AI Headshot Apk

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Progress in artificial intelligence

Progress in artificial intelligence (AI) refers to the advances, milestones, and breakthroughs that have been achieved in the field of artificial intelligence over time. AI is a branch of computer science that aims to create machines and systems capable of performing tasks that typically require human intelligence. AI applications have been used in a wide range of fields including medical diagnosis, finance, robotics, law, video games, agriculture, and scientific discovery. The society as a whole is looking for artificial intelligence to be on a key factor in the upcming years because of its potential. However, many AI applications are not perceived as AI: "A lot of cutting-edge AI has filtered into general applications, often without being called AI because once something becomes useful enough and common enough it's not labeled AI anymore." "Many thousands of AI applications are deeply embedded in the infrastructure of every industry." In the late 1990s and early 2000s, AI technology became widely used as elements of larger systems, but the field was rarely credited for these successes at the time. Kaplan and Haenlein structure artificial intelligence along three evolutionary stages: Artificial narrow intelligence – AI capable only of specific tasks; Artificial general intelligence – AI with ability in several areas, and able to autonomously solve problems they were never even designed for; Artificial superintelligence – AI capable of general tasks, including scientific creativity, social skills, and general wisdom. To allow comparison with human performance, artificial intelligence can be evaluated on constrained and well-defined problems. Such tests have been termed subject-matter expert Turing tests. Also, smaller problems provide more achievable goals and there are an ever-increasing number of positive results. In 2023, humans still substantially outperformed both GPT-4 and other models tested on the ConceptARC benchmark. Those models scored 60% on most, and 77% on one category, while humans scored 91% on all and 97% on one category. However, later research in 2025 showed that human-generated output grids were only accurate 73% of the time, while AI models available that year managed to score above 77%. == History == Increasing, promoting or constraining AI progress has often be done via controlling or increasing the amount of compute. == Current performance in specific areas == There are many useful abilities that can be described as showing some form of intelligence. This gives better insight into the comparative success of artificial intelligence in different areas. AI, like electricity or the steam engine, is a general-purpose technology. There is no consensus on how to characterize which tasks AI tends to excel at. Some versions of Moravec's paradox observe that humans are more likely to outperform machines in areas such as physical dexterity that have been the direct target of natural selection. While projects such as AlphaZero have succeeded in generating their own knowledge from scratch, many other machine learning projects require large training datasets. Researcher Andrew Ng has suggested, as a "highly imperfect rule of thumb", that "almost anything a typical human can do with less than one second of mental thought, we can probably now or in the near future automate using AI." Games provide a high-profile benchmark for assessing rates of progress; many games have a large professional player base and a well-established competitive rating system. AlphaGo brought the era of classical board-game benchmarks to a close when Artificial Intelligence proved their competitive edge over humans in 2016. Deep Mind's AlphaGo AI software program defeated the world's best professional Go Player Lee Sedol. Games of imperfect knowledge provide new challenges to AI in the area of game theory; the most prominent milestone in this area was brought to a close by Libratus' poker victory in 2017. E-sports continue to provide additional benchmarks; Facebook AI, Deepmind, and others have engaged with the popular StarCraft franchise of videogames. Broad classes of outcome for an AI test may be given as: optimal: it is not possible to perform better (note: some of these entries were solved by humans) super-human: performs better than all humans high-human: performs better than most humans par-human: performs similarly to most humans sub-human: performs worse than most humans === Optimal === Tic-tac-toe Connect Four: 1988 Checkers (aka 8x8 draughts): Weakly solved (2007) Rubik's Cube: Mostly solved (2010) Heads-up limit hold'em poker: Statistically optimal in the sense that "a human lifetime of play is not sufficient to establish with statistical significance that the strategy is not an exact solution" (2015) === Super-human === Othello (aka reversi): c. 1997 Scrabble: 2006 Backgammon: c. 1995–2002 Chess: Supercomputer (c. 1997); Personal computer (c. 2006); Mobile phone (c. 2009); Computer defeats human + computer (c. 2017) Jeopardy!: Question answering, although the machine did not use speech recognition (2011) Arimaa: 2015 Shogi: c. 2017 Go: 2017 Heads-up no-limit hold'em poker: 2017 Six-player no-limit hold'em poker: 2019 Gran Turismo Sport: 2022 === High-human === Crosswords: c. 2012 Freeciv: 2016 Dota 2: 2018 Bridge card-playing: According to a 2009 review, "the best programs are attaining expert status as (bridge) card players", excluding bidding. StarCraft II: 2019 Mahjong: 2019 Stratego: 2022 No-Press Diplomacy: 2022 Hanabi: 2022 Natural language processing === Par-human === Optical character recognition for ISO 1073-1:1976 and similar special characters. Classification of images Handwriting recognition Facial recognition Visual question answering SQuAD 2.0 English reading-comprehension benchmark (2019) SuperGLUE English-language understanding benchmark (2020) Some school science exams (2019) Some tasks based on Raven's Progressive Matrices Many Atari 2600 games (2015) === Sub-human === Optical character recognition for printed text (nearing par-human for Latin-script typewritten text) Object recognition Various robotics tasks that may require advances in robot hardware as well as AI, including: Stable bipedal locomotion: Bipedal robots can walk, but are less stable than human walkers (as of 2017) Humanoid soccer Speech recognition: "nearly equal to human performance" (2017) Explainability. Current medical systems can diagnose certain medical conditions well, but cannot explain to users why they made the diagnosis. Many tests of fluid intelligence (2020) Bongard visual cognition problems, such as the Bongard-LOGO benchmark (2020) Visual Commonsense Reasoning (VCR) benchmark (as of 2020) Stock market prediction: Financial data collection and processing using Machine Learning algorithms Angry Birds video game, as of 2020 Various tasks that are difficult to solve without contextual knowledge, including: Translation Word-sense disambiguation == Proposed tests of artificial intelligence == In his famous Turing test, Alan Turing picked language, the defining feature of human beings, for its basis. The Turing test is now considered too exploitable to be a meaningful benchmark. The Feigenbaum test, proposed by the inventor of expert systems, tests a machine's knowledge and expertise about a specific subject. A paper by Jim Gray of Microsoft in 2003 suggested extending the Turing test to speech understanding, speaking and recognizing objects and behavior. Proposed "universal intelligence" tests aim to compare how well machines, humans, and even non-human animals perform on problem sets that are generic as possible. At an extreme, the test suite can contain every possible problem, weighted by Kolmogorov complexity; however, these problem sets tend to be dominated by impoverished pattern-matching exercises where a tuned AI can easily exceed human performance levels. == Exams == According to OpenAI, in 2023 GPT-4 achieved high scores on several standardized and professional examinations, including around the 90th percentile on the Uniform Bar Exam, the 89th percentile on the mathematics section of the SAT, the 93rd percentile on SAT Reading and Writing, the 54th percentile on the analytical writing section of the GRE, the 88th percentile on GRE quantitative reasoning, and the 99th percentile on GRE verbal reasoning. OpenAI also reported that GPT-4 scored in the 99th to 100th percentile on the 2020 USA Biology Olympiad semifinal exam and earned top scores on several AP exams. Independent researchers found in 2023 that ChatGPT based on GPT-3.5 performed "at or near the passing threshold" on all three parts of the United States Medical Licensing Examination (USMLE), suggesting that large language models could reach passing-level performance on some medical knowledge assessments even without domain-specific fine-tuning. GPT-3.5 was also reported to attain a low but passing grade on examinations for four law school courses at the University of Minnes
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Social influence bias

The social influence bias is an asymmetric herding effect on online social media platforms which makes users overcompensate for negative ratings but amplify positive ones. Driven by the desire to be accepted within a specific group, it surrounds the idea that people alter certain behaviors to be like those of the people within a group. Therefore, it is a subgroup term for various types of cognitive biases. Some social influence bias types include the bandwagon effect, authority bias, groupthinking effect, social comparison bias, social media bias and more. Understanding these biases helps us understand the term overall. However, the composition of the term "social influence bias" requires critical examination to understand the way that it affects individuals' and groups' lives. The term "influence" has 2 different types of stigma. For one, it surrounds the idea that people show their true inner selves when "under the influence". On the other end, it also proposes the idea that people are not their own selves when "under the influence". These tend to be constructions made by people, which also tend to fit the situation based on their own perspectives. So, even in social terms, it requires both sides to be examined to understand whether we truly are affected by context, or we remain to be and behave in terms of our own selves. The term "influence" doesn't necessarily say that there lies greater strength in our inner self's desires and decisions, nor does it say that external factors have the greater power. In a similar manner, both social and non-social judgments are to be associated with anxiety, but the same can't necessarily be said in the case of social conformity. So, the gray areas within this topic beg the question, "What does social influence bias say about us, and does it affect us all in the same way?" == Social media bias == Media bias is reflected in search systems in social media. Kulshrestha and her team found through research in 2018 that the top-ranked results returned by these search engines can influence users' perceptions when they conduct searches for events or people, which is particularly reflected in political bias and polarizing topics. Fueled by confirmation bias, online echo chambers allow users to be steeped within their own ideology. Because social media is tailored to your interests and your selected friends, it is an easy outlet for political echo chambers. Social media bias is also reflected in hostile media effect. Social media has a place in disseminating news in modern society, where viewers are exposed to other people's comments while reading news articles. In their 2020 study, Gearhart and her team showed that viewers' perceptions of bias increased and perceptions of credibility decreased after seeing comments with which they held different opinions. == In research context == In observational data, how social influence affects collected judgment is challenging to fully understand. Positive social influence can accumulate and result in a rating bubble, while negative social influence is neutralized by crowd correction. This phenomenon was first described in a paper written by Lev Muchnik, Sinan Aral and Sean J. Taylor in 2014, then the question was revisited by Cicognani et al., whose experiment reinforced Munchnik's and his co-authors' results. == Relevance == Online customer reviews are trusted sources of information in various contexts such as online marketplaces, dining, accommodation, movies, or digital products. However, these online ratings are not immune to herd behavior, which means that subsequent reviews are not independent from each other. As on many such sites, preceding opinions are visible to a new reviewer, he or she can be heavily influenced by the antecedent evaluations in his or her decision about the certain product, service or online content. This form of herding behavior inspired Muchnik, Aral and Taylor to conduct their experiment on influence in social contexts. == Experimental design == Muchnik, Aral, and Taylor designed a large-scale randomized experiment to measure social influence on user reviews. The experiment was conducted on social news aggregation website like Reddit. The study lasted for 5 months, the authors randomly assigned 101 281 comments to one of the following treatment groups: up-treated (4049), down-treated (1942), or control (the proportions reflect the observed ratio of up-and down-votes. Comments which fell to the first group were given an up-vote upon the creation of the comment, the second group got a down-vote upon creation, the comments in the control group remained untouched. A vote is equivalent to a single rating (+1 or -1). As other users are unable to trace a user’s votes, they were unaware of the experiment. Due to randomization, comments in the control and the treatment group were not different in terms of expected rating. The treated comments were viewed more than 10 million times and rated 308 515 times by successive users. == Results == The up-vote treatment increased the probability of up-voting by the first viewer by 32% over the control group, while the probability of down-voting did not change compared to the control group, which means that users did not correct the random positive rating. The upward bias remained inplace for the observed 5-month period. The accumulating herding effect increased the comment’s mean rating by 25% compared to the control group comments. Positively manipulated comments did receive higher ratings at all parts of the distribution, which means that they were also more likely to collect extremely high scores. The negative manipulation created an asymmetric herd effect: although the probability of subsequent down-votes was increased by the negative treatment, the probability of up-voting also grew for these comments. The community performed a correction which neutralized the negative treatment and resulted non-different final mean ratings from the control group. The authors also compared the final mean scores of comments across the most active topic categories on the website. The observed positive herding effect was present in the "politics," "culture and society," and "business" subreddits, but was not applicable for "economics," "IT," "fun," and "general news".- == Implications == The skewed nature of online ratings makes review outcomes different to what it would be without the social influence bias. In a 2009 experiment by Hu, Zhang and Pavlou showed that the distribution of reviews of a certain product made by unconnected individuals is approximately normal, however, the rating of the same product on Amazon followed a J-Shaped distribution with twice as much five-star ratings than others. Cicognani, Figini and Magnani came to similar conclusions after their experiment conducted on a tourism services website: positive preceding ratings influenced raters' behavior more than mediocre ones. Positive crowd correction makes community-based opinions upward-biased.
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Social media as a news source

Social media as a news source is defined as the use of online social media platforms such as Instagram, TikTok, and Facebook rather than the use of traditional media platforms like the newspaper or live TV to obtain news. Television had just begun to turn a nation of people who once listened to media content into watchers of media content between the 1950s and the 1980s when the popularity of social media had also begun creating a nation of media content creators. Almost half of Americans use social media as a news source, according to the Pew Research Center. As social media's role in news consumption grows, questions have emerged about its impact on knowledge, the formation of echo chambers, and the effectiveness of fact-checking efforts in combating misinformation. Social media platforms allow user-generated content and sharing content within one's own virtual network. Using social media as a news source allows users to engage with news in a variety of ways including: Consuming and discovering news Sharing or reposting news Posting one's own photos, videos, or reports of news (i.e., engage in citizen or participatory journalism) Commenting on news posts Using social media as a news source has become an increasingly popular way for people of all age groups to obtain current and important information. Just like many other new forms of technology there are going to be pros and cons. There are ways that social media positively affects the world of news and journalism but it is important to acknowledge that there are also ways in which social media has a negative effect on the news. With this accessibility, people now have more ways to consume false news, biased news, and even disturbing content. In 2019, the Pew Research Center created a poll that reported Americans are wary about the ways that social media sites share news and certain content. This wariness of accuracy grew as awareness that social media sites could be exploited by bad actors who concoct false narratives and fake news. == Relationship to traditional news sources == Unlike traditional news platforms such as newspapers and news shows, social media platforms allow people without professional journalistic backgrounds to create news and cover events that news agencies might not cover. Social media users may read a set of news that differs slightly from what newspaper editors prioritize in the print press. A 2019 study found that Facebook and Twitter users are more likely to share politics, public affairs, and visual media news. Typically social media users circulate more towards posting about negative news. A study of tweets found that while optimistic-sounding and neutral-sounding tweets were equally likely to express certainty or uncertainty, the pessimistic tweets were nearly twice as likely to appear certain of an outcome than uncertain. These results could imply that posts of a more pessimistic nature that are also written with an air of certainty are more likely to be shared or otherwise permeate groups on Twitter. A similar bias towards negativity has developed on Facebook, where internal memos revealed that an algorithm built to promote "meaningful social interaction" actually incentivized publishers to promote negative and sensational news. Biases towards negativity need to be considered when the utility of new media is addressed, as the potential for human opinion to overemphasize any particular news story is greater despite general improvement. In order to compete in this rapidly changing technological environment, there has been an upheaval of traditional news sources onto online spaces. The production and circulation of newspaper prints have continued to globally decline in accordance with the increasing presence of news outlets on social media. Prominent platforms such as Twitter and Facebook have been key in engaging users through the integration of journalistic news into their newsfeeds. This feature has now become a foundational part of these apps' interfaces. Social media incentivizes both legacy news brands and individual professional journalists to share their reporting and interact with audiences on social platforms to boost engagement. However, most people who consume news on social media report that accessing news is not their main motivation for being on social media, but rather, they see and consume news incidentally. Nonetheless, informational interviews reveal that these consumers rely on being informed through social media. Some news consumers attest that a news brand's participation in social media does not improve their trust in the brand and that more in-depth reporting and more transparency about biases would improve trust instead. == Use as a news source == Globally, data from 2020 shows that over 70% of adult participants from Kenya, South Africa, Chile, Bulgaria, Greece, and Argentina utilized social media for news while those from France, the UK, the Netherlands, Germany, and Japan were reportedly less than 40 percent. According to the Pew Research Center, 20% of adults in the United States in 2018 said they get their news from social media "often," compared to 16% who said they often get news from print newspapers, 26% who often get it from the radio, 33% who often get it from news websites, and 49% who often get it from TV. The same survey found that social media was the most popular way for American adults age 18–29 to get news, the second-to-last most popular way for Americans age 20–49 to get news, and the least popular way for American adults age 50-64 and 65+ to get the news. In 2019, the Pew Research Center found that over half of Americans (54%) either got their news "sometimes" or "often" from social media, and Facebook was the most popular social media site where American adults got their news. However, at least 50% off all respondents reported that the following were either a "very big problem" or a "moderately big problem" for getting news on social media: One-sided news (83%) Inaccurate news (81%) Censorship of the news (69%) Uncivil discussions about the news (69%) Harassment of journalists (57%) News organizations or personalities being banned (53%) Violent or disturbing news images or videos (51%) In a later survey from the same year, the Pew Research Center reported that 18% of American adults reported that the most common way they get news about politics and the election was from social media. Additional source information shows that from politics and the United States presidential election in 2016, the popularity of fake news had grown to global attention. With this information, the study explains that more than 60 percent of adults receive their news from social media, the most popular being Facebook. With the increase of fake news, and the large amount of adult participation on these social media sites, it made it much harder for those who were searching for news to find a source that they could find credible. Another study found that adult participants found their own friends on Facebook to be a more reliable source of information online compared to a professional news organization. Although, when news was posted by a news organization online, they were then found more reliable compared to when they are shared by their online friends. Showing that adult participants found that the news that was only posted on Facebook and social media was much more credible to them than compared to other forms of information spreading. The study further states that these outcomes have the potential explanation that the topic of the news article played a part in the ways they were affected. This could have affected the way adult participants interacted with the different news sources, such as their online friends compared to a news organization, prominently because depending on the story, they want to have the correct information about the news from the most credible source. === By young people === Social media platforms are some of the most easily accessible forms of news and with the growing generations, the technology is only going to grow. With that, the use of social media in younger generations is also going to grow alongside it. Technology in the hands of young kids can be a concern moving into the future. Globally, there is evidence that through social media, youth have become more directly involved in protests, social campaigns and generally, in the sharing of news across multiple platforms. The number of people who use social media platforms such as Twitter, Facebook, Instagram, or Snapchat as ways to seek information has increased significantly in recent years especially for people who are part of the younger generation.TikTok is a rapidly expanding platform that young adults can use to find news content on social media. TikTok is one of the sites that young adults and teens utilize to get news about trending themes and controversial topics. The younger generation accepts without hesitation the information that thei
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Information security

Information security is the practice of protecting information by mitigating information risks. It is part of information risk management. It typically involves preventing or reducing the probability of unauthorized or inappropriate access to data or the unlawful use, disclosure, disruption, deletion, corruption, modification, inspection, recording, or devaluation of information. It also involves actions intended to reduce the adverse impacts of such incidents. Protected information may take any form, e.g., electronic or physical, tangible (e.g., paperwork), or intangible (e.g., knowledge). Information security's primary focus is the balanced protection of data confidentiality, integrity, and availability (known as the CIA triad, unrelated to the US government organization) while maintaining a focus on efficient policy implementation, all without hampering organization productivity. This is largely achieved through a structured risk management process. To standardize this discipline, academics and professionals collaborate to offer guidance, policies, and industry standards on passwords, antivirus software, firewalls, encryption software, legal liability, security awareness and training, and so forth. This standardization may be further driven by a wide variety of laws and regulations that affect how data is accessed, processed, stored, transferred, and destroyed. While paper-based business operations are still prevalent, requiring their own set of information security practices, enterprise digital initiatives are increasingly being emphasized, with information assurance now typically being dealt with by information technology (IT) security specialists. These specialists apply information security to technology (most often some form of computer system). IT security specialists are almost always found in any major enterprise/establishment due to the nature and value of the data within larger businesses. They are responsible for keeping all of the technology within the company secure from malicious attacks that often attempt to acquire critical private information or gain control of the internal systems. There are many specialist roles in Information Security including securing networks and allied infrastructure, securing applications and databases, security testing, information systems auditing, business continuity planning, electronic record discovery, and digital forensics. == Standards == Information security standards are guidelines generally outlined in published materials that aim to protect a user's or an organization's cyber environment from threats. This environment includes the users themselves, hardware such as devices and networks, software such as applications or services, and any information in storage or transit. These standards comprise security concepts, technologies, and guidelines to deal with an adverse event. They may also include assessment criteria and certification for organizations implementing a minimum level of security. These standards are developed by various international and national bodies to prevent or mitigate cyber-attacks, ensure consistency among developers, and establish a minimum standard in industries susceptible to an attack. The ISO/IEC 27000 family, published by the International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC), provides information about the guidelines and requirements for an Information Security Management System (ISMS). The Common Criteria (ISO/IEC 15408) provides guidelines on evaluating and certifying the security of a system. The IEC 62443 establishes security standards for automation and control systems. Similarly, the ISO/SAE 21434, ETSI EN 303 645, and EN 18031 provide standards for road vehicles, the Internet of Things, and radio-based systems respectively. The NIST Cybersecurity Framework (NIST CSF) is a set of guidelines developed by the U.S. National Institute of Standards and Technology to help organizations with risk management. NIST also publishes various Federal Information Processing Standards (FIPS) and Special Publications. The United Kingdom has introduced Cyber Essentials, which is a certification scheme to protect organizations against common security threats. The Australian Cyber Security Centre publishes the Essential Eight mitigation strategies. The Payment Card Industry Data Security Standard (PCI DSS) regulates handling of cardholder data in order to reduce credit card fraud. UL has published standards related to specific industries such as UL 2900-2-3 for security and life safety signaling systems and UL-2900-2-1 for healthcare and wellness systems. == Threats == Information security threats come in many different forms. Some of the most common threats today are software attacks, theft of intellectual property, theft of identity, theft of equipment or information, sabotage, and information extortion. Viruses, worms, phishing attacks, and Trojan horses are a few common examples of software attacks. The theft of intellectual property has also been an extensive issue for many businesses. Identity theft is the attempt to act as someone else usually to obtain that person's personal information or to take advantage of their access to vital information through social engineering. Sabotage usually consists of the destruction of an organization's website in an attempt to cause loss of confidence on the part of its customers. Information extortion consists of theft of a company's property or information as an attempt to receive a payment in exchange for returning the information or property back to its owner, as with ransomware. One of the most functional precautions against these attacks is to conduct periodical user awareness. Governments, military, corporations, financial institutions, hospitals, non-profit organizations, and private businesses amass a great deal of confidential information about their employees, customers, products, research, and financial status. Should confidential information about a business's customers or finances or new product line fall into the hands of a competitor or hacker, a business and its customers could suffer widespread, irreparable financial loss, as well as damage to the company's reputation. From a business perspective, information security must be balanced against cost; the Gordon-Loeb Model provides a mathematical economic approach for addressing this concern. For the individual, information security has a significant effect on privacy, which is viewed very differently in various cultures. == History == Since the early days of communication, diplomats and military commanders understood that it was necessary to provide some mechanism to protect the confidentiality of correspondence and to have some means of detecting tampering. Julius Caesar is credited with the invention of the Caesar cipher c. 50 B.C., which was created in order to prevent his secret messages from being read should a message fall into the wrong hands. However, for the most part protection was achieved through the application of procedural handling controls. Sensitive information was marked up to indicate that it should be protected and transported by trusted persons, guarded and stored in a secure environment or strong box. As postal services expanded, governments created official organizations to intercept, decipher, read, and reseal letters (e.g., the U.K.'s Secret Office, founded in 1653). In the mid-nineteenth century more complex classification systems were developed to allow governments to manage their information according to the degree of sensitivity. For example, the British Government codified this, to some extent, with the publication of the Official Secrets Act in 1889. Section 1 of the law concerned espionage and unlawful disclosures of information, while Section 2 dealt with breaches of official trust. A public interest defense was soon added to defend disclosures in the interest of the state. A similar law was passed in India in 1889, The Indian Official Secrets Act, which was associated with the British colonial era and used to crack down on newspapers that opposed the Raj's policies. A newer version was passed in 1923 that extended to all matters of confidential or secret information for governance. By the time of the First World War, multi-tier classification systems were used to communicate information to and from various fronts, which encouraged greater use of code making and breaking sections in diplomatic and military headquarters. Encoding became more sophisticated between the wars as machines were employed to scramble and unscramble information. The establishment of computer security inaugurated the history of information security. The need for such appeared during World War II. The volume of information shared by the Allied countries during the Second World War necessitated formal alignment of classification systems and procedural controls. An arcane range of markings evol
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Mooky (app)

Mooky was a location-based social and dating application, designed to help its users to find the perfect match by providing a large scale of filters. Mooky was free of charge. The app made use of mobile devices' geolocation, a feature of smart phones and other devices which allows users to locate other users who are nearby. == History == Mooky was published on Google Play on April 17, 2016, by Mooky BV. The latest version of this application was version 1.0.6. == Overview == === How it works === Mooky used Facebook to build a user profile with photos and basic information, like the user's surname and age. From there on the user had to fill in their Mooky profile, which contains information about the user's height, posture, hair color, eye color, ethnicity and religion. After this the user could select its preferences to find matches nearby. === User verification === Mooky asked their users to take a selfie holding a piece of paper saying 'Mooky'. Mooky would then manually accept or decline the user verification.
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Tableau de Concordance

The Tableau de Concordance was the main French diplomatic code used during World War I; the term also refers to any message sent using the code. It was a superenciphered four-digit code that was changed three times between 1 August 1914 and 15 January 1915. The Tableau de Concordance is considered superenciphered because there is more than one step required to use it. First, each word in a message is replaced by four digits via a codebook. These four digits are divided into three groups (one digit, two digits, one digit) so that when the whole message has been translated into code, the four-digit sets can be put together so it looks like the entire message is made up of two-digit pairs. This is called a "Straddle Gimmick." Then, in turn, each of these two digit pairs (and the single digits at the beginning and end) are replaced by two letters. The letters are then combined with no spaces for the final ciphertext. The manual for the Tableau de Concordance included the instruction that if there was not adequate time for completely enciphering the message, it should simply be sent in clear, because a partially enciphered message would have provided insight into the inner workings of the code.
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Data custodian

In data governance groups, responsibilities for data management are increasingly divided between the business process owners and information technology (IT) departments. Two functional titles commonly used for these roles are data steward and data custodian. Data Stewards are commonly responsible for data content, context, and associated business rules. Data custodians are responsible for the safe custody, transport, storage of the data and implementation of business rules. Simply put, Data Stewards are responsible for what is stored in a data field, while data custodians are responsible for the technical environment and database structure. Common job titles for data custodians are database administrator (DBA), data modeler, ETL developer and data engineer. == Data custodian responsibilities == A data custodian ensures: Access to the data is authorized and controlled Data stewards are identified for each data set Technical processes sustain data integrity Processes exist for data quality issue resolution in partnership with data stewards Technical controls safeguard data Data added to data sets are consistent with the common data model Versions of master data are maintained along with the history of changes Change management practices are applied in maintenance of the database Data content and changes can be audited
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Data lineage

Data lineage refers to the process of tracking how data is generated, transformed, transmitted and used across systems over time. It documents data's origins, transformations and movements, providing detailed visibility into its life cycle. This process simplifies the identification of errors in data analytics workflows, by enabling users to trace issues back to their root causes. Data lineage facilitates the ability to replay specific segments or inputs of the dataflow. This can be used in debugging or regenerating lost outputs. In database systems, this concept is closely related to data provenance, which involves maintaining records of inputs, entities, systems and processes that influence data. Data provenance provides a historical record of data origins and transformations. It supports forensic activities such as data-dependency analysis, error/compromise detection, recovery, auditing and compliance analysis: "Lineage is a simple type of why provenance." Data governance plays a critical role in managing metadata by establishing guidelines, strategies and policies. Enhancing data lineage with data quality measures and master data management adds business value. Although data lineage is typically represented through a graphical user interface (GUI), the methods for gathering and exposing metadata to this interface can vary. Based on the metadata collection approach, data lineage can be categorized into three types: Those involving software packages for structured data, programming languages and Big data systems. Data lineage information includes technical metadata about data transformations. Enriched data lineage may include additional elements such as data quality test results, reference data, data models, business terminology, data stewardship information, program management details and enterprise systems associated with data points and transformations. Data lineage visualization tools often include masking features that allow users to focus on information relevant to specific use cases. To unify representations across disparate systems, metadata normalization or standardization may be required. == Representation of data lineage == Representation broadly depends on the scope of the metadata management and reference point of interest. Data lineage provides sources of the data and intermediate data flow hops from the reference point with backward data lineage, leading to the final destination's data points and its intermediate data flows with forward data lineage. These views can be combined with end-to-end lineage for a reference point that provides a complete audit trail of that data point of interest from sources to their final destinations. As the data points or hops increase, the complexity of such representation becomes incomprehensible. Thus, the best feature of the data lineage view is the ability to simplify the view by temporarily masking unwanted peripheral data points. Tools with the masking feature enable scalability of the view and enhance analysis with the best user experience for both technical and business users. Data lineage also enables companies to trace sources of specific business data to track errors, implement changes in processes and implementing system migrations to save significant amounts of time and resources. Data lineage can improve efficiency in business intelligence BI processes. Data lineage can be represented visually to discover the data flow and movement from its source to destination via various changes and hops on its way in the enterprise environment. This includes how the data is transformed along the way, how the representation and parameters change and how the data splits or converges after each hop. A simple representation of the Data Lineage can be shown with dots and lines, where dots represent data containers for data points, and lines connecting them represent transformations the data undergoes between the data containers. Data lineage can be visualized at various levels based on the granularity of the view. At a very high-level, data lineage is visualized as systems that the data interacts with before it reaches its destination. At its most granular, visualizations at the data point level can provide the details of the data point and its historical behavior, attribute properties and trends and data quality of the data passed through that specific data point in the data lineage. The scope of the data lineage determines the volume of metadata required to represent its data lineage. Usually, data governance and data management of an organization determine the scope of the data lineage based on their regulations, enterprise data management strategy, data impact, reporting attributes and critical data elements of the organization. == Rationale == Distributed systems like Google Map Reduce, Microsoft Dryad, Apache Hadoop (an open-source project) and Google Pregel provide such platforms for businesses and users. However, even with these systems, Big Data analytics can take several hours, days or weeks to run, simply due to the data volumes involved. For example, a ratings prediction algorithm for the Netflix Prize challenge took nearly 20 hours to execute on 50 cores, and a large-scale image processing task to estimate geographic information took 3 days to complete using 400 cores. "The Large Synoptic Survey Telescope is expected to generate terabytes of data every night and eventually store more than 50 petabytes, while in the bioinformatics sector, the 12 largest genome sequencing houses in the world now store petabytes of data apiece. It is very difficult for a data scientist to trace an unknown or an unanticipated result. === Big data debugging === Big data analytics is the process of examining large data sets to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information. Machine learning, among other algorithms, is used to transform and analyze the data. Due to the large size of the data, there could be unknown features in the data. The massive scale and unstructured nature of data, the complexity of these analytics pipelines, and long runtimes pose significant manageability and debugging challenges. Even a single error in these analytics can be extremely difficult to identify and remove. While one may debug them by re-running the entire analytics through a debugger for stepwise debugging, this can be expensive due to the amount of time and resources needed. Auditing and data validation are other major problems due to the growing ease of access to relevant data sources for use in experiments, the sharing of data between scientific communities and use of third-party data in business enterprises. As such, more cost-efficient ways of analyzing data intensive scale-able computing (DISC) are crucial to their continued effective use. === Challenges in Big Data debugging === ==== Massive scale ==== According to an EMC/IDC study, 2.8 ZB of data were created and replicated in 2012. Furthermore, the same study states that the digital universe will double every two years between now and 2020, and that there will be approximately 5.2 TB of data for every person in 2020. Based on current technology, the storage of this much data will mean greater energy usage by data centers. ==== Unstructured data ==== Unstructured data usually refers to information that doesn't reside in a traditional row-column database. Unstructured data files often include text and multimedia content, such as e-mail messages, word processing documents, videos, photos, audio files, presentations, web pages and many other kinds of business documents. While these types of files may have an internal structure, they are still considered "unstructured" because the data they contain doesn't fit neatly into a database. The amount of unstructured data in enterprises is growing many times faster than structured databases are growing. Big data can include both structured and unstructured data, but IDC estimates that 90 percent of Big Data is unstructured data. The fundamental challenge of unstructured data sources is that they are difficult for non-technical business users and data analysts alike to unbox, understand and prepare for analytic use. Beyond issues of structure, the sheer volume of this type of data contributes to such difficulty. Because of this, current data mining techniques often leave out valuable information and make analyzing unstructured data laborious and expensive. In today's competitive business environment, companies have to find and analyze the relevant data they need quickly. The challenge is going through the volumes of data and accessing the level of detail needed, all at a high speed. The challenge only grows as the degree of granularity increases. One possible solution is hardware. Some vendors are using increased memory and parallel processing to crunch large volumes of data quickly. Another method is putting data in-memory but using a grid
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Shape table

Shape tables are a feature of the Apple II ROMs which allows for manipulation of small images encoded as a series of vectors. An image (or shape) can be drawn in the high-resolution graphics mode—with scaling and rotation—via software routines in the ROM. Shape tables are supported via Applesoft BASIC and from machine code in the "Programmer's Aid" package that was bundled with the original Integer BASIC ROMs for that computer. Applesoft's high-resolution graphics routines were not optimized for speed, so shape tables were not typically used for performance-critical software such as games, which were typically written in assembly language and used pre-shifted bitmap shapes. Shape tables were used primarily for static shapes and sometimes for fancy text; Beagle Bros offered a number of fonts in Font Mechanic as Applesoft shape tables. == Technical details == The vectors of a two-dimensional graphic, each encoding a direction from the previous pixel along with a flag indicating whether the new pixel should be illuminated or not, were encoded up to three in a byte. These were stored in a table via the Monitor or the POKE command. From there, the graphic could be referenced by number (a table could contain up to 255 shapes), and built-in Applesoft routines permitted scaling, rotating, and drawing or erasing the shape. An XOR mode was also available to allow the shape to be visible on any color background; this had the advantage, also, of allowing the shape to be easily erased by redrawing it. Apple did not provide any utilities for creating shape tables; they had to be created by hand, usually by plotting on graph paper, then calculating the hexadecimal values and entering them into the computer. Beagle Bros created a shape table editing program, which eliminated the "number crunching", called Apple Mechanic, and a related program, Font Mechanic.
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Social business model

The social business model is use of social media tools and social networking behavioral standards by businesses for communication with customers, suppliers, and others. Combining social networking etiquette (being helpful, transparent and authentic) with business engagement on LinkedIn (for one-to-one interaction), Twitter (for immediacy) and Facebook (for content sharing) more fully involves employees in the organization and increases customer intimacy and trust. == Overview == Traditional business models, particularly in large organizations, have had as one common characteristic careful limitation of direct contact between those within the organization and those outside of it. Only certain specific individuals (most frequently in roles such as sales, customer service and field consulting) were designated as "customer-facing" personnel. Organizations further limited outside access to internal employees through filtering mechanisms such as publishing only a main switchboard number (whether routed through a live receptionist or an interactive voice response system) and generic "sales@" or "info@" email addresses. The Cluetrain Manifesto (written by Rick Levine, Christopher Locke, Doc Searls, and David Weinberger and published in 1999) was among the first books to predict the demise of this old order and the emergence of more open business models, though most of the business world was slow to adopt the book's recommended cultural changes. Thirteen years later, authors Dion Hinchcliffe and Peter Kim added structural underpinnings to the cultural shifts outlined in The Cluetrain Manifesto in their book, Social Business by Design. The book details many of the ways social media tools and practices are being adopted within organizations, to support both internal employee collaboration and external customer engagement (which the authors describe as the "bigger problem"). == Elements == In implementing the social business model, organizations apply social networking protocols and tools in a range of areas, potentially including: Marketing Customer Support Recruiting Crowdsourcing Internal employee collaboration Sales Product Development Supply Chain Operations Investor Relations == Characteristics of organizations adopting the social business model == Organizations that fully adopt the social business model will exhibit four key characteristics: Connected – employees will be able to seamlessly engage one-on-one in real-time with other employees and individuals outside the organization (customers, prospects, partners, media, etc.) using a variety of communications methods including text chat, voice, file sharing, email, and video chat. Social – employees will follow social networking etiquette (being authentic, helpful and transparent) in external interactions. The focus will be on answering questions and providing information rather than overt sales or promotion. Presence – these conversations may originate on the company's website or elsewhere online (e.g., publication websites, industry portals, or social networking sites such as LinkedIn or Facebook). Intelligent – organizations will use in-depth analytics to monitor connections, social interactions and presence; measure corresponding business results; and continually adjust and improve practices for increased effectiveness. == Technical and functional requirements == While much of the change inherent in adopting the social business model is cultural, it also requires process changes enabled by social business technology. Functional requirements for a social business technology platform include: Analytics (including the cost of engagement as well as various measures of return on investment such as leads, sales, referrals, recommendations, and retained customers). Integration with other social media and business tools such as CRM systems, partner relationship management (PRM) software, product development, website analytics, and employee-recruiting applications. Rules-based workflow (e.g. routing a comment to the appropriate individual for a response, based on content). Geolocation (so customers or prospects can be automatically routed to local sales or customer service representatives). Content sharing. Collaboration tools. Transparency (i.e., people should know who they are engaging with) Unified communications (the ability to engage via voice, text, video, email, and share a wide variety of file types) Storage (the ability to store interactions for legal, training, compliance or compensation purposes, and purge stored data when no longer needed based on company policy or regulatory requirements). Immediacy (real-time monitoring and response).
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Social advertising (social relationships)

Social advertising is advertising that relies on social information or networks in generating, targeting, and delivering marketing communications. Many current examples of social advertising use a particular Internet service to collect social information, establish and maintain relationships with consumers, and for delivering communications. For example, the advertising platforms provided by Google, Twitter, and Facebook involve targeting and presenting ads based on relationships articulated on those same services. Social advertising can be part of a broader social media marketing strategy designed to connect with consumers. == Social targeting == Since a pair of consumers connected via a relationship are more likely to be similar than an unconnected pair, information about such relationships can be used to infer characteristics of consumers useful for targeting. For example, predictions of an individual's home location can be improved using geographic information about their peers. Existing advertising platforms can allow advertisers to explicitly target the peers (e.g., Facebook friends, Twitter followers) of consumers who have a known affiliation with their brand. Thus, one way social advertising is expected to be effective is because social networks encode information about unobserved characteristics of consumers, including their susceptibility to adopt a product and to influence their peers to adopt. Social advertisement targets audiences' demographics based on customers browsing histories. This helped companies understand users' interests and target a specific group of users. Whether it is location or personal interest, different categories of companies can make the consumers on social media rely heavily on their advertisements. This is one of the reasons why social advertising has grown over time. Targeting their audience to real life stakeholders generally increase the attention of the advertised deal which brings up more profits for companies. Subsequently, the psychological effects that social media gives off to its users play a huge role in advertisement companies keeping their customers online. One of the main reasons users rely on social media is because it's a source of entertainment that provides them with a feeling of inclusiveness. In making the customers feel the inclusiveness, social advertising targeting a specific group of users is presented as if these advertisements are customized for the users in their perspective making them feel the attention that they do not often feel in the real world. You can use Social signals checker tool to find more information about links. Social signals are metrics that measure how much people interact with your content on social media. From likes, to shares, to comments; each of these signals contributes to an overall number that tells search engines like Google how much people like your content. The more social signals your website gets, the more likely it is to rank higher in Google. The reason for this is two-fold. First, social media is used by millions of people every day, and if your content is being shared and interacted with on these sites, it shows that it’s worthy of being seen. And second, social media sites are highly trusted by Google. So if you can get your content seen and interacted with on these platforms, you’ll be off to a great start. == Social cues in advertisements == Social ads often include information about the affiliation of a peer with an advertised entity. For example, a social ad might indicate a friend has endorsed a product, highly rated a restaurant, or watched a particular film. In fact, some definitions make these personalized social signals a necessary condition for the advertising being social advertising. Inclusion of personalized social signals creates a channel for social influence. Experiments that remove peers' names or images from social advertisements provide evidence that their presence increases proximal outcomes (e.g., clicks on advertisements). This is technically how trends are started on social media. Since social media links a single profile to thousands of other accounts some being real-life friends or even acquaintances, the opinions and the bias a user has for other users who are also a customer of an advertisement on the feed can heavily affect whether to click on the advertisement or not. Once this pattern continues, the brand benefits from increased customers, profit, and attention. Social networking can spread rapidly because 71 percent of the world's population contributes and uses social media which means social advertising gives companies a better marketing technique than a physical poster advertisement. == Word of mouth == Advertisers often attempt to use word of mouth to affect consumers and their decisions to adopt products and services. Ads and other inducements targeted at a seed set of individuals can be designed to produce a larger cascade of adoption through influence. Businesses are also using social media to attempt to identify and persuade influential consumers to spread positive messages about their products or services. Consequently, not only on social platforms but also in physical settings, users start talking to each other. When individuals develop an intimate relationship with each other, it is quite heavily based on shared characteristics, interests, and personalities. If one social media user becomes a regular customer to a well-known company that advertises often, there is a higher chance that all the other people who have intimate relationships with that one customer will be exposed to the online advertisement more than another user who might be completely new to a brand that is being advertised on screen. In reality, this happens to not only one user but to most of the users which mean a single brand advertisement online can have to potential of being talked about between billions and trillions of people all around the globe. == Relationship marketing == To accurately conduct relationship marketing, businesses must develop and manage six marketplaces: internal, customer, referral, supplier, influencer and employee. To maintain relationship marketing, customers often see social media influencers getting free sponsorships or PR boxes just to advertise their products. At times, users who become customers through these social influencers will get a better deal than regular customers which stands as a very commonly used marketing technique. By doing this, users think they are receiving special treatment when in reality it very much benefits social influencers and brands. Especially for brands that are just starting, they use this marketing technique so that their names can be out there, and people will start talking, which is their initial goal.
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Social media as a public utility

Social media as a public utility is a theory postulating that social networking sites (such as Meta - ie:Facebook & Instagram or Alphabet - ie: YouTube & Google, but also independent sites such as Twitter, Tumblr, Snapchat etc.) are essential public services that should be regulated by the government, in a manner similar to how electric and phone utilities are typically government regulated. It is based on the notion that social media platforms have monopoly power and broad social influence. == Background == === Definitions === Social media is defined as "a group of Internet-based applications that build on the ideological and technological foundations of Web 2.0, and that allow the creation and exchange of User Generated Content." Furthermore, the New Zealand Government of Internal Affairs describes it as "a set of online technologies, sites, and practices which are used to share opinions, experiences and perspectives. Fundamentally it is about the conversation. In contrast with traditional media, the nature of social media is to be highly interactive." Moreover, the term social media is described as online tools that let people interact and communicate with each other. This has become a standard word for online cultural exchange and a dominant way for individuals to engage on the internet. By using social media individuals become more closely and strongly connected than ever before. The traditional definition of the term public utility is "an infrastructural necessity for the general public where the supply conditions are such that the public may not be provided with a reasonable service at reasonable prices because of monopoly in the area." Conventional public utilities include water, natural gas, and electricity. In order to secure the interests of the public, utilities are regulated. Public utilities can also be seen as natural monopolies implying that the highest degree of efficiency is accomplished under one operator in the marketplace. Public utility regulation for social media has been largely criticized because people believe it would produce undesirable and indirect effects. However, others say that truly effective government regulation would produce valuable results. Social media as a public utility is a crucial debate because utilities get regulated, so marking social media websites as utilities would require government regulation of various social media websites and platforms such as Facebook, Google, and Twitter. Applying the term public utility to social media implies that social media websites are public necessities, and, consequently, should be regulated by the government. While social media are not as essential for survival as traditional public utilities such as electricity, water, and natural gas, many people believe it has become vital for living in an interconnected world and without it, living a successful life would be difficult. Therefore, many people believe that social media has reached utility status and should be treated as a public utility. However, others believe that this is not true because social media are constantly revolutionizing and giving such platforms "utility status" would result in government regulation, which would consequently hinder innovation. Over the past decade many have debated and questioned whether or not "Internet service providers should be considered essential facilities or natural monopolies and regulated as public utilities." === Monopoly === A monopoly is defined as "a firm that is the only seller of a product or service having no close substitutes." A natural monopoly is when the entire demand within a relevant market can be satisfied at lowest cost by one firm rather than by two or more, and if such a market contains more than one firm then the firms will "quickly shake down to one through mergers or failures, or production will continue to consume more resources than necessary." In a monopoly competition is said to be short-lived, and in a natural monopoly it is said to produce inefficient results." Public utility companies can be regulated to prevent them from gaining monopolistic control. In November 2011 AT&T's proposal for merging with T-Mobile was rejected because it would have "diminished competition," and have led to the company having monopolistic power within the telephone industry. Such regulation is permitted because the telephone industry is a public utility. Similarly, Microsoft has also been prevented from taking various business actions that could result in the company gaining monopolistic power. If social media were a public utility then regulation of Google and Facebook would similarly dictate what they could and could not do. The possibility was raised in 2018 by U.S. Representative Steve King during a House Judiciary hearing on social media filtering practices. == Arguments == Advocates of this theory believe that social media websites already act like public utilities, and therefore regulation is needed. Additionally, advocates say that in the 21st century, using such websites are as necessary for communication as using traditional public utilities such as telephone, water, electricity, and natural gas are for other everyday uses. Specifically, advocates note that Google search should be treated as a public utility and needs to be regulated because it dominates the search engine market and no website can afford to ignore it. There is the position that a social media website such as Google "is a common carrier and should be regulated as such (Newman 2011)." These are reinforced by a perception that social media companies fail to properly maintain fair platforms for discourse. === Individual level === Advocates of regulating social media as a public utility believe that having an Internet presence using social media websites is imperative for individuals to adequately take part in the 21st century. Consequently, they argue that these sites are public utilities that need to be regulated to ensure that the constitutional rights of users are protected. For example, regulation may be needed to protect freedom of speech against risks such as Internet censorship and deplatforming. Social media affects people's behavior. For instance, it plays an important role in shaping its users' decisions and actions pertaining to health. This is demonstrated in a Pew Research Center research, which showed that 72 percent of American adults turned to social media for health information in 2011. Around 70 percent of people with chronic illnesses also use the platform to find cure, diagnoses, and other health answers. This development becomes a public issue as social media are likely to provide wrong medical information. Additionally, social media sites can also facilitate deleterious health behavior such as smoking, drug use, and harmful sexual behavior. === Business level === Advocates of social media as a public utility maintain that social media services dominate the Internet and are mainly owned by three or four companies that have unparalleled power to shape user interaction, and because of this power such businesses need to be regulated as public utilities. Zeynep Tufekci, University of North Carolina Chapel Hill, claims that services on the Internet such as Google, eBay, Facebook, Amazon.com, are all natural monopolies. She has stated that these services "benefit greatly from network externalities[,] which means that the more people on the service, the more useful it is for everyone," and thus it is difficult to replace the market leader. === Government level === Advocates of social media as a public utility believe that the government should impose restrictions on social media websites, such as Google, that are designed to benefit its rivals. Due to the recent substantial growth of social media websites such as Google, advocates claim that such a website "might need search neutrality regulation modeled after net neutrality regulation and that a Federal Search Commission might be needed to enforce such a regime." danah boyd expresses a future issue which the government may have to deal with in her research: Facebook is becoming an international social media website, specifically prevalent in Canada and Europe which are "two regions that love to regulate their utilities." Furthermore, recent books by New America Foundation Senior Fellow Rebecca MacKinnon and law professor Lori Andrews advise society to start considering Facebook and Google as nation-states or the "sovereigns of cyberspace." Overall, advocates of social media as a public utility believe that due to the immense popularity and necessity of social media websites, it is imperative that the Government imposes regulations in the same manner they do for electricity, water, and natural gas. == Counterarguments == Opponents of this theory say that social media websites should not be treated as public utilities because these platforms are changing every year, and because they are not essential services for s
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Supper (Spotify)

Supper is a web-based application on the Spotify digital music streaming platform. The Supper app was born from a group of friends who had backgrounds in the music and gastronomy industries. Digital music solutions company Artisan Council later executed it. The app now sits in the top 40 applications on Spotify. == About == The Supper Spotify application matches recipes for all occasions and skill levels with a playlist for both preparation and presentation, as envisioned by the chefs themselves. Supper is credited with being one of the first apps to pair music with food. Playing on the social nature of music and food culture, users can seamlessly experience both for the first time with real time music streaming. == Supper.mx == In May 2014 Supper was launched outside of the Spotify streaming platform. Though still in partnership with Spotify, supper.mx allows users to view Supper's music + food collaborations on mobile, tablet and desktop, without the need to download Spotify directly. == Curators == All of the recipes and playlists featured on the Supper app come straight from a growing network of tastemakers, including chefs, musicians and institutions around the world. Each month the recipes and playlists are updated in conjunction with current holidays, events and seasons. === Launch === Launching in October 2013 the first edition of Supper featured content from a range of eating institutions and culture makers from the US and Australia. Brooklyn Bowl (Brooklyn) Roberta's Pizza (Brooklyn) Fancy Hanks (Melbourne) The Foresters/Queenies Upstairs (Sydney) Hipstamatic Panama House (Bondi) Sweetwater Inn (Melbourne) Soul Clap (Syd record label) Yellow Birds (Melbourne) === November 2013 === Yardbird (Hong Kong) Sonoma Bakery (Sydney) Do or Dine (Brooklyn) Cameo Gallery (Brooklyn) Hypertrak (Blog) Blue Smoke (NYC) The Crepes of Wrath (Blog) Willin Low // Wild Rocket - Wild Oats - Relish === December 2013 === The Copper Mill (Sydney) Thug Kitchen Mamak (Sydney) Tutu's (Brooklyn) Chin Chin (Melbourne) Flat Iron Steak (London) Greasy Spoon (Copenhagen) === January 2014 === Mexicali Taco & Co. (LA) Church & State (LA) Salts Cure (LA) Nopa (SF) L & E Oyster (LA) 4100 bar (LA) Golden Gopher (LA) The Pie Hole (LA) State Bird Provisions (SF) === Momofuku === In February 2014 Supper teamed up with restaurant heavy weights Momofuku. The recipes featured came from their iconic New York, Toronto and Sydney restaurants. Head office also got involved with an instructional from Brand Director Sue Chan on how to paint Momofuku vibes on to any party. === SXSW === March sees the Supper team migrate to Austin, Texas for SXSW, bringing together the best eateries the city has to offer as well as the music that has influenced them. Restaurants and eateries on board in 2014 included: The Backspace Kelis Swifts Attic Uchi Jackalope Paul Qui/East Side King Thai Kun Wonderland Hole in the Wall Justine's Brasserie The Liberty === Kelis === In April 2014 Kelis presented 5 of her recipes paired with a personal playlist for Supper. Kelis shared her recipes for apple farro, jerk ribs, New York vanilla bean cheesecake and Jerk Ribs. The Kelis/Supper collaboration coincided with the release of Kelis' 2014 album titled 'Food'. === Roberta's Pizza === In May 2014 Bushwick's Roberta's Pizza was guest curator on the Supper app and website. Included in their selections were restaurants and bars from across New York including Bun-ker Vietnamese, Old Stanley's Bar, St. Anselm, Chuko, Frank's Cocktail Lounge, Junior's Cheesecake, Xi'an Famous Foods, Xe Lua, 124 Old Rabbit and Yuji Ramen.
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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}}}
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Simply Local

Simply Local is a decentralized community social networking and neighborhood broadcasting service developed by Simply Local, based in New Delhi. The app is used as a tool by residents to bridge the information gap and know what is happening in the locality. Simply Local creates private geo-fenced networks for people living in an area and provides social and community related services within that network. The user doesn’t post to a single person but broadcasts to a chosen community. One of its primary purposes is also to connect citizens to their elected representatives. Each community is independent of the other and information shared remains telescoped to that particular community. The app has been designed to maintain privacy and security of users and provides decentralized social networking in the sense that it forms an owner-independent, micro community, which is not connected with the world outside. Simply Local is available on Android Play and iOS App Store. It is available in two languages - English and Hindi. Simply Local’s founder and CEO is Nikhil Bapna. == History == 2020 May: Included as a Top 5 Useful App by Zee News. 2020: Used to connect candidates with local residents during the Delhi assembly elections. 2019: Renamed from Gadfly to its current name. 2018: Used for Karnataka State Elections to get detailed information on candidates. 2017: Launched under the name Gadfly as a tool to connect citizens with their elected representatives.
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