either in in-game interstitials (e.g. when the game is loading a new screen) or through incentive-based advertising, i.e. you will get either an in-game reward or Facebook credits for watching an advertisement. === Product placement === A brand or product will be injected in a game in some way. Due to the variety of ways in which product placement can be accomplished in any media, and because the category is nascent, this category is not standardized at all, but some examples include branded in-game goods or even in-game quests. For example, in a game where you run a restaurant, you might be asked to collect ingredients to make a Starbucks Frappuccino, and receive in-game rewards for doing so. As these product placement deals are non-standard, they are largely charged with a production fee, which can be $350,000 to $750,000 depending on the type of placement and the popularity of the game. === Lead generation offers === Another form of advertising that is prevalent in many social games are lead generation offers. In this form of advertising, companies, usually from different industries, aim to convince players to sign up for their goods or services and in exchange, players will receive virtual gifts or advance in the game as a reward. === Sponsorship === ==== White label games ==== Applications that are built once, then individualized and licensed again and again. Developer can create a quality app focused on fun while leaving the edge
Read more →
BitFunnel

BitFunnel is the search engine indexing algorithm and a set of components used in the Bing search engine, which were made open source in 2016. BitFunnel uses bit-sliced signatures instead of an inverted index in an attempt to reduce operations cost. == History == Progress on the implementation of BitFunnel was made public in early 2016, with the expectation that there would be a usable implementation later that year. In September 2016, the source code was made available via GitHub. A paper discussing the BitFunnel algorithm and implementation was released as through the Special Interest Group on Information Retrieval of the Association for Computing Machinery in 2017 and won the Best Paper Award. == Components == BitFunnel consists of three major components: BitFunnel – the text search/retrieval system itself WorkBench – a tool for preparing text for use in BitFunnel NativeJIT – a software component that takes expressions that use C data structures and transforms them into highly optimized assembly code == Algorithm == === Initial problem and solution overview === The BitFunnel paper describes the "matching problem", which occurs when an algorithm must identify documents through the usage of keywords. The goal of the problem is to identify a set of matches given a corpus to search and a query of keyword terms to match against. This problem is commonly solved through inverted indexes, where each searchable item is maintained with a map of keywords. In contrast, BitFunnel represents each searchable item through a signature. A signature is a sequence of bits which describe a Bloom filter of the searchable terms in a given searchable item. The bloom filter is constructed through hashing through several bit positions. === Theoretical implementation of bit-string signatures === The signature of a document (D) can be described as the logical-or of its term signatures: S D → = ⋃ t ∈ D S t → {\displaystyle {\overrightarrow {S_{D}}}=\bigcup _{t\in D}{\overrightarrow {S_{t}}}} Similarly, a query for a document (Q) can be defined as a union: S Q → = ⋃ t ∈ Q S t → {\displaystyle {\overrightarrow {S_{Q}}}=\bigcup _{t\in Q}{\overrightarrow {S_{t}}}} Additionally, a document D is a member of the set M' when the following condition is satisfied: S Q → ∩ S D → = S Q → {\displaystyle {\overrightarrow {S_{Q}}}\cap {\overrightarrow {S_{D}}}={\overrightarrow {S_{Q}}}} This knowledge is then combined to produce a formula where M' is identified by documents which match the query signature: M ′ = { D ∈ C ∣ S Q → ∩ S D → = S Q → } {\displaystyle M'=\left\{D\in C\mid {\overrightarrow {S_{Q}}}\cap {\overrightarrow {S_{D}}}={\overrightarrow {S_{Q}}}\right\}} These steps and their proofs are discussed in the 2017 paper. === Pseudocode for bit-string signatures === This algorithm is described in the 2017 paper. M ′ = ∅ foreach D ∈ C do if S D → ∩ S Q → = S Q → then M ′ = M ′ ∪ { D } endif endfor {\displaystyle {\begin{array}{l}M'=\emptyset \\{\texttt {foreach}}\ D\in C\ {\texttt {do}}\\\qquad {\texttt {if}}\ {\overrightarrow {S_{D}}}\cap {\overrightarrow {S_{Q}}}={\overrightarrow {S_{Q}}}\ {\texttt {then}}\\\qquad \qquad M'=M'\cup \{D\}\\\qquad {\texttt {endif}}\\{\texttt {endfor}}\end{array}}}
Read more →
Human–AI interaction

Human–AI interaction is a developing field of research and a sub-field of human–computer interaction (HCI). HCI is a field of research that explores the interactions between humans and computer-based technology, focusing on design implementation, user experience, and psychological factors. With the proliferation of artificial intelligence (AI), there has developed a sub-section of HCI research dedicated specifically to artificial intelligence and how people interact with and are impacted by it. This is human–AI interaction, abbreviated either as HAX or HAII. == Introduction == Artificial intelligence (AI), in general, has fluid definitions and varied research applications, but in brief can be applied to mechanizing tasks that would require human intelligence to complete. AI are tools designed to replicate the human abilities of navigating uncertainty, active learning, and processing information in different contexts. Within the context of HCI and HAX research, artificial intelligence can be broken into two sub-fields, natural language processing (NLP) and computer vision (CV). AI technologies notably include machine-learning, deep-learning and neural networks, and large-language models (LLMs). As a new and rapidly developing technology, AI is changing how computers work and therefore changing how humans interact with computers. Unlike the traditional human-computer interaction, where a human directs a machine, human-AI interaction is characterized by a more collaborative relationship between the computer program (the AI) and the human user, as AI is perceived as an active agent rather than a tool. This changing dynamic creates new questions and necessitates new research methods that are not present in traditional HCI research. According to a scoping review on the state of the discipline, the HAX field comprises research on the "design, development, and evaluation of AI systems" and encompasses the themes of human-AI collaboration, human-AI competition, human-AI conflict, and human-AI symbiosis. == Design == Machine learning and artificial intelligence have been used for decades in targeted advertising and to recommend content in social media. Ethical Guidelines (Framework for ethical AI development) == User Experience (UX) == This section should handle research on how users interact with tools. What techniques do they use, do they develop habits, what types of programs and devices are they using to access these tools, what do they use these tools to do exactly. === Cognitive Frameworks in AI Tool Users === AI has been viewed with various expectations, attributions, and often misconceptions. Many people exclusively understand AI as the LLM chatbots they interact with, like ChatGPT or Claude, or other generative AI programs. [Insert section: discuss how people interact with these specific AI tools as a connection to the following paragraphs] Most fundamentally, humans have a mental model of understanding AI's reasoning and motivation for its decision recommendations, and building a holistic and precise mental model of AI helps people create prompts to receive more valuable responses from AI. However, these mental models are not whole because people can only gain more information about AI through their limited interaction with it; more interaction with AI builds a better mental model that a person may build to produce better prompt outcomes. Research on human-AI interaction has emphasized that users develop mental models of AI systems and revise those models through repeated use, feedback, and explanation, while design research has stressed the importance of communicating capabilities and limitations early and supporting trust calibration through explanation and correction. In a 2025 SSRN working paper, John DeVadoss proposed "Hypothetico-Deductive Interaction" (HDI), a framework that describes human-AI interaction as a mutual process of conjecture and refutation in which users test assumptions about an AI system's capabilities while the system infers and updates assumptions about user goals through its responses and clarifying questions. DeVadoss argued that this framing helps explain prompt iteration, weak capability awareness, and trust miscalibration, and suggested design responses such as clearer communication of uncertainty, easier correction, actionable explanations, and safer failure modes. == Research themes == === Human-AI collaboration === Human-AI collaboration occurs when the human and AI supervise the task on the same level and extent to achieve the same goal. Some collaboration occurs in the form of augmenting human capability. AI may help human ability in analysis and decision-making through providing and weighing a volume of information, and learning to defer to the human decision when it recognizes its unreliability. It is especially beneficial when the human can detect a task that AI can be trusted to make few errors so that there is not a lot of excessive checking process required on the human's end. Some findings show signs of human-AI augmentation, or human–AI symbiosis, in which AI enhances human ability in a way that co-working on a task with AI produces better outcomes than a human working alone. For example: the quality and speed of customer service tasks increase when a human agent collaborates with AI, training on specific models allows AI to improve diagnoses in clinical settings, and AI with human-intervention can improve creativity of artwork while fully AI-generated haikus were rated negatively. Human-AI synergy, a concept in which human-AI collaboration would produce more optimal outcomes than either human or AI working alone could explain why AI does not always help with performance. Some AI features and development may accelerate human-AI synergy, while others may stagnate it. For example, when AI updates for better performance, it sometimes worsens the team performance with human and AI by reducing the compatibility with the new model and the mental model a user has developed on the previous version. Research has found that AI often supports human capabilities in the form of human-AI augmentation and not human-AI synergy, potentially because people rely too much on AI and stop thinking on their own. Prompting people to actively engage in analysis and think when to follow AI recommendations reduces their over-reliance, especially for individuals with higher need for cognition. === Human-AI competition === Robots and computers have substituted routine tasks historically completed by humans, but agentic AI has made it possible to also replace cognitive tasks including taking phone calls for appointments and driving a car. At the point of 2016, research has estimated that 45% of paid activities could be replaced by AI by 2030. Perceived autonomy of robots is known to increase people's negative attitude toward them, and worry about the technology taking over leads people to reject it. There has been a consistent tendency of algorithm aversion in which people prefer human advice over AI advice. However, people are not always able to tell apart tasks completed by AI or other humans. See AI takeover for more information. It is also notable that this sentiment is more prominent in the Western cultures as Westerners tend to show less positive views about AI compared to East Asians. == Research on the psychological impacts of AI == === Perception on others who use AI === As much as people perceive and make judgment about AI itself, they also form impressions of themselves and others who use AI. In the workplace, employees who disclose the use of AI in their tasks are more likely to receive feedback that they are not as hardworking as those who are in the same job who receive non-AI help to complete the same tasks. AI use disclosure diminishes the perceived legitimacy in the employee's task and decision making which ultimately leads observers to distrust people who use AI. Although these negative effects of AI use disclosure are weakened by the observers who use AI frequently themselves, the effect is still not attenuated by the observers' positive attitude towards AI. === Bias, AI, and human === Although AI provides a wide range of information and suggestions to its users, AI itself is not free of biases and stereotypes, and it does not always help people reduce their cognitive errors and biases. People are prone to such errors by failing to see other potential ideas and cases that are not listed by AI responses and committing to a decision suggested by AI that directly contradicts the correct information and directions that they are already aware of. Gender bias is also reflected as the female gendering of AI technologies which conceptualizes females as a helpful assistant. == Emotional connection with AI == Human-AI interaction has been theorized in the context of interpersonal relationships mainly in social psychology, communications and media studies, and as a technology interface through the lens of hu
Read more →
Sharenting

"Sharenting" is a portmanteau of "sharing" and "parenting", describing the practice of parents publicizing a large amount of potentially sensitive content about their children on internet platforms, most notably on social media. While the term was coined as recently as 2010, sharenting has become an international phenomenon with widespread presence in the United States, Spain, France, and the United Kingdom. Proponents of sharenting frame the practice as a natural expression of parental pride in their children and argue that critics take sharenting-related posts out of context. Detractors find that it violates child privacy and hurts a parent–child relationship. Academic research has been conducted over the potential social motivations for sharenting and legal frameworks to balance child privacy with this parental practice. Researchers have conducted several psychological surveys, outlining social media accessibility, parental self-identification with children, and social pressure as potential causes for sharenting. Legal scholars have identified international human rights laws, labor protections, and recent online child privacy statutes as potential legal standards to check sharenting abuses. == History == The origins of the term "sharenting" have been attributed to the Wall Street Journal, where they called it "oversharenting," a portmanteau of "oversharing" and "parenting." Priya Kumar suggests that recording life moments of children rearing is not a new practice: people have been using diaries, scrapbooks and baby log books as the media of documentation for centuries. Scholars assert that sharenting has become popular as a result of social media, which has made many people more comfortable with sharing their lives and those of their children online. The trend of oversharing on social media has raised public attention in the 2010s and become the focus of a number of editorials and academic research projects. It was also added to Times Word of the Day in February 2013 and Collins English Dictionary in 2016 given its influence. == Popularity == Several studies describe sharenting as an international phenomenon with widespread prevalence across households. In the United States, researchers at the University of Michigan C.S. Mott Children's Hospital found that almost 75% of American parents were familiar with someone who over-shared information about their child on social media, and an AVG survey determined that 92% of all American two-year-olds had some presence on the internet. In Australia, Fisher-Price conducted a survey which revealed that 90% of Australian parents admitted to over-sharing. In Spain and Czech Republic, a survey of approximately 1,500 parents found that 70-80% participated in sharenting. In the United Kingdom, France, Germany, and Italy, a Research Now report revealed that almost three-quarters of surveyed parents said that they were "willing to share images of their infants". Some claim that sharenting presents a violation of child privacy, and this backlash includes anti-sharenting sites and apps that block baby pictures. One particular outlet of protest was the blog STFU Parents, founded in 2009 to criticize parental oversharing on social media. Some parents felt that these criticisms of sharenting often took posts out of context and neglected some positive aspects of the practice, including advancing a stronger sense of online community. Others, while acknowledging the potential privacy violations of sharenting, suggested a more tailored approach that would only permit posting under certain conditions, notwithstanding audience and identification restrictions for social media posts. == Motivations == Research has suggested that sharenting is associated with a mix of parent self-identification with children, mothering pressures, and the accessibility of social media. Conducting 17 interviews with mothers in the United Kingdom, a London School of Economics study found that parent bloggers often re-explained their sharing practices in terms of expressing their own personal identity, representing their own child as part of themselves. In particular, the report surveyed the use of blogs as a networking vehicle to connect parents with similar family situations and found that sharenting parents, by filtering self-presentation through their parent-child relationship, adopted a more relational identity on social media websites. This included identifying oneself in terms of parental circumstances, whether it be raising a child with a disability or being a single mother. Alternatively, some have suggested that these online expressions indicate the infiltration of individual pride into the sphere of parenting, as family photography becomes a means to "show off" one's children to the others and strengthens a parent's sense of individuated self. Addressing the prevalence of mothers engaging in sharenting, those who purport this view argue that the rise of digital communication has pressured mothers into performing the role of a "good" parent on social media platforms. They claim that these developments may reinforce a dominant vision of a "normal" family, as sharenting posts could be motivated by the need to converge to a normative interpretation of family. == Controversy == While some people assert that online platforms enable parents to establish a community and seek parenting support, others are concerned about the children's data privacy and their lack of informed consent. Sharing content may not only embarrass children but also creates an initial digital footprint, a history of online activity, that the children themselves have no control over. This might bring some negative consequences, such as being ridiculed at school or leaving a negative impression on future employers. === Parental benefits === Many parents use social media to seek parenting advice and share information about their children. With the convenience of online platforms, parent bloggers can easily connect with other people in similar situations as well as those who are willing to contribute meaningful advice. By forming a community, parents can receive encouragement from empathetic peers and assistance from experts in children rearing. Parents whose children need special educational accommodations or have disabilities often found themselves detached from the mainstream parenting style. Therefore, they regard online blogs as a means to gain support from others and support back. Online blogging enables parents of children with disabilities and special needs to connect with other parents. The advice from similarly situated families can open up new possibilities that help the parents "negotiate the complexities of social services, health care, and schools". However, in some cases, posting online about a parent's struggles can cause a backlash, as advocates may accuse the parent of presenting people with that condition in a bad light, or wonder how the child will feel, if they later read these posts and see how much their parents struggled to care for them. Such advantages of social media are not limited to particular groups of parents. In general, most parents benefit from exchanging parenting experience. Statistically speaking, 72% of parents rate social media useful for emotional connection and affirmations, and 74% of them receive support about parenting from friends on social media. Sharenting also plays a role in fostering interpersonal relationships. As the images and words about children's lives initiate conversations, parents use sharenting to stay connected with distant friends and relatives. In particular, mothers, as a research study reveals, are willing to engage in sharenting since they believe that the positive contents can help avoid digital conflicts and maintain close relations with those in their social circles. Researchers also found that female participants in this study carefully chose photos and phrases to express love and present laudable behaviors of children in their updates, which indicates their intention to convey positive messages. These messages also promote a close social network for a child as the parents invites supportive family members and friends into daily life. === Children's privacy === Given the potential misuse of digital data, people are critical about sharenting, and the majority of parents are cautious about the wrongdoing with online posts. The disclosure of minors' personal information, such as geographic location, name, date of birth, pictures, and the schools they attend, might expose them to illegal practices by recipients with malicious intentions. Sharented information is often abused for "identity theft", when imposters manage to track, stalk, commit fraud against children, or even blackmail the family. According to Barclays, online fraud targeting the young generation will contribute to a loss of £670 million (approximately $790 million) by 2030, and two-thirds of identity fraud will be related to s
Read more →
Social bot

A social bot, refers to fully or partially automated social media accounts designed to perform most regular users’ actions, such as liking, posting content, and chatting with other users. Although their levels of autonomy vary, and often include a human-in-the-loop, social bots can use artificial intelligence to perform social media actions and can use large language models to mimic human dialogue. Social bots can operate alone or in groups that coordinate messaging as part of a network of coordinated inauthentic behavior. Social bots are often used to perform ad fraud by artificially boosting viewership and engagement metrics and to spread disinformation on social media. == Uses == Social bots are used for a large number of purposes on a variety of social media platforms, including Twitter, Instagram, Facebook, and YouTube. One common use of social bots is to inflate a social media user's apparent popularity, usually by artificially manipulating their engagement metrics with large volumes of fake likes, reposts, or replies. Social bots can similarly be used to artificially inflate a user's follower count with fake followers, creating a false perception of a larger and more influential online following than is the case. The use of social bots to create the impression of a large social media influence allows individuals, brands, and organizations to attract a higher number of human followers and boost their online presence. Fake engagement can be bought and sold in the black market of social media engagement. Corporations typically use automated customer service agents on social media to affordably manage high levels of support requests. Social bots are used to send automated responses to users’ questions, sometimes prompting the user to private message the support account with additional information. The increased use of automated support bots and virtual assistants has led to some companies laying off customer-service staff. Social bots are also often used to influence public opinion. Autonomous bot accounts can flood social media with large numbers of posts expressing support for certain products, companies, or political campaigns, creating the impression of organic grassroots support. This can create a false perception of the number of people who support a certain position, which may also have effects on the direction of stock prices or on elections. Messages with similar content can also influence fads or trends. Many social bots are also used to amplify phishing attacks. These malicious bots are used to trick a social media user into giving up their passwords or other personal data. This is usually accomplished by posting links claiming to direct users to news articles that would in actuality direct to malicious websites containing malware. Scammers often use URL shortening services such as TinyURL and bit.ly to disguise a link's domain address, increasing the likelihood of a user clicking the malicious link. The presence of fake social media followers and high levels of engagement help convince the victim that the scammer is in fact a trusted user. Social bots can be a tool for computational propaganda. Bots can also be used for algorithmic curation, algorithmic radicalization, and/or influence-for-hire, a term that refers to the selling of an account on social media platforms. == History == Bots have coexisted with computer technology since the earliest days of computing. Social bots have their roots in the 1950s with Alan Turing, whose work focused on machine intelligence with the development of the Turing Test. The following decades saw further progress made towards the goal of creating programs capable of mimicking human behavior, notably with Joseph Weizenbaum’s creation of ELIZA. Considered to be one of the first Chatbots, ELIZA could simulate natural conversations with human users through pattern matching. Its most famous script was DOCTOR, a simulation of a Rogerian psychotherapist that was programmed to chat with patients and respond to questions. With the growth of social media platforms in the early 2000s, these bots could be used to interact with much larger user groups in an inconspicuous manner. Early instances of autonomous agents on social media could be found on sites like MySpace, with social bots being used by marketing firms to inflate activity on a user’s page in an effort to make them appear more popular. Social bots have been observed on a large variety of social media websites, with Twitter being one of the most widely observed examples. The creation of Twitter bots is generally against the site’s terms of service when used to post spam or to automatically like and follow other users, but some degree of automation using Twitter’s API may be permitted if used for “entertainment, informational, or novelty purposes.” Other platforms such as Reddit and Discord also allow for the use of social bots as long as they are not used to violate policies regarding harmful content and abusive behavior. Social media platforms have developed their own automated tools to filter out messages that come from bots, although they cannot detect all bot messages. == Legal regulation == Due to the difficulty of recognizing social bots and separating them from "eligible" automation via social media APIs, it is unclear how legal regulation can be enforced. Social bots are expected to play a role in shaping public opinion by autonomously acting as influencers. Some social bots have been used to rapidly spread misinformation, manipulate stock markets, influence opinion on companies and brands, promote political campaigns, and engage in malicious phishing campaigns. In the United States, some states have started to implement legislation in an attempt to regulate the use of social bots. In 2019, California passed the Bolstering Online Transparency Act (the B.O.T. Act) to make it unlawful to use automated software to appear indistinguishable from humans for the purpose of influencing a social media user's purchasing and voting decisions. Other states such as Utah and Colorado have passed similar bills to restrict the use of social bots. The Artificial Intelligence Act (AI Act) in the European Union is the first comprehensive law governing the use of Artificial Intelligence. The law requires transparency in AI to prevent users from being tricked into believing they are communicating with another human. AI-generated content on social media must be clearly marked as such, preventing social bots from using AI in a manner that mimics human behavior. == Detection == The first generation of bots could sometimes be distinguished from real users by their often superhuman capacities to post messages. Later developments have succeeded in imprinting more "human" activity and behavioral patterns in the agent. With enough bots, it might be even possible to achieve artificial social proof. To unambiguously detect social bots as what they are, a variety of criteria must be applied together using pattern detection techniques, some of which are: cartoon figures as user pictures sometimes also random real user pictures are captured (identity fraud) reposting rate temporal patterns sentiment expression followers-to-friends ratio length of user names variability in (re)posted messages engagement rate (like/followers rate) analysis of the time series of social media posts Social bots are always becoming increasingly difficult to detect and understand. The bots' human-like behavior, ever-changing behavior of the bots, and the sheer volume of bots covering every platform may have been a factor in the challenges of removing them. Social media sites, like Twitter, are among the most affected, with CNBC reporting up to 48 million of the 319 million users (roughly 15%) were bots in 2017. Botometer (formerly BotOrNot) is a public Web service that checks the activity of a Twitter account and gives it a score based on how likely the account is to be a bot. The system leverages over a thousand features. An active method for detecting early spam bots was to set up honeypot accounts that post nonsensical content, which may get reposted (retweeted) by the bots. However, bots evolve quickly, and detection methods have to be updated constantly, because otherwise they may get useless after a few years. One method is the use of Benford's Law for predicting the frequency distribution of significant leading digits to detect malicious bots online. This study was first introduced at the University of Pretoria in 2020. Another method is artificial-intelligence-driven detection. Some of the sub-categories of this type of detection would be active learning loop flow, feature engineering, unsupervised learning, supervised learning, and correlation discovery. Some operations of bots work together in a synchronized way. For example, ISIS used Twitter to amplify its Islamic content by numerous orchestrated accounts which further pushed an item to the Hot List news, thus further a
Read more →
Locally recoverable code

Locally recoverable codes are a family of error correction codes that were introduced first by D. S. Papailiopoulos and A. G. Dimakis and have been widely studied in information theory due to their applications related to distributive and cloud storage systems. An [ n , k , d , r ] q {\displaystyle [n,k,d,r]_{q}} LRC is an [ n , k , d ] q {\displaystyle [n,k,d]_{q}} linear code such that there is a function f i {\displaystyle f_{i}} that takes as input i {\displaystyle i} and a set of r {\displaystyle r} other coordinates of a codeword c = ( c 1 , … , c n ) ∈ C {\displaystyle c=(c_{1},\ldots ,c_{n})\in C} different from c i {\displaystyle c_{i}} , and outputs c i {\displaystyle c_{i}} . == Overview == Erasure-correcting codes, or simply erasure codes, for distributed and cloud storage systems, are becoming more and more popular as a result of the present spike in demand for cloud computing and storage services. This has inspired researchers in the fields of information and coding theory to investigate new facets of codes that are specifically suited for use with storage systems. It is well-known that LRC is a code that needs only a limited set of other symbols to be accessed in order to restore every symbol in a codeword. This idea is very important for distributed and cloud storage systems since the most common error case is when one storage node fails (erasure). The main objective is to recover as much data as possible from the fewest additional storage nodes in order to restore the node. Hence, Locally Recoverable Codes are crucial for such systems. The following definition of the LRC follows from the description above: an [ n , k , r ] {\displaystyle [n,k,r]} -Locally Recoverable Code (LRC) of length n {\displaystyle n} is a code that produces an n {\displaystyle n} -symbol codeword from k {\displaystyle k} information symbols, and for any symbol of the codeword, there exist at most r {\displaystyle r} other symbols such that the value of the symbol can be recovered from them. The locality parameter satisfies 1 ≤ r ≤ k {\displaystyle 1\leq r\leq k} because the entire codeword can be found by accessing k {\displaystyle k} symbols other than the erased symbol. Furthermore, Locally Recoverable Codes, having the minimum distance d {\displaystyle d} , can recover d − 1 {\displaystyle d-1} erasures. == Definition == Let C {\displaystyle C} be a [ n , k , d ] q {\displaystyle [n,k,d]_{q}} linear code. For i ∈ { 1 , … , n } {\displaystyle i\in \{1,\ldots ,n\}} , let us denote by r i {\displaystyle r_{i}} the minimum number of other coordinates we have to look at to recover an erasure in coordinate i {\displaystyle i} . The number r i {\displaystyle r_{i}} is said to be the locality of the i {\displaystyle i} -th coordinate of the code. The locality of the code is defined as An [ n , k , d , r ] q {\displaystyle [n,k,d,r]_{q}} locally recoverable code (LRC) is an [ n , k , d ] q {\displaystyle [n,k,d]_{q}} linear code C ∈ F q n {\displaystyle C\in \mathbb {F} _{q}^{n}} with locality r {\displaystyle r} . Let C {\displaystyle C} be an [ n , k , d ] q {\displaystyle [n,k,d]_{q}} -locally recoverable code. Then an erased component can be recovered linearly, i.e. for every i ∈ { 1 , … , n } {\displaystyle i\in \{1,\ldots ,n\}} , the space of linear equations of the code contains elements of the form x i = f ( x i 1 , … , x i r ) {\displaystyle x_{i}=f(x_{i_{1}},\ldots ,x_{i_{r}})} , where i j ≠ i {\displaystyle i_{j}\neq i} . == Optimal locally recoverable codes == Theorem Let n = ( r + 1 ) s {\displaystyle n=(r+1)s} and let C {\displaystyle C} be an [ n , k , d ] q {\displaystyle [n,k,d]_{q}} -locally recoverable code having s {\displaystyle s} disjoint locality sets of size r + 1 {\displaystyle r+1} . Then An [ n , k , d , r ] q {\displaystyle [n,k,d,r]_{q}} -LRC C {\displaystyle C} is said to be optimal if the minimum distance of C {\displaystyle C} satisfies == Tamo–Barg codes == Let f ∈ F q [ x ] {\displaystyle f\in \mathbb {F} _{q}[x]} be a polynomial and let ℓ {\displaystyle \ell } be a positive integer. Then f {\displaystyle f} is said to be ( r {\displaystyle r} , ℓ {\displaystyle \ell } )-good if • f {\displaystyle f} has degree r + 1 {\displaystyle r+1} , • there exist distinct subsets A 1 , … , A ℓ {\displaystyle A_{1},\ldots ,A_{\ell }} of F q {\displaystyle \mathbb {F} _{q}} such that – for any i ∈ { 1 , … , ℓ } {\displaystyle i\in \{1,\ldots ,\ell \}} , f ( A i ) = { t i } {\displaystyle f(A_{i})=\{t_{i}\}} for some t i ∈ F q {\displaystyle t_{i}\in \mathbb {F} _{q}} , i.e., f {\displaystyle f} is constant on A i {\displaystyle A_{i}} , – # A i = r + 1 {\displaystyle \#A_{i}=r+1} , – A i ∩ A j = ∅ {\displaystyle A_{i}\cap A_{j}=\varnothing } for any i ≠ j {\displaystyle i\neq j} . We say that { A 1 , … , A ℓ {\displaystyle A_{1},\ldots ,A_{\ell }} } is a splitting covering for f {\displaystyle f} . === Tamo–Barg construction === The Tamo–Barg construction utilizes good polynomials. • Suppose that a ( r , ℓ ) {\displaystyle (r,\ell )} -good polynomial f ( x ) {\displaystyle f(x)} over F q {\displaystyle \mathbb {F} _{q}} is given with splitting covering i ∈ { 1 , … , ℓ } {\displaystyle i\in \{1,\ldots ,\ell \}} . • Let s ≤ ℓ − 1 {\displaystyle s\leq \ell -1} be a positive integer. • Consider the following F q {\displaystyle \mathbb {F} _{q}} -vector space of polynomials V = { ∑ i = 0 s g i ( x ) f ( x ) i : deg ⁡ ( g i ( x ) ) ≤ deg ⁡ ( f ( x ) ) − 2 } . {\displaystyle V=\left\{\sum _{i=0}^{s}g_{i}(x)f(x)^{i}:\deg(g_{i}(x))\leq \deg(f(x))-2\right\}.} • Let T = ⋃ i = 1 ℓ A i {\textstyle T=\bigcup _{i=1}^{\ell }A_{i}} . • The code { ev T ⁡ ( g ) : g ∈ V } {\displaystyle \{\operatorname {ev} _{T}(g):g\in V\}} is an ( ( r + 1 ) ℓ , ( s + 1 ) r , d , r ) {\displaystyle ((r+1)\ell ,(s+1)r,d,r)} -optimal locally coverable code, where ev T {\displaystyle \operatorname {ev} _{T}} denotes evaluation of g {\displaystyle g} at all points in the set T {\displaystyle T} . === Parameters of Tamo–Barg codes === • Length. The length is the number of evaluation points. Because the sets A i {\displaystyle A_{i}} are disjoint for i ∈ { 1 , … , ℓ } {\displaystyle i\in \{1,\ldots ,\ell \}} , the length of the code is | T | = ( r + 1 ) ℓ {\displaystyle |T|=(r+1)\ell } . • Dimension. The dimension of the code is ( s + 1 ) r {\displaystyle (s+1)r} , for s {\displaystyle s} ≤ ℓ − 1 {\displaystyle \ell -1} , as each g i {\displaystyle g_{i}} has degree at most deg ⁡ ( f ( x ) ) − 2 {\displaystyle \deg(f(x))-2} , covering a vector space of dimension deg ⁡ ( f ( x ) ) − 1 = r {\displaystyle \deg(f(x))-1=r} , and by the construction of V {\displaystyle V} , there are s + 1 {\displaystyle s+1} distinct g i {\displaystyle g_{i}} . • Distance. The distance is given by the fact that V ⊆ F q [ x ] ≤ k {\displaystyle V\subseteq \mathbb {F} _{q}[x]_{\leq k}} , where k = r + 1 − 2 + s ( r + 1 ) {\displaystyle k=r+1-2+s(r+1)} , and the obtained code is the Reed-Solomon code of degree at most k {\displaystyle k} , so the minimum distance equals ( r + 1 ) ℓ − ( ( r + 1 ) − 2 + s ( r + 1 ) ) {\displaystyle (r+1)\ell -((r+1)-2+s(r+1))} . • Locality. After the erasure of the single component, the evaluation at a i ∈ A i {\displaystyle a_{i}\in A_{i}} , where | A i | = r + 1 {\displaystyle |A_{i}|=r+1} , is unknown, but the evaluations for all other a ∈ A i {\displaystyle a\in A_{i}} are known, so at most r {\displaystyle r} evaluations are needed to uniquely determine the erased component, which gives us the locality of r {\displaystyle r} . To see this, g {\displaystyle g} restricted to A j {\displaystyle A_{j}} can be described by a polynomial h {\displaystyle h} of degree at most deg ⁡ ( f ( x ) ) − 2 = r + 1 − 2 = r − 1 {\displaystyle \deg(f(x))-2=r+1-2=r-1} thanks to the form of the elements in V {\displaystyle V} (i.e., thanks to the fact that f {\displaystyle f} is constant on A j {\displaystyle A_{j}} , and the g i {\displaystyle g_{i}} 's have degree at most deg ⁡ ( f ( x ) ) − 2 {\displaystyle \deg(f(x))-2} ). On the other hand | A j ∖ { a j } | = r {\displaystyle |A_{j}\backslash \{a_{j}\}|=r} , and r {\displaystyle r} evaluations uniquely determine a polynomial of degree r − 1 {\displaystyle r-1} . Therefore h {\displaystyle h} can be constructed and evaluated at a j {\displaystyle a_{j}} to recover g ( a j ) {\displaystyle g(a_{j})} . === Example of Tamo–Barg construction === We will use x 5 ∈ F 41 [ x ] {\displaystyle x^{5}\in \mathbb {F} _{41}[x]} to construct [ 15 , 8 , 6 , 4 ] {\displaystyle [15,8,6,4]} -LRC. Notice that the degree of this polynomial is 5, and it is constant on A i {\displaystyle A_{i}} for i ∈ { 1 , … , 8 } {\displaystyle i\in \{1,\ldots ,8\}} , where A 1 = { 1 , 10 , 16 , 18 , 37 } {\displaystyle A_{1}=\{1,10,16,18,37\}} , A 2 = 2 A 1 {\displaystyle A_{2}=2A_{1}} , A 3 = 3 A 1 {\displaystyle A_{3}=3A_{1}} , A 4 = 4 A 1 {\displaystyle A_{4}=4A_{1}} , A 5 = 5 A 1 {\displaystyle A_{5}=5A_{1}} , A 6 = 6 A 1 {\displaystyle A_{6}=6A_{1}}
Read more →
Calais (Reuters product)

Calais is a service created by Thomson Reuters that automatically extracts semantic information from web pages in a format that can be used on the semantic web. Calais was launched in January 2008, and is free to use. The technology is now available via the website of Refinitiv, a provider of financial market data and infrastructure founded in 2018, that is a subsidiary of London Stock Exchange Group. The Calais Web service reads unstructured text and returns Resource Description Framework formatted results identifying entities, facts and events within the text. The service appears to be based on technology acquired when Reuters purchased ClearForest in 2007. The technology has also been used to automatically tag blog articles, and organize museum collections. Calais uses natural language processing technologies delivered via a web service interface.
Read more →
Intent-based network

Intent-Based Networking (IBN) is an approach to network management that shifts the focus from manually configuring individual devices to specifying desired outcomes or business objectives, referred to as "intents". == Description == Rather than relying on low-level commands to configure the network, administrators define these high-level intents, and the network dynamically adjusts itself to meet these requirements. IBN simplifies the management of complex networks by ensuring that the network infrastructure aligns with the desired operational goals. For example, an implementer can explicitly state a network purpose with a policy such as "Allow hosts A and B to communicate with X bandwidth capacity" without the need to understand the detailed mechanisms of the underlying devices (e.g. switches), topology or routing configurations. == Architecture == Advances in Natural Language Understanding (NLU) systems, along with neural network-based algorithms like BERT, RoBERTa, GLUE, and ERNIE, have enabled the conversion of user queries into structured representations that can be processed by automated services. This capability is crucial for managing the increasing complexity of network services. Intent-Based Networking (IBN) leverages these advancements to simplify network management by abstracting network services, reducing operational complexity, and lowering costs. A proposed three-layered architecture integrates intent-based automation into network management systems. In the business layer, intents are based on Key Performance Indicators (KPIs) and Service Level Agreements (SLAs), reflecting business objectives. The intent layer evaluates and re-plans actions dynamically, where a Knowledge module abstracts and reasons about intents, while an Agent interfaces with network objects to execute actions. The data layer observes network objects, updates topology information, and interacts with the Knowledge and Agent modules to ensure accurate and timely responses to network changes. At the bottom, the network layer contains the physical infrastructure, transforming network data into a usable format for the intent layer to act upon.
Read more →
Honey encryption

Honey encryption is a type of data encryption that "produces a ciphertext, which, when decrypted with an incorrect key as guessed by the attacker, presents a plausible-looking yet incorrect plaintext." == Creators == Ari Juels and Thomas Ristenpart of the University of Wisconsin, the developers of the encryption system, presented a paper on honey encryption at the 2014 Eurocrypt cryptography conference. == Method of protection == A brute-force attack involves repeated decryption with random keys; this is equivalent to picking random plaintexts from the space of all possible plaintexts with a uniform distribution. This is effective because even though the attacker is equally likely to see any given plaintext, most plaintexts are extremely unlikely to be legitimate i.e. the distribution of legitimate plaintexts is non-uniform. Honey encryption defeats such attacks by first transforming the plaintext into a space such that the distribution of legitimate plaintexts is uniform. Thus an attacker guessing keys will see legitimate-looking plaintexts frequently and random-looking plaintexts infrequently. This makes it difficult to determine when the correct key has been guessed. In effect, honey encryption "[serves] up fake data in response to every incorrect guess of the password or encryption key." The security of honey encryption relies on the fact that the probability of an attacker judging a plaintext to be legitimate can be calculated (by the encrypting party) at the time of encryption. This makes honey encryption difficult to apply in certain applications e.g. where the space of plaintexts is very large or the distribution of plaintexts is unknown. It also means that honey encryption can be vulnerable to brute-force attacks if this probability is miscalculated. For example, it is vulnerable to known-plaintext attacks: if the attacker has a crib that a plaintext must match to be legitimate, they will be able to brute-force even Honey Encrypted data if the encryption did not take the crib into account. == Example == An encrypted credit card number is susceptible to brute-force attacks because not every string of digits is equally likely. The number of digits can range from 13 to 19, though 16 is the most common. Additionally, it must have a valid IIN and the last digit must match the checksum. An attacker can also take into account the popularity of various services: an IIN from MasterCard is probably more likely than an IIN from Diners Club Carte Blanche. Honey encryption can protect against these attacks by first mapping credit card numbers to a larger space where they match their likelihood of legitimacy. Numbers with invalid IINs and checksums are not mapped at all (i.e. have probability 0 of legitimacy). Numbers from large brands like MasterCard and Visa map to large regions of this space, while less popular brands map to smaller regions, etc. An attacker brute-forcing such an encryption scheme would only see legitimate-looking credit card numbers when they brute-force, and the numbers would appear with the frequency the attacker would expect from the real world. == Application == Juels and Ristenpart aim to use honey encryption to protect data stored on password manager services. Juels stated that "password managers are a tasty target for criminals," and worries that "if criminals get a hold of a large collection of encrypted password vaults they could probably unlock many of them without too much trouble." Hristo Bojinov, CEO and founder of Anfacto, noted that "Honey Encryption could help reduce their vulnerability. But he notes that not every type of data will be easy to protect this way. … Not all authentication or encryption system yield themselves to being honeyed."
Read more →
Social knowledge management

Social knowledge management is a business approach that aims to leverage the collective intelligence and social interactions of an organization’s members and stakeholders. It is a branch of knowledge management, which is a multidisciplinary field that deals with the creation, sharing, and use of knowledge in various domains, such as business, economics, psychology, and information management. Knowledge management seeks to enhance organizational performance, innovation, and competitiveness by managing the intangible assets of an organization, such as human capital, know-how, technology, customers, and networks. Social media plays a crucial role in social knowledge management by enhancing communication, collaboration, and learning among individuals and groups, both internally and externally. It offers valuable insights and feedback from customers, partners, and stakeholders, and aids in generating and disseminating new knowledge. In a business context, social media is utilized for various purposes, including sentiment analysis, social learning, social collaboration, and social knowledge management. Social knowledge management is one of the application areas of social media in a business context next to others like sentiment analysis, social learning or social collaboration. Social media use by businesses can strive to achieve the following things from social media strategy point of view: learn, listen, engage in conversation, measure and refine, develop capabilities, define activities, prioritize objectives etc. Social media are not only transforming private communication and interaction, they also will transform how people work. With social media knowledge work in organizations can be optimized extremely: like a better distribution sharing and access to knowledge. This will be more and more important, as in today's business world, speed and complexity increase dramatically, while work environments change constantly. == Examples of Social KM platforms == Elium, a European software application which combines social tagging, bookmarking and networking paradigms to address internal information management purposes. Sciomino was a startup enterprise social network for Social Knowledge Management.
Read more →
List of Ruby software and tools

This is a list of software and programming tools for the Ruby programming language, which includes libraries, web frameworks, implementations, tools, and related projects. == Web tools == Capistrano (software) – remote server automation tool Mongrel – Ruby web server Rack – interface between web servers and web applications Ruby on Rails – full-stack web application framework Sinatra – lightweight Ruby web application framework Spree Commerce – e-commerce platform WEBrick – Ruby HTTP server toolkit == Libraries == BioRuby – bioinformatics and computational biology library for Ruby Bogus – Ruby library for creating reliable test doubles with contract verification ERuby – embedded Ruby templating EventMachine – event-driven I/O library Factory Bot – test fixtures library Fat comma – Ruby library for JSON-like hash syntax Geocoder – Ruby library for geocoding and reverse geocoding addresses Haml – HTML templating engine Markaby – HTML generation via Ruby Nokogiri – XML/HTML parsing library RSpec – behavior-driven testing framework for Ruby RubyGems – package manager for Ruby libraries and applications Sass – CSS preprocessor Sidekiq – background job framework for Ruby, used to handle asynchronous tasks. Uconv – Unicode text conversion library Watir – web application testing framework == Ruby implementations == HotRuby – Ruby interpreter implemented in JavaScript, enabling Ruby code to run in web browsers. IronRuby – Ruby for .NET platform JRuby – Ruby on the Java Virtual Machine MacRuby – Ruby implementation for macOS Mod ruby – Apache module that embeds the Ruby interpreter to improve performance of Ruby web applications Mruby – lightweight Ruby interpreter Rubinius – alternative Ruby implementation, based loosely on the Smalltalk-80 Blue Book design. Ruby MRI – the standard Ruby interpreter YARV – "Yet Another Ruby VM," the bytecode interpreter used in modern Ruby implementations == Tools == Homebrew – package manager for macOS and Linux written in Ruby Pry – interactive Ruby shell Rake – build and task management Ruby Version Manager – environment manager RubyCocoa – bridge between Ruby and Cocoa RubyForge – project hosting site RubyMotion – for iOS/macOS development RubySpec – language specification tests == Integrated Development Environments == Aptana Studio — integrated RadRails plugin for Ruby on Rails development Eclipse DLTK Ruby Plugin — Ruby development plugin for Eclipse Eric — open-source Python-based IDE with Ruby support Komodo IDE — commercial cross-platform IDE with Ruby support RubyMine — commercial IDE for Ruby and Rails by JetBrains SlickEdit — commercial cross-platform IDE with Ruby support == List of websites using Ruby on Rails == Airbnb Basecamp Diaspora – decentralized social network application built with Ruby on Rails Discourse – open-source discussion platform built with Ruby on Rails Fiverr GitHub Hulu Shopify SoundCloud Twitch Zendesk
Read more →
CrySyS Lab

CrySyS Lab (Hungarian pronunciation: [ˈkriːsis]) is part of the Department of Telecommunications at the Budapest University of Technology and Economics. The name is derived from "Laboratory of Cryptography and System Security", the full Hungarian name is CrySys Adat- és Rendszerbiztonság Laboratórium. == History == CrySyS Lab. was founded in 2003 by a group of security researchers at the Budapest University of Technology and Economics. Currently, it is located in the Infopark Budapest. The heads of the lab were Dr. István Vajda (2003–2010) and Dr. Levente Buttyán (2010-now). Since its establishment, the lab participated in several research and industry projects, including successful EU FP6 and FP7 projects (SeVeCom, a UbiSecSens and WSAN4CIP). == Research results == CrySyS Lab is recognized in research for its contribution to the area of security in wireless embedded systems. In this area, the members of the lab produced 5 books 4 book chapters 21 journal papers 47 conference papers 3 patents 2 Internet Draft The above publications had an impact factor of 30+ and obtained more than 7500 references. Several of these publications appeared in highly cited journals (e.g., IEEE Transactions on Dependable and Secure Systems, IEEE Transactions on Mobile Computing). == Forensics analysis of malware incidents == The laboratory was involved in the forensic analysis of several high-profile targeted attacks. In October 2011, CrySyS Lab discovered the Duqu malware; pursued the analysis of the Duqu malware and as a result of the investigation, identified a dropper file with an MS 0-day kernel exploit inside; and finally released a new open-source Duqu Detector Toolkit to detect Duqu traces and running Duqu instances. In May 2012, the malware analysis team at CrySyS Lab participated in an international collaboration aiming at the analysis of an as yet unknown malware, which they call sKyWIper. At the same time Kaspersky Lab analyzed the malware Flame and Iran National CERT (MAHER) the malware Flamer. Later, they turned out to be the same. Other analysis published by CrySyS Lab include the password analysis of the Hungarian ISP, Elender, and a thorough Hungarian security survey of servers after the publications of the Kaminsky DNS attack.
Read more →
Strong secrecy

Strong secrecy is a term used in formal proof-based cryptography for making propositions about the security of cryptographic protocols. It is a stronger notion of security than syntactic (or weak) secrecy. Strong secrecy is related with the concept of semantic security or indistinguishability used in the computational proof-based approach. Bruno Blanchet provides the following definition for strong secrecy: Strong secrecy means that an adversary cannot see any difference when the value of the secret changes For example, if a process encrypts a message m an attacker can differentiate between different messages, since their ciphertexts will be different. Thus m is not a strong secret. If however, probabilistic encryption were used, m would be a strong secret. The randomness incorporated into the encryption algorithm will yield different ciphertexts for the same value of m.
Read more →