AI Analytics Usf

AI Analytics Usf — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

  • Elements of AI

    Elements of AI

    Elements of AI is a massive open online course (MOOC) teaching the basics of artificial intelligence. The course, originally launched in 2018, is designed and organized by the University of Helsinki and learning technology company MinnaLearn. The course includes modules on machine learning, neural networks, the philosophy of artificial intelligence, and using artificial intelligence to solve problems. It consists of two parts: Introduction to AI and its sequel, Building AI, that was released in late 2020. In November 2019, the course was named one of four winners of MIT’s Inclusive Innovation Challenge. University of Helsinki's computer science department is known as the alma mater of Linus Torvalds, a Finnish-American software engineer who is the creator of the Linux kernel, which is the kernel for Linux operating systems. == EU’s AI pledge == The government of Finland has pledged to offer the course for all EU citizens by the end of 2021, as the course is made available in all the official EU languages. The initiative was launched as part of Finland's Presidency of the Council of the European Union in 2019, with the European Commission providing translations of the course materials. In 2017, Finland launched an AI strategy to stay competitive in the field of AI amid growing competition between China and the United States. With the support of private companies and the government, Finland's now-realized goal was to get 1 percent of its citizens to participate in Elements of AI. Other governments have also given their support to the course. For instance, Germany's Federal Minister for Economic Affairs and Energy Peter Altmeier has encouraged citizens to take part in the course to help Germany gain a competitive advantage in AI. Sweden's Minister for Energy and Minister for Digital Development Anders Ygeman has said that Sweden aims to teach 1 percent of its population the basics of AI like Finland has. == Participants == Elements of AI had enrolled more than 1 million students from more than 110 countries by May 2023. A quarter of the course's participants are aged 45 and over, and some 40 percent are women. Among Nordic participants, the share of women is nearly 60 percent. In September 2022, the course was available in Finnish, Swedish, Estonian, English, German, Latvian, Norwegian, French, Belgian, Czech, Greek, Slovakian, Slovenian, Latvian, Lithuanian, Portuguese, Spanish, Irish, Icelandic, Maltese, Croatian, Romanian, Italian, Dutch, Polish, and Danish.

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  • Semiotics of social networking

    Semiotics of social networking

    The semiotics of social networking discusses the images, symbols and signs used in systems that allow users to communicate and share experiences with each other. Examples of social networking systems include Facebook, Twitter and Instagram. == Semiotics == Semiotics is a discipline that studies images, symbols, signs and other similarly related objects in an effort to understand their use and meaning. Semiotic structuralism seeks the meaning of these objects within a social context. Post-structuralist theories take tools from structuralist semiotics in combination with social interaction, creating social semiotics. Social semiotics is “a branch of the field of semiotics which investigates human signifying practices in specific social and cultural circumstances and which tries to explain meaning-making as a social practice.” “Social semiotics also examines semiotic practices, specific to a culture and community, for the making of various kinds of texts and meanings in various situational contexts and contexts of culturally meaningful activity”. Social semiotics is concerned with studying human interactions. == Social networking == Social networking is the communication among people within a virtual social space. This medium of communication allows insight into the significance of social semiotics. “Millions of people now interact through blogs, collaborate through wikis, play multiplayer games, publish podcasts and video, build relationships through social network sites and evaluate all the above forms of communication through feedback and ranking mechanisms”. Social semiotics “unlike speech, writing necessitates some sort of technology in the form of person device interaction”. Social semiotics functions through the triad of communication or Peircean semiotics in the form of sign, object, interpretant (Chart 1) and “Human, Machine, Tag (Information)” (Chart 2). In Peircean semiotics (Chart 1), "A sign…[in the form of representamen] is something which stands to somebody for something in some respect or capacity. It addresses somebody, that is, creates in the mind of that person an equivalent sign, or perhaps a more developed sign. That sign which it creates I call the interpretant of the first sign. The sign stands for an object, not in all respects, but in reference to a sort of idea which I have something called the ground of the representamen". This example of the triangle of Human, Machine, Tag is shown when looking at tagging photographs on Facebook (Chart 3). The Human takes the photo on a camera and puts the digital file (information) on the Machine, the Machine is then navigated to Facebook where the file is downloaded. The Human has the Machine Tag the photo with information (e. g., names, places, data) for other Humans to see. This process then can be continued (see Chart 2). “Collaborative tagging has been quickly gaining ground because of its ability to recruit the activity of web users into effectively organizing and sharing large amounts of information”.

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  • Conjugate coding

    Conjugate coding

    Conjugate coding is a cryptographic tool, introduced by Stephen Wiesner in the late 1960s. It is part of the two applications Wiesner described for quantum coding, along with a method for creating fraud-proof banking notes. The application that the concept was based on was a method of transmitting multiple messages in such a way that reading one destroys the others. This is called quantum multiplexing and it uses photons polarized in conjugate bases as "qubits" to pass information. Conjugate coding also is a simple extension of a random number generator. At the behest of Charles Bennett, Wiesner published the manuscript explaining the basic idea of conjugate coding with a number of examples but it was not embraced because it was significantly ahead of its time. Because its publication has been rejected, it was developed to the world of public-key cryptography in the 1980s as oblivious transfer, first by Michael Rabin and then by Shimon Even. It is used in the field of quantum computing. The initial concept of quantum cryptography developed by Bennett and Gilles Brassard was also based on this concept.

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  • Private message

    Private message

    In computer networking, a private message (PM), or direct message (DM), refers to a private communication, often text-based, sent or received by a user of a private communication channel on any given platform. Unlike public posts, PMs are only viewable by the participants. Long a function present on IRCs and Internet forums, private channels for PMs have also been prevalent features on instant messaging (IM) and on social media networks. It may be either synchronous (e.g. on an IM) or asynchronous (e.g. on an Internet forum). The term private message (PM) originated as a feature on internet forums, while the term direct message (DM) originated as a feature on Twitter. Due to the popularity of the latter service, DM has since been appropriated by other platforms, such as Instagram, and is often genericized in popular usage. == Overview == There are two main types of private messages, and one obscure type: One type includes those found on IRCs and Internet forums, as well as on social media services like Twitter, Facebook, and Instagram, where the focus is public posting, PMs allow users to communicate privately without leaving the platform. The second type are those relayed through instant messaging platforms such as WhatsApp and Snapchat, where users join the networks primarily to exchange PMs. A third type, peer-to-peer messaging, occurs when users create and own the infrastructure used to transmit and store the messages; while features vary depending on application, they give the user full control over the data they transmit. An example of software that enables this kind of messaging is Classified-ads. Besides serving as a tool to connect privately with friends and family, PMs have gained momentum in the workplace. Working professionals use PMs to reach coworkers in other spaces and increase efficiency during meetings. Although useful, using PMs in the workplace may blur the boundary between work and private lives. Some common forms of private messaging today include Facebook messaging (sometimes referred to as "inboxing"), Twitter direct messaging, and Instagram direct messaging. These forms of private messaging provide a private space on a usually public site. For instance, most activity on Twitter is public, but Twitter DMs provide a private space for communication between two users. This differs from mediums like email, texting, and Snapchat, where most or all activity is always private. Modern forms of private messaging may include multimedia messages, such as pictures or videos. == History == Email was first developed to send messages between different computers on ARPANET in 1971. Access to ARPANET was primarily limited to universities and other research institutions. Starting in 1983 or 1984, FidoNet allowed home computer users to send and receive email via bulletin board systems. Information services such as CompuServe, America Online, and Prodigy also helped to popularizes online messaging. The advent of the public World Wide Web in 1993 increased access to email via internet service providers, and later via webmail. Instant messaging systems became popular in the mid 1990s, as Internet access improved and personal computers became more common. The introduction of Skype in 2003 popularized Internet-based voice and video messaging. Direct messaging is now a feature of all major social networking services. == Privacy concerns == In January 2014, Matthew Campbell and Michael Hurley filed a class-action lawsuit against Facebook for breaching the Electronic Communications Privacy Act. They alleged that private messages which contained URLs were being read and used to generate profit, through data mining and user profiling, and that it was misleading for Facebook to refer to the functionality as "private" with the implication that the communication was "free from surveillance". In 2012, some Facebook users misinterpreted a redesign of the Facebook wall as publicly sharing private messages from 2008–2009. These were found to be public wall posts from those years, made at a time when it was not possible to like or comment on a wall post, making the notes look like private messages.

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  • Zynn

    Zynn

    Zynn was a Chinese video-sharing social networking service owned by Kuaishou, a Beijing-based internet technology company established in 2011 by Su Hua and Cheng Yixiao. It was used to create and share short videos, and it pays its users for using the app and referring others. Zynn was launched on May 7, 2020. It became the most-downloaded app in the App Store in the same month. It has also been criticized for being a "pyramid scheme", and it has faced accusations of plagiarism and stealing content. Aside from Zynn in North America, Kuaishou is available under the name Kwai in Russia, South Korea, Japan, Thailand, Vietnam, Philippines, Malaysia, Indonesia, Brazil, America, India, and the Middle East. Kwai used to be available in Australia and the United States on the App Store, but was removed at an unknown date. Zynn was permanently shut down on the 20th of August, 2021. == History == In 2011, entrepreneur Su Hua co-founded Kuaishou with business partner Cheng Yixiao. Originally a GIF-making app, Kuaishou soon moved to short video content. Su Hua also serves as the current Kuaishou CEO. In December 2019, Chinese internet conglomerate Tencent invested $2 billion in Kuaishou, reportedly to compete with rival ByteDance. In December 2019, Kuaishou acquired an app developer called Owlii, which is the developer of Zynn. Zynn was developed to be a North American Market edition of Kuaishou. On May 7, 2020, the app was launched and it was downloaded over 2 million times in that month. On May 12, 2020, Kuaishou filed a lawsuit seeking compensation for "unfair competition", and accused Douyin, the sister app of TikTok, of "interfering" with search results on app stores. Zynn shut down on the 20th of August, 2021. == Features == Zynn allows its users to create, edit and share short videos of themselves. Its interface has been described as a "complete clone" of TikTok, its main competitor. The Zynn app was unique in the way that they paid users for using the platform. Each user earned $1 for signing up, and they could earn money for referring users to the platform. Watching videos resulted in earning "points", which could be redeemed for gift cards or be cashed out via PayPal.[1] == Criticisms and controversies == Multiple TikTok users had reported seeing their entire accounts plagiarized, with one account pretending to be Addison Rae. Despite being launched in May, many videos were posted in February. Zynn has employed "intermittent variable rewards" in its point system, which has been criticized as being the "same reinforcement strategy used to addict people to slot machines". Cash payouts for using the app have resulted in criticism and accusations of anti-competitive behavior. The app was taken down from the Google Play store on June 10. Zynn blamed it on an "isolated incident". Six days later, it was taken down from the App Store as well. US Senator Josh Hawley has criticized the platform, calling it "predatory" and "anti-competitive" in a letter to the Federal Trade Commission asking for an investigation into Zynn. He said "[Zynn] smacks of a textbook predatory-pricing scheme, one calculated to attain immediate market dominance for Zynn by driving competitors out of the market."

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  • Media intelligence

    Media intelligence

    Media intelligence uses data mining and data science to analyze public, social and editorial media content. It refers to marketing systems that synthesize billions of online conversations into relevant information. This allow organizations to measure and manage content performance, understand trends, and drive communications and business strategy. Media intelligence can include software as a service using big data terminology. This includes questions about messaging efficiency, share of voice, audience geographical distribution, message amplification, influencer strategy, journalist outreach, creative resonance, and competitor performance in all these areas. Media intelligence differs from business intelligence in that it uses and analyzes data outside company firewalls. Examples of that data are user-generated content on social media sites, blogs, comment fields, and wikis etc. It may also include other public data sources like press releases, news, blogs, legal filings, reviews and job postings. Media intelligence may also include competitive intelligence, wherein information that is gathered from publicly available sources such as social media, press releases, and news announcements are used to better understand the strategies and tactics being deployed by competing businesses. Media intelligence is enhanced by means of emerging technologies like ambient intelligence, machine learning, semantic tagging, natural language processing, sentiment analysis and machine translation. == Technologies used == Different media intelligence platforms use different technologies for monitoring, curating content, engaging with content, data analysis and measurement of communications and marketing campaign success. These technology providers may obtain content by scraping content directly from websites or by connecting to the API provided by social media, or other content platforms that are created for 3rd party developers to develop their own applications and services that access data. Technology companies may also get data from a data reseller. Some social media monitoring and analytics companies use calls to data providers each time an end-user develops a query. Others archive and index social media posts to provide end users with on-demand access to historical data and enable methodologies and technologies leveraging network and relational data. Additional monitoring companies use crawlers and spidering technology to find keyword references, known as semantic analysis or natural language processing. Basic implementation involves curating data from social media on a large scale and analyzing the results to make sense out of it.

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  • Social media surgery

    Social media surgery

    A social media surgery is a gathering at which volunteer "surgeons" with expertise in using web tools, chiefly social media, offer free advice in using such tools, to representatives ("patients") of non-profit organisations, charities, community groups and activists, with "no boring speeches or jargon". The idea was conceived by Pete Ashton, with Nick Booth of Podnosh Ltd, who ran the first such surgery in Birmingham, England, on 15 October 2008. In July 2009, a spin-off surgery (dubbed the "Social media mob") started in Mosman, Australia, and in January 2010, the first spin-off surgery in Africa was held. On 16 February 2012, it was announced that the Social Media Surgery movement had won "the Prime Minister’s Big Society Award". Prime Minister David Cameron said: This is an excellent initiative - such a simple idea and yet so effective. The popularity of these surgeries and the fact that they have inspired so many others across the country to follow in their footsteps, is testament to its brilliance. Congratulations to Nick and all the volunteers who have shared their time and expertise to help so many local groups make the most of the internet to support their community. A great example of the Big Society in action. The scheme also won the 2013 Adult Learners' Week "BBC Learning Through Technology Award".

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  • Software token

    Software token

    A software token (a.k.a. soft token) is a piece of a two-factor authentication security device that may be used to authorize the use of computer services. Software tokens are stored on a general-purpose electronic device such as a desktop computer, laptop, PDA, or mobile phone and can be duplicated. (Contrast hardware tokens, where the credentials are stored on a dedicated hardware device and therefore cannot be duplicated — absent physical invasion of the device) Because software tokens are something one does not physically possess, they are exposed to unique threats based on duplication of the underlying cryptographic material - for example, computer viruses and software attacks. Both hardware and software tokens are vulnerable to bot-based man-in-the-middle attacks, or to simple phishing attacks in which the one-time password provided by the token is solicited, and then supplied to the genuine website in a timely manner. Software tokens do have benefits: there is no physical token to carry, they do not contain batteries that will run out, and they are cheaper than hardware tokens. == Security architecture == There are two primary architectures for software tokens: shared secret and public-key cryptography. For a shared secret, an administrator will typically generate a configuration file for each end-user. The file will contain a username, a personal identification number, and the secret. This configuration file is given to the user. The shared secret architecture is potentially vulnerable in a number of areas. The configuration file can be compromised if it is stolen and the token is copied. With time-based software tokens, it is possible to borrow an individual's PDA or laptop, set the clock forward, and generate codes that will be valid in the future. Any software token that uses shared secrets and stores the PIN alongside the shared secret in a software client can be stolen and subjected to offline attacks. Shared secret tokens can be difficult to distribute, since each token is essentially a different piece of software. Each user must receive a copy of the secret, which can create time constraints. Some newer software tokens rely on public-key cryptography, or asymmetric cryptography. This architecture eliminates some of the traditional weaknesses of software tokens, but does not affect their primary weakness (ability to duplicate). A PIN can be stored on a remote authentication server instead of with the token client, making a stolen software token no good unless the PIN is known as well. However, in the case of a virus infection, the cryptographic material can be duplicated and then the PIN can be captured (via keylogging or similar) the next time the user authenticates. If there are attempts made to guess the PIN, it can be detected and logged on the authentication server, which can disable the token. Using asymmetric cryptography also simplifies implementation, since the token client can generate its own key pair and exchange public keys with the server.

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  • Screen space ambient occlusion

    Screen space ambient occlusion

    Screen space ambient occlusion (SSAO) is a computer graphics technique for efficiently approximating the ambient occlusion effect in real time. It was developed by Vladimir Kajalin while working at Crytek and was used for the first time in 2007 by the video game Crysis, also developed by Crytek. == Implementation == The algorithm is implemented as a pixel shader, analyzing the scene depth buffer which is stored in a texture. For every pixel on the screen, the pixel shader samples the depth values around the current pixel and tries to compute the amount of occlusion from each of the sampled points. In its simplest implementation, the occlusion factor depends only on the depth difference between sampled point and current point. Without additional smart solutions, such a brute force method would require about 200 texture reads per pixel for good visual quality. This is not acceptable for real-time rendering on current graphics hardware. In order to get high quality results with far fewer reads, sampling is performed using a randomly rotated kernel. The kernel orientation is repeated every N screen pixels in order to have only high-frequency noise in the final picture. In the end this high frequency noise is greatly removed by a NxN post-process blurring step taking into account depth discontinuities (using methods such as comparing adjacent normals and depths). Such a solution allows a reduction in the number of depth samples per pixel to about 16 or fewer while maintaining a high quality result, and allows the use of SSAO in soft real-time applications like computer games. Compared to other ambient occlusion solutions, SSAO has the following advantages: Independent from scene complexity. No data pre-processing needed, no loading time and no memory allocations in system memory. Works with dynamic scenes. Works in the same consistent way for every pixel on the screen. No CPU usage – it can be executed completely on the GPU. May be easily integrated into any modern graphics pipeline. SSAO also has the following disadvantages: Rather local and in many cases view-dependent, as it is dependent on adjacent texel depths which may be generated by any geometry whatsoever. Hard to correctly smooth/blur out the noise without interfering with depth discontinuities, such as object edges (the occlusion should not "bleed" onto objects). Because SSAO operates only on the current depth buffer, it can miss occluding geometry that is not rasterized into the z-buffer and may produce undersampling-related artifacts.

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  • Virtual collective consciousness

    Virtual collective consciousness

    Virtual collective consciousness (VCC) is a term rebooted and promoted by two behavioral scientists, Yousri Marzouki and Olivier Oullier in their 2012 Huffington Post article titled: "Revolutionizing Revolutions: Virtual Collective Consciousness and the Arab Spring", after its first appearance in 1999-2000. VCC is now defined as an internal knowledge catalyzed by social media platforms and shared by a plurality of individuals driven by the spontaneity, the homogeneity, and the synchronicity of their online actions. VCC occurs when a large group of persons, brought together by a social media platform think and act with one mind and share collective emotions. Thus, they are able to coordinate their efforts efficiently, and could rapidly spread their word to a worldwide audience. When interviewed about the concept of VCC that appeared in the book - Hyperconnectivity and the Future of Internet Communication - he edited, Professor of Pervasive Computing, Adrian David Cheok mentioned the following: "The idea of a global (collective) virtual consciousness is a bottom-up process and a rather emergent property resulting from a momentum of complex interactions taking place in social networks. This kind of collective behaviour (or intelligence) results from a collision between a physical world and a virtual world and can have a real impact in our life by driving collective action." == Etymology == In 1999-2000, Richard Glen Boire provided a cursory mention and the only occurrence of the term "Virtual collective consciousness" in his text as follows: The trend of technology is to overcome the limitations of the human body. And, the Web has been characterized as a virtual collective consciousness and unconsciousness The recent definition of VCC evolved from the first empirical study that provided a cyberpsychological insight into the contribution of Facebook to the 2011 Tunisian revolution. In this study, the concept was originally called "collective cyberconsciousness". The latter is an extension of the idea of "collective consciousness" coupled with "citizen media" usage. The authors of this study also made a parallel between this original definition of VCC and other comparable concepts such as Durkheim's collective representation, Žižek's "collective mind" or Boguta's "new collective consciousness" that he used to describe the computational history of the Internet shutdown during the Egyptian revolution. Since VCC is the byproduct of the network's successful actions, then these actions must be timely, acute, rapid, domain-specific, and purpose-oriented to successfully achieve their goal. Before reaching a momentum of complexity, each collective behavior starts by a spark that triggers a chain of events leading to a crystallized stance of a tremendous amount of interactions. Thus, VCC is an emergent global pattern from these individual actions. In 2012, the term virtual collective consciousness resurfaced and was brought to light after extending its applications to the Egyptian case and the whole social networking major impact on the success of the so-called Arab Spring. Moreover, the acronym VCC was suggested to identify the theoretical framework covering on-line behaviors leading to a virtual collective consciousness. Hence, online social networks have provided a new and faster way of establishing or modifying "collective consciousness" that was paramount to the 2011 uprisings in the Arab world. == Theoretical underpinnings of VCC == Various theoretical references in fields ranging from sociology to computer science were mentioned in order to account for the key features that render the framework for a virtual collective consciousness. The following list is not exhaustive, but the references it contains are often highlighted: Émile Durkheim's collective representations are at the heart of VCC since collectivity taken decisions according to Durkheim's assumptions will approve or disapprove individuals' actions and help them eventually reach their final goal. Marshall McLuhan's global village: The shrinking of our big world to a small place called cyberspace is made possible by technological extensions of human consciousness. Carl Jung's collective unconscious: When a society witnesses significant changes, the anchoring of archetypal images (e.g., political leaders) seems to be deeply rooted in individuals' collective unconscious that is likely to bias their political choices. Individual memories of public events were also supposed to convey a "collective awareness" that can be subconsciously altered by the instantaneous spread of information through social networking around the world. Daniel Wegner's transactive memory (TM): social-networking platforms such as Facebook during the Tunisian revolution or Twitter during the Egyptian revolution served as placeholders of a VCC where information can be harnessed and steered to the highly specific revolutionary purpose. Although research on TM was originally limited to couples, small groups, and organizations, recent studies strongly suggest that an effective TM can operate on a very large scale too. James Surowiecki's wisdom of crowds Collective influence algorithm: The CI (Collective influence) algorithm is effective in finding influential nodes in a variety of networks, including social networks, communication networks, and biological networks. It has been used to identify influencers on social-media platforms, to identify key nodes in transportation networks, and to identify potential drug-targets in biological networks. == Some illustrations of VCC == Besides the studied effect of social networking on the Tunisian and Egyptian revolutions, the former via Facebook and the latter via Twitter other applications were studied under the prism of VCC framework: The Whitacre's virtual choir: A compelling example of the degree of autonomy and self-identity members of a spontaneously created network through a VCC is Eric Whitacre's unique musical project that involved a collection of singers performing remotely to create a virtual Choir. The effect of all the voices illustrated a genuine virtual collective empathy merging the artist's mind with all the singers through his silent conducting gestures. The Harlem Shake dance: The Bitcoin protocol: It was questioned whether or not the Bitcoin protocol can morph into virtual collective consciousness. The Byzantine generals problem was used as an analogy to understand the behavioral complexity of the community of Bitcoin's users. Artificial Social Networking Intelligence (ASNI): refers to the application of artificial intelligence within social networking services and social media platforms. It encompasses various technologies and techniques used to automate, personalize, enhance, improve, and synchronize users' interactions and experiences within social networks. ASNI is expected to evolve rapidly, influencing how we interact online and shaping our digital experiences. Transparency, ethical considerations, media influence bias, and user control over data will be crucial to ensure responsible development and positive impact.

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  • Signals intelligence

    Signals intelligence

    Signals intelligence (SIGINT) is the act and field of intelligence-gathering by interception of signals, whether communications between people (communications intelligence—abbreviated to COMINT) or from electronic signals not directly used in communication (electronic intelligence—abbreviated to ELINT). As classified and sensitive information is usually encrypted, signals intelligence may necessarily involve cryptanalysis (to decipher the messages). Traffic analysis—the study of who is signaling to whom and in what quantity—is also used to integrate information, and it may complement cryptanalysis. == History == === Origins === Electronic interceptions appeared as early as 1900, during the Boer War of 1899–1902. The British Royal Navy had installed wireless sets produced by Marconi on board their ships in the late 1890s, and the British Army used some limited wireless signalling. The Boers captured some wireless sets and used them to make vital transmissions. Since the British were the only people transmitting at the time, the British did not need special interpretation of the signals that they were. The birth of signals intelligence in a modern sense dates from the Russo-Japanese War of 1904–1905. As the Russian fleet prepared for conflict with Japan in 1904, the British ship HMS Diana stationed in the Suez Canal intercepted Russian naval wireless signals being sent out for the mobilization of the fleet, for the first time in history. === Development in World War I === Over the course of the First World War, a new method of signals intelligence reached maturity. Russia's failure to properly protect its communications fatally compromised the Russian Army's advance early in World War I and led to their disastrous defeat by the Germans under Ludendorff and Hindenburg at the Battle of Tannenberg. In 1918, French intercept personnel captured a message written in the new ADFGVX cipher, which was cryptanalyzed by Georges Painvin. This gave the Allies advance warning of the German 1918 Spring Offensive. The British in particular, built up great expertise in the newly emerging field of signals intelligence and codebreaking (synonymous with cryptanalysis). On the declaration of war, Britain cut all German undersea cables. This forced the Germans to communicate exclusively via either (A) a telegraph line that connected through the British network and thus could be tapped; or (B) through radio which the British could then intercept. Rear Admiral Henry Oliver appointed Sir Alfred Ewing to establish an interception and decryption service at the Admiralty; Room 40. An interception service known as 'Y' service, together with the post office and Marconi stations, grew rapidly to the point where the British could intercept almost all official German messages. The German fleet was in the habit each day of wirelessing the exact position of each ship and giving regular position reports when at sea. It was possible to build up a precise picture of the normal operation of the High Seas Fleet, to infer from the routes they chose where defensive minefields had been placed and where it was safe for ships to operate. Whenever a change to the normal pattern was seen, it immediately signalled that some operation was about to take place, and a warning could be given. Detailed information about submarine movements was also available. The use of radio-receiving equipment to pinpoint the location of any single transmitter was also developed during the war. Captain H.J. Round, working for Marconi, began carrying out experiments with direction-finding radio equipment for the army in France in 1915. By May 1915, the Admiralty was able to track German submarines crossing the North Sea. Some of these stations also acted as 'Y' stations to collect German messages, but a new section was created within Room 40 to plot the positions of ships from the directional reports. Room 40 played an important role in several naval engagements during the war, notably in detecting major German sorties into the North Sea. The battle of Dogger Bank was won in no small part due to the intercepts that allowed the Navy to position its ships in the right place. It played a vital role in subsequent naval clashes, including at the Battle of Jutland as the British fleet was sent out to intercept them. The direction-finding capability allowed for the tracking and location of German ships, submarines, and Zeppelins. The system was so successful that by the end of the war, over 80 million words, comprising the totality of German wireless transmission over the course of the war, had been intercepted by the operators of the Y-stations and decrypted. However, its most astonishing success was in decrypting the Zimmermann Telegram, a telegram from the German Foreign Office sent via Washington to its ambassador Heinrich von Eckardt in Mexico. === Postwar consolidation === With the importance of interception and decryption firmly established by the wartime experience, countries established permanent agencies dedicated to this task in the interwar period. In 1919, the British Cabinet's Secret Service Committee, chaired by Lord Curzon, recommended that a peace-time codebreaking agency should be created. The Government Code and Cypher School (GC&CS) was the first peace-time codebreaking agency, with a public function "to advise as to the security of codes and cyphers used by all Government departments and to assist in their provision", but also with a secret directive to "study the methods of cypher communications used by foreign powers". GC&CS officially formed on 1 November 1919, and produced its first decrypt on 19 October. By 1940, GC&CS was working on the diplomatic codes and ciphers of 26 countries, tackling over 150 diplomatic cryptosystems. The US Cipher Bureau was established in 1919 and achieved some success at the Washington Naval Conference in 1921, through cryptanalysis by Herbert Yardley. Secretary of War Henry L. Stimson closed the US Cipher Bureau in 1929 with the words "Gentlemen do not read each other's mail." === World War II === The use of SIGINT had even greater implications during World War II. The combined effort of intercepts and cryptanalysis for the whole of the British forces in World War II came under the code name "Ultra", managed from Government Code and Cypher School at Bletchley Park. Properly used, the German Enigma and Lorenz ciphers should have been virtually unbreakable, but flaws in German cryptographic procedures, and poor discipline among the personnel carrying them out, created vulnerabilities which made Bletchley's attacks feasible. Bletchley's work was essential to defeating the U-boats in the Battle of the Atlantic, and to the British naval victories in the Battle of Cape Matapan and the Battle of North Cape. In 1941, Ultra exerted a powerful effect on the North African desert campaign against German forces under General Erwin Rommel. General Sir Claude Auchinleck wrote that were it not for Ultra, "Rommel would have certainly got through to Cairo". Ultra decrypts featured prominently in the story of Operation SALAM, László Almásy's mission across the desert behind Allied lines in 1942. Prior to the Normandy landings on D-Day in June 1944, the Allies knew the locations of all but two of Germany's fifty-eight Western Front divisions. Winston Churchill was reported to have told King George VI: "It is thanks to the secret weapon of General Menzies, put into use on all the fronts, that we won the war!" Supreme Allied Commander, Dwight D. Eisenhower, at the end of the war, described Ultra as having been "decisive" to Allied victory. Official historian of British Intelligence in World War II Sir Harry Hinsley argued that Ultra shortened the war "by not less than two years and probably by four years"; and that, in the absence of Ultra, it is uncertain how the war would have ended. At a lower level, German cryptanalysis, direction finding, and traffic analysis were vital to Rommel's early successes in the Western Desert Campaign until British forces tightened their communications discipline and Australian raiders destroyed his principal SIGINT Company. == Technical definitions == The United States Department of Defense has defined the term "signals intelligence" as: A category of intelligence comprising either individually or in combination all communications intelligence (COMINT), electronic intelligence (ELINT), and foreign instrumentation signals intelligence (FISINT), however transmitted. Intelligence derived from communications, electronic, and foreign instrumentation signals. Being a broad field, SIGINT has many sub-disciplines. The two main ones are communications intelligence (COMINT) and electronic intelligence (ELINT). == Disciplines shared across the branches == === Targeting === A collection system has to know to look for a particular signal. "System", in this context, has several nuances. Targeting is the process of developing collection requirements: "1. A

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  • Letter frequency

    Letter frequency

    Letter frequency is the number of times letters of the alphabet appear on average in written language. Letter frequency analysis dates back to the Arab mathematician Al-Kindi (c. AD 801–873), who formally developed the method to break ciphers. Letter frequency analysis gained importance in Europe with the development of movable type in AD 1450, wherein one must estimate the amount of type required for each letterform. Linguists use letter frequency analysis as a rudimentary technique for language identification, where it is particularly effective as an indication of whether an unknown writing system is alphabetic, syllabic, or logographic. The use of letter frequencies and frequency analysis plays a fundamental role in cryptograms and several word puzzle games, including hangman, Scrabble, Wordle and the television game show Wheel of Fortune. One of the earliest descriptions in classical literature of applying the knowledge of English letter frequency to solving a cryptogram is found in Edgar Allan Poe's famous story "The Gold-Bug", where the method is successfully applied to decipher a message giving the location of a treasure hidden by Captain Kidd. Herbert S. Zim, in his classic introductory cryptography text Codes and Secret Writing, gives the English letter frequency sequence as "ETAON RISHD LFCMU GYPWB VKJXZQ", the most common letter pairs as "TH HE AN RE ER IN ON AT ND ST ES EN OF TE ED OR TI HI AS TO", and the most common doubled letters as "LL EE SS OO TT FF RR NN PP CC". Different ways of counting can produce somewhat different orders. Letter frequencies also have a strong effect on the design of some keyboard layouts. The most frequent letters are placed on the home row of the Blickensderfer typewriter, the Dvorak keyboard layout, Colemak and other optimized layouts, while the commonly used QWERTY layout places common letters apart from each other to prevent typewriter jamming. == Background == The frequency of letters in text has been studied for use in cryptanalysis, and frequency analysis in particular, dating back to the Arab mathematician al-Kindi (c. AD 801–873 ), who formally developed the method (the ciphers breakable by this technique go back at least to the Caesar cipher used by Julius Caesar, so this method could have been explored in classical times). Letter frequency analysis gained additional importance in Europe with the development of movable type in AD 1450, wherein one must estimate the amount of type required for each letterform, as evidenced by the variations in letter compartment size in typographer's type cases. No exact letter frequency distribution underlies a given language, since all writers write slightly differently. However, most languages have a characteristic distribution which is strongly apparent in longer texts. Even language changes as extreme as from Old English to modern English (regarded as mutually unintelligible) show strong trends in related letter frequencies: over a small sample of Biblical passages, from most frequent to least frequent, enaid sorhm tgþlwu æcfy ðbpxz of Old English compares to eotha sinrd luymw fgcbp kvjqxz of modern English, with the most extreme differences concerning letterforms not shared. Linotype machines for the English language assumed the letter order, from most to least common, to be etaoin shrdlu cmfwyp vbgkqj xz based on the experience and custom of manual compositors. The equivalent for the French language was elaoin sdrétu cmfhyp vbgwqj xz. Arranging the alphabet in Morse into groups of letters that require equal amounts of time to transmit, and then sorting these groups in increasing order, yields e it san hurdm wgvlfbk opxcz jyq. Letter frequency was used by other telegraph systems, such as the Murray Code. Similar ideas are used in modern data-compression techniques such as Huffman coding. Letter frequencies, like word frequencies, tend to vary, both by writer and by subject. For instance, ⟨d⟩ occurs with greater frequency in fiction, as most fiction is written in past tense and thus most verbs will end in the inflectional suffix -ed / -d. One cannot write an essay about x-rays without using ⟨x⟩ frequently, and the essay will have an idiosyncratic letter frequency if the essay is about, say, Queen Zelda of Zanzibar requesting X-rays from Qatar to examine hypoxia in zebras. Different authors have habits which can be reflected in their use of letters. Hemingway's writing style, for example, is visibly different from Faulkner's. Letter, bigram, trigram, word frequencies, word length, and sentence length can be calculated for specific authors and used to prove or disprove authorship of texts, even for authors whose styles are not so divergent. Accurate average letter frequencies can only be gleaned by analyzing a large amount of representative text. With the availability of modern computing and collections of large text corpora, such calculations are easily made. Examples can be drawn from a variety of sources (press reporting, religious texts, scientific texts and general fiction) and there are differences especially for general fiction with the position of ⟨h⟩ and ⟨i⟩, with ⟨h⟩ becoming more common. Different dialects of a language will also affect a letter's frequency. For example, an author in the United States would produce something in which ⟨z⟩ is more common than an author in the United Kingdom writing on the same topic: words like "analyze", "apologize", and "recognize" contain the letter in American English, whereas the same words are spelled "analyse", "apologise", and "recognise" in British English. This would highly affect the frequency of the letter ⟨z⟩, as it is rarely used by British writers in the English language. The "top twelve" letters constitute about 80% of the total usage. The "top eight" letters constitute about 65% of the total usage. Letter frequency as a function of rank can be fitted well by several rank functions, with the two-parameter Cocho/Beta rank function being the best. Another rank function with no adjustable free parameter also fits the letter frequency distribution reasonably well (the same function has been used to fit the amino acid frequency in protein sequences.) A spy using the VIC cipher or some other cipher based on a straddling checkerboard typically uses a mnemonic such as "a sin to err" (dropping the second "r") or "at one sir" to remember the top eight characters. == Relative frequencies of letters in the English language == There are three ways to count letter frequency that result in very different charts for common letters. The first method, used in the chart below, is to count letter frequency in lemmas of a dictionary. The lemma is the word in its canonical form. The second method is to include all word variants when counting, such as "abstracts", "abstracted" and "abstracting" and not just the lemma of "abstract". This second method results in letters like ⟨s⟩ appearing much more frequently, such as when counting letters from lists of the most used English words on the Internet. ⟨s⟩ is especially common in inflected words (non-lemma forms) because it is added to form plurals and third person singular present tense verbs. A final method is to count letters based on their frequency of use in actual texts, resulting in certain letter combinations like ⟨th⟩ becoming more common due to the frequent use of common words like "the", "then", "both", "this", etc. Absolute usage frequency measures like this are used when creating keyboard layouts or letter frequencies in old fashioned printing presses. An analysis of entries in the Concise Oxford dictionary, ignoring frequency of word use, gives an order of "EARIOTNSLCUDPMHGBFYWKVXZJQ". The letter-frequency table above is taken from Pavel Mička's website, which cites Robert Lewand's Cryptological Mathematics. According to Lewand, arranged from most to least common in appearance, the letters are: etaoinshrdlcumwfgypbvkjxqz. Lewand's ordering differs slightly from others, such as Cornell University Math Explorer's Project, which produced a table after measuring 40,000 words. In English, the space character occurs almost twice as frequently as the top letter (⟨e⟩) and the non-alphabetic characters (digits, punctuation, etc.) collectively occupy the fourth position (having already included the space) between ⟨t⟩ and ⟨a⟩. == Relative frequencies of the first letters of a word in the English language == The frequency of the first letters of words or names is helpful in pre-assigning space in physical files and indexes. Given 26 filing cabinet drawers, rather than a 1:1 assignment of one drawer to one letter of the alphabet, it is often useful to use a more equal-frequency-letter code by assigning several low-frequency letters to the same drawer (often one drawer is labeled VWXYZ), and to split up the most-frequent initial letters (⟨s, a, c⟩) into several drawers (often 6 drawers Aa-An, Ao-Az, Ca-Cj, Ck-Cz, Sa-Si, Sj-Sz). The same system is used in some mult

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  • Cognition Network Technology

    Cognition Network Technology

    Cognition Network Technology (CNT), also known as Definiens Cognition Network Technology, is an object-based image analysis method developed by Nobel laureate Gerd Binnig together with a team of researchers at Definiens AG in Munich, Germany. It serves for extracting information from images using a hierarchy of image objects (groups of pixels), as opposed to traditional pixel processing methods. To emulate the human mind's cognitive powers, Definiens used patented image segmentation and classification processes, and developed a method to render knowledge in a semantic network. CNT examines pixels not in isolation, but in context. It builds up a picture iteratively, recognizing groups of pixels as objects. It uses the color, shape, texture and size of objects as well as their context and relationships to draw conclusions and inferences, similar to human analysis. == History == In 1994 Professor Gerd Binnig founded Definiens. CNT was first available with the launch of the eCognition software in May 2000. In June 2010, Trimble Navigation Ltd (NASDAQ: TRMB) acquired Definiens business asset in earth sciences markets, including eCognition software, and also licensed Definiens' patented CNT. In 2014, Definiens was acquired by MedImmune, the global biologics research and development arm of AstraZeneca, for an initial consideration of $150 million. == Software == Definiens Tissue Studio Definiens Tissue Studio is a digital pathology image analysis software application based on CNT. The intended use of Definiens Tissue Studio is for biomarker translational research in formalin-fixed, paraffin-embedded tissue samples which have been treated with immunohistochemical staining assays, or hematoxylin and eosin (H&E). The central concept behind Definiens Tissue Studio is a user interface that facilitates machine learning from example digital histopathology images to derive an image analysis solution suitable for the measurement of biomarkers and/or histological features within pre-defined regions of interest on a cell-by-cell basis, and within sub-cellular compartments. The derived image analysis solution is then automatically applied to subsequent digital images to objectively measure defined sets of multiparametric image features. These data sets are used for further understanding the underlying biological processes that drive cancer and other diseases. Image processing and data analysis are performed either on a local desktop computer workstation, or on a server grid. eCognition The eCognition suite offers three components that can be used stand-alone or in combination to solve image analysis tasks. eCognition Developer is a development environment for object-based image analysis. It is used in earth sciences to develop rule sets (or applications) for the analysis of remote sensing data. eCognition Architect enables non-technical users to configure, calibrate and execute image analysis workflows created in eCognition Developer. eCognition Server software provides a processing environment for batch execution of image analysis jobs. eCognition software is utilized in numerous remote sensing and geospatial application scenarios and environments, using a variety of data types: Generic: Rapid Mapping, Change Detection, Object Recognition By environment: Diverse Landcover Mapping, Urban Analysis (i.e. impervious surface area analysis for taxation, property assessment for insurance, inventory of green infrastructure), Forestry (i.e. biomass measurement, species identification, firescar measurement), Agriculture (i.e. regional planning, precision farming, crisis response), Marine and Riparian (i.e. ecosystem evaluation, disaster management, harbor monitoring). Other: Defense, security, atmosphere and climate The online eCognition community was launched in July 2009 and had 2813 members as of July 9, 2010. Membership is distributed globally and user conferences are held regularly, the last having taken place in November 2009 in Munich, Germany. The bi-annual GEOBIA (Geographic Object-Based Image Analysis) conference is heavily attended by eCognition users, with the majority of presentations based on eCognition software.

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  • Key Transparency

    Key Transparency

    Key Transparency allows communicating parties to verify public keys used in end-to-end encryption. In many end-to-end encryption services, to initiate communication a user will reach out to a central server and request the public keys of the user with which they wish to communicate. If the central server is malicious or becomes compromised, a man-in-the-middle attack can be launched through the issuance of incorrect public keys. The communications can then be intercepted and manipulated. Additionally, legal pressure could be applied by surveillance agencies to manipulate public keys and read messages. With Key Transparency, public keys are posted to a public log that can be universally audited. Communicating parties can verify public keys used are accurate.

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  • Content-oriented workflow models

    Content-oriented workflow models

    In data management, a content-oriented workflow model seeks to articulate workflow progression by the presence of content units (like data-records/objects/documents). Most content-oriented workflow approaches provide a life-cycle model for content units, such that workflow progression can be qualified by conditions on the state of the units. Most approaches are research and work in progress and the content models and life-cycle models are more or less formalized. The term content-oriented workflows is an umbrella term for several scientific workflow approaches, namely "data-driven", "resource-driven", "artifact-centric", "object-aware", and "document-oriented". Thus, the meaning of "content" ranges from simple data attributes to self-contained documents; the term "content-oriented workflows" appeared at first in as an umbrella term. Such a general term, independent from a specific approach, is necessary to contrast the content-oriented modelling principle with traditional activity-oriented workflow models (like Petri nets or BPMN) where a workflow is driven by a control flow and where the content production perspective is neglected or even missing. The term "content" was chosen to subsume the different levels in granularity of the content units in the respective workflow models; it was also chosen to make associations with content management. Both terms "artifact-centric" and "data-driven" would also be good candidates for an umbrella term, but each is closely related to a specific approach of a single working group. The "artifact-centric" group itself (i.e. IBM Research) has generalized the characteristics of their approach and has used "information-centric" as an umbrella term in. Yet, the term information is too unspecific in the context of computer science, thus, "content-orientated workflows" is considered as good compromise. == Workflow Model Approaches == === Data-driven === The data-driven process structures provides a sophisticated workflow model being specialized on hierarchical write-and-review-processes. The approach provides interleaved synchronization of sub-processes and extends activity diagrams. Unfortunately, the COREPRO prototype implementation is not publicly available. Research on the project had been ceased. The general idea has been continued by Reichert in form of the #Object-aware approach. Synonyms data-driven process structures / data-driven modeling and coordination Protagonists Dr. Dominic Müller (University of Twente), Joachim Herbst (DaimlerChrysler Research), and Manfred Reichert (at this time Assoc. Prof. at Univ. of Twente, currently Prof. at Ulm Univ.) Organization(s) University of Twente, DaimlerChrysler Period 2005 - 2007 Selected publications Implementation COREPRO === Resource-driven === The resource-driven workflow system is an early approach that considered workflows from a content-oriented perspective and emphasizes on the missing support for plain document-driven processes by traditional activity-oriented workflow engines. The resource-driven approach demonstrated the application of database triggers for handling workflow events. Still the system implementation is centralized and the workflow schema is statically defined. The project appeared in 2005 but many aspects are considered future work by the authors. Research did not continue on the project. Wang completed his PhD thesis in 2009, yet, his thesis does not mention the resource-driven approach to workflow modelling but is about discrete event simulation. Synonyms Resource-based Workflows / Document-Driven Workflow Systems Protagonists Jianrui Wang and Prof. Akhil Kumar Organization Pennsylvania State University Period 2005 - today Selected publications Implementation N/A === Artifact-centric === The artifact-centric approach provides a framework for content-oriented workflows. In this model, the enterprise application landscape includes distributed business services, while the workflow engine is centralized. Process enactment is integrated with database management system infrastructure, and the project is funded by IBM. Synonyms artifact-centric business process models / artifact-based business process (ACP) / artifact-centric workflows Protagonists Richard Hull and Dr. Kamal Bhattacharya as well as Cagdas E. Gerede and Jianwen Su Organization IBM (T.J. Watson Research Center, NY) Period 2007 - today Selected publications Implementation ArtiFact === Object-aware === The object-aware approach manages a set of object types and generates forms for creating object instances. The form completion flow is controlled by transitions between object configurations each describing a progressing set of mandatory attributes. Each object configuration is named by an object state. The data production flow is user-shifting and it is discrete by defining a sequence of object states. The discussion is currently limited to a centralized system, without any workflows across different organizations. However, the approach is of great relevance to many domains like concurrent engineering. Finally, the object-aware approach and its PHILharmonicFlows system are going to provide general-purpose workflow systems for generic enactment of data production processes. Synonyms object-aware process management / datenorientiertes Prozess-Management-System Protagonists Vera Künzle and Prof. Manfred Reichert Organization Ulm University Period 2009 - today Selected publications Implementation PHILharmonicFlows === Distributed Document-oriented === Distributed document-oriented process management (dDPM) enables distributed case handling in heterogeneous system environments and it is based on document-oriented integration. The workflow model reflects the paper-based working practice in inter-institutional healthcare scenarios. It targets distributed knowledge-driven ad hoc workflows, wherein distributed information systems are required to coordinate work with initially unknown sets of actors and activities. The distributed workflow engine supports process planning & process history as well as participant management and process template creation with import/export. The workflow engine embeds a functional fusion of 1) group-based instant messaging 2) with a shared work list editor 3) with version control. The software implementation of dDPM is α-Flow which is available as open source. dDPM and α-Flow provide a content-oriented approach to schema-less workflows. The complete distributed case handling application is provided in form of a single active Document ("α-Doc"). The α-Doc is a case file (as information carrier) with an embedded workflow engine (in form of active properties). Inviting process participants is equivalent to providing them with a copy of an α-Doc, copying it like an ordinary desktop file. All α-Docs that belong to the same case can synchronize each other, based on the participant management, electronic postboxes, store-and-forward messaging, and an offline-capable synchronization protocol. Synonyms distributed document-oriented process management (dDPM), distributed case handling via active documents Protagonists Christoph P. Neumann and Prof. Richard Lenz Organization Friedrich-Alexander-Universität Erlangen-Nürnberg Period 2009 - 2012 Selected Publications and a PhD thesis Implementation α-Flow (open source) == Related Concepts == === Content Management === The bandwidth of Content management systems (CMS) reaches from Web content management systems (WCMS) and Document management system (DMS) to Enterprise Content Management (ECM). Mature DMS products support document production workflows in a basic form, primarily focusing on review cycle workflows concerning a single document. === Groupware and Computer-Supported Cooperative Work === Groupware focuses on messaging (like E-Mail, Chat, and Instant Messaging), shared calendars (e.g. Lotus Notes, Microsoft Outlook with Exchange Server), and conferencing (e.g. Skype). Groupware overlaps with Computer-supported cooperative work (CSCW), that originated from shared multimedia editors (for live drawing/sketching) and synchronous multi-user applications like desktop sharing. The extensive conceptual claim of CSWC must be put into perspective by its actual solution scope, that is available as the CSCW Matrix. === Case Handling === The case handling paradigm stems from Prof. van der Aalst and gained momentum in 2005. The core features are: (a) provide all information available, i.e. present the case as a whole rather than showing bits and pieces, (b) decide about activities on the basis of the information available rather than the activities already executed, (c) separate work distribution from authorization and allow for additional types of roles, not just the execute role, and (d) allow workers to view and add/modify data before or after the corresponding activities have been executed. In healthcare, the flow of a patient between healthcare professionals is considered as a workflow - with activities that inc

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