AI Coding Projects

AI Coding Projects — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

  • Conduit (company)

    Conduit (company)

    Conduit Ltd. is an international software company. From its founding in 2005 to 2013, its most well-known product was the Conduit toolbar, which was widely-described as malware. In 2013, it spun off its toolbar business; today, its main product is a mobile development platform that allows users to create native and web mobile applications for smartphones. == Products == From 2005 to 2013, the company's most well-known product was the Conduit toolbar, which is flagged by most antivirus software as potentially unwanted and adware. Conduit's toolbar software is often downloaded by malware packages from other publishers. The company spun off the toolbar division that manages the Conduit toolbar in 2013. Today, the company's main product is a mobile development platform that allows users to create native and web mobile applications for smartphones. App creation for its App Gallery is free, but it charges a monthly subscription fee to place apps on the App Store or Google Play. == History == Conduit was founded in 2005 by Shilo, Dror Erez, and Gaby Bilcyzk. Between years 2005 and 2013, it ran a successful but controversial toolbar platform business. Conduit was part of the so-called Download Valley companies monetizing free software and downloads by bundling adware. The toolbars were criticized by some as being very difficult to uninstall. The toolbar software was referred to as a "potentially unwanted program" by some in the computer industry because it could be used to change browser settings. The company had more than 400 employees in 2013. In September same year, Conduit spun off its entire website toolbar business division, which combined with Perion Network. After the deal, Conduit shareholders owned 81% of Perion's existing shares and both Perion and Conduit remained independent companies. The substantial size of the Conduit user base allowed Perion to immediately surpass AOL in U.S. searches. In 2015, Conduit announced it would purchase Keeprz, a mobile customer loyalty platform, for $45 million.

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  • IAmAnas

    IAmAnas

    #IAmAnas (I Am Anas) is a Twitter hashtag and social media campaign that started in 2015. Users tweeted to express support for the undercover investigative works of Ghanaian journalist Anas Aremeyaw Anas. The campaign restarted in 2018 when the Ghanaian MP and financier of the New Patriotic Party, Kennedy Agyapong, announced his intention to reveal the identity of Anas following the journalist's exposé of corruption at the Ghana Football Association. Anas maintains that "being anonymous has always been his secret weapon." Pictures purported to be of Anas were first released by a TV station owned by Agyapong, and were quickly picked up by other media houses. At least one person, a Dutch-Brazilian model, has claimed ownership of one picture that was released, and has threatened legal action against Agyapong for possibly putting his life in danger. In response to Agyapong, social media users retweeted photos of themselves, random people, or even comic images of entities that resemble the trademark covered face of Anas. When the hashtag first began in 2015, along with other popular uses of the journalist's name, Elizabeth Ohene wrote an article about Ghanaians use of humour in response to dealing with the expose of government corruption. "I do not know when these words will make it into Wikipedia or the Oxford English Dictionary but for the moment you can take it from me that: To go undercover is to anas, to make secret recordings is to anas-anas, to wear disguises is to do an anas, to be caught in the act is to be anased. To have someone exposed taking bribes is to have that person being given the full Anas Aremeyaw Anas."

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  • Bookmarklet

    Bookmarklet

    A bookmarklet is a bookmark stored in a web browser that contains JavaScript commands that add new features to the browser. They are stored as the URL of a bookmark in a web browser or as a hyperlink on a web page. Bookmarklets are usually small snippets of JavaScript executed when a user clicks on them. When clicked, bookmarklets can perform a wide variety of operations, such as running a search query from selected text or extracting data from a table. Another name for bookmarklet is favelet or favlet, derived from favorites (synonym of bookmark). == History == Steve Kangas of bookmarklets.com coined the word bookmarklet when he started to create short scripts based on a suggestion in Netscape's JavaScript guide. Before that, Tantek Çelik called these scripts favelets and used that word as early as on 6 September 2001 (personal email). Brendan Eich, who developed JavaScript at Netscape, gave this account of the origin of bookmarklets: They were a deliberate feature in this sense: I invented the javascript: URL along with JavaScript in 1995, and intended that javascript: URLs could be used as any other kind of URL, including being bookmark-able. In particular, I made it possible to generate a new document by loading, e.g. javascript:'hello, world', but also (key for bookmarklets) to run arbitrary script against the DOM of the current document, e.g. javascript:alert(document.links[0].href). The difference is that the latter kind of URL uses an expression that evaluates to the undefined type in JS. I added the void operator to JS before Netscape 2 shipped to make it easy to discard any non-undefined value in a javascript: URL. The increased implementation of Content Security Policy (CSP) in websites has caused problems with bookmarklet execution and usage (2013–2015), with some suggesting that this hails the end or death of bookmarklets. William Donnelly created a work-around solution for this problem (in the specific instance of loading, referencing and using JavaScript library code) in early 2015 using a Greasemonkey userscript (Firefox / Pale Moon browser add-on extension) and a simple bookmarklet-userscript communication protocol. It allows (library-based) bookmarklets to be executed on any and all websites, including those using CSP and having an https:// URI scheme. However, if/when browsers support disabling/disallowing inline script execution using CSP, and if/when websites begin to implement that feature, it will "break" this "fix". == Concept == Web browsers use URIs for the href attribute of the tag and for bookmarks. The URI scheme, such as http or ftp, and which generally specifies the protocol, determines the format of the rest of the string. Browsers also implement javascript: URIs that to a parser is just like any other URI. The browser recognizes the specified javascript scheme and treats the rest of the string as a JavaScript program which is then executed. The expression result, if any, is treated as the HTML source code for a new page displayed in place of the original. The executing script has access to the current page, which it may inspect and change. If the script returns an undefined type (rather than, for example, a string), the browser will not load a new page, with the result that the script simply runs against the current page content. This permits changes such as in-place font size and color changes without a page reload. An immediately invoked function that returns no value or an expression preceded by the void operator will prevent the browser from attempting to parse the result of the evaluation as a snippet of HTML markup: == Usage == Bookmarklets are saved and used as normal bookmarks. As such, they are simple "one-click" tools which add functionality to the browser. For example, they can: Modify the appearance of a web page within the browser (e.g., change font size, background color, etc.) Extract data from a web page (e.g., hyperlinks, images, text, etc.) Remove redirects from (e.g. Google) search results, to show the actual target URL Submit the current page to a blogging service such as Posterous, link-shortening service such as bit.ly, or bookmarking service such as Delicious Query a search engine or online encyclopedia with highlighted text or by a dialog box Submit the current page to a link validation service or translation service Set commonly chosen configuration options when the page itself provides no way to do this Control HTML5 audio and video playback parameters such as speed, position, toggling looping, and showing/hiding playback controls, the first of which can be adjusted beyond HTML5 players' typical range setting. Installing a bookmarklet follows the same process as adding a normal bookmark; the only difference is that in place of the URL destination field is JavaScript code preceded by javascript:. Once created, bookmarklets can be run by clicking on them.

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  • Redshift (theory)

    Redshift (theory)

    Redshift is a techno-economic theory suggesting hypersegmentation of information technology markets based on whether individual computing needs are over or under-served by Moore's law, which predicts the doubling of computing transistors (and therefore roughly computing power) every two years. The theory, proposed and named by New Enterprise Associates partner and former Sun Microsystems CTO Greg Papadopoulos, categorized a series of high growth markets (redshifting) while predicting slower GDP-driven growth in traditional computing markets (blueshifting). Papadopoulos predicted the result will be a fundamental redesign of components comprising computing systems. == Hypergrowth market segments (redshifting) == According to the Redshift theory, applications "redshift" when they grow dramatically faster than Moore's Law allows, growing quickly in their absolute number of systems. In these markets, customers are running out of datacenter real-estate, power and cooling infrastructure. According to Dell Senior Vice President Brad Anderson, “Businesses requiring hyperscale computing environments – where infrastructure deployments are measured by up to millions of servers, storage and networking equipment – are changing the way they approach IT.” While various Redshift proponents offer minor alterations on the original presentation, “Redshifting” generally includes: === ΣBW (Sum-of-Bandwidth) === These are companies that drive heavy Internet traffic. This includes popular web-portals like Google, Yahoo, AOL and MSN. It also includes telecoms, multimedia, television over IP, online games like World of Warcraft and others. This segment has been enabled by widespread availability of high-bandwidth Internet connections to consumers through a DSL or cable modem. A simple way to understand this market is that for every byte of content served to a PC, mobile phone or other device over a network, there must exist computing systems to send it over the network. === High performance computing (HPC) === These are companies that do complex simulations that involve (for example) weather, stock markets or drug-design simulations. This is a generally elastic market because businesses frequently spend every "available" dollar budgeted for IT. A common anecdote claims that cutting the cost of computing by half causes customers in this segment to buy at least twice as much, because each marginal IT dollar spent contributes to business advantage. === prise (or "Star-prise") === These are companies that aggregate traditional computing applications and offer them as services, typically in the form of Software as a Service (SaaS). For example, companies that deploy CRM are over-served by Moore's Law, but companies that aggregate CRM functions and offer them as a service, such as Salesforce.com, grow faster than Moore's Law. === The eBay crisis === A prime example of redshift was a crisis at eBay. In 1999 eBay suffered a database crisis when a single Oracle Database running on the fastest Sun machine available (these tracking Moore's law in this period) was not enough to cope with eBay's growth. The solution was to massively parallelise their system architecture. == Traditional computing markets (blueshifting) == Redshift theory suggests that traditional computing markets, such as those serving enterprise resource planning or customer relationship management applications, have reached relative saturation in industrialized nations. Thereafter, proponents argued further market growth will closely follow gross domestic product growth, which typically remains under 10% for most countries annually. Given that Moore's Law continues to predict accurately the rate of computing transistor growth, which roughly translates into computing power doubling every two years, the Redshift theory suggests that traditional computing markets will ultimately contract as a percentage of computing expenditures over time. Functionally, this means “Blueshifting” customers can satisfy computing requirement growth by swapping in faster processors without increasing the absolute number of computing systems. == Consequences and industry commentary == Papadopoulos argued that while traditional computing markets remain the dominant source of revenue through the late 2000s, a shift to hypergrowth markets will inevitably occur. When that shift occurs, he argued computing (but not computers) will become a utility, and differentiation in the IT market will be based upon a company's ability to deliver computing at massive scale, efficiently and with predictable service levels, much like electricity at that time. If computing is to be delivered as a utility, Nicholas Carr suggested Papadopoulos' vision compares with Microsoft researcher Jim Hamilton, who both agree that computing is most efficiently generated in shipping containers. Industry analysts are also beginning to quantify Redshifting and Blueshifting markets. According to International Data Corporation vice president Matthew Eastwood, "IDC believes that the IT market is in a period of hyper segmentation... This a class of customers that is Moore's law driven and as price performance gains continue, IDC believes that these organizations will accelerate their consumption of IT infrastructure.” == History and nomenclature == Key portions of Papadopoulos' theory were first presented by Sun Microsystems CEO Jonathan Schwartz in late 2006. Papadopoulos later gave a full presentation on Redshift to Sun's annual Analyst Summit in February 2007. The term Redshift refers to what happens when electromagnetic radiation, usually visible light, moves away from an observer. Papadopoulos chose this term to reflect growth markets because redshift helped cosmologists explain the expansion of the universe. Papadopoulos originally depicted traditional IT markets as green to represent their revenue base, but later changed them to “blueshift,” which occurs when a light source moves toward an observer, similar to what would happen during a contraction of the universe.

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  • GitHub Codespaces

    GitHub Codespaces

    GitHub Codespaces is a cloud-based online integrated development environment developed by GitHub. It allows users to create and manage development environments directly within the browser or through Visual Studio Code desktop. Codespaces is tightly integrated with GitHub repositories and enables on-demand coding, debugging, and testing in a full-featured development container hosted in the cloud. == Features == Instant development environments integrated with GitHub Browser-based and desktop access via Visual Studio Code Configurable Dockerfile or devcontainer.json environments Built-in support for GitHub Copilot, extensions, snippets, and SSH. == Licensing == GitHub Codespaces is proprietary software and available to GitHub users under various subscription plans. Codespaces includes a monthly usage quota for free tier users of 120 hours, and expanded access for GitHub education, Pro, Team, and GitHub Enterprise plans. == GitHub Classroom == GitHub Classroom is an educational tool developed by GitHub to streamline the process of managing programming assignments and coursework. Integrated with GitHub repositories, it allows instructors to distribute starter code, automate grading workflows, and track student progress. GitHub Classroom is widely used in computer science education and supports integration with GitHub Codespaces for cloud-based development environments. == Programming languages supported == == Extensions == Some of the popular extensions include:

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  • Radio code

    Radio code

    A radio code is any code that is commonly used over a telecommunication system such as Morse code, brevity codes and procedure words. == Brevity code == Brevity codes are designed to convey complex information with a few words or codes. Specific brevity codes include: ACP-131 Aeronautical Code signals ARRL Numbered Radiogram Multiservice tactical brevity code Ten-code Phillips Code NOTAM Code === Operating signals === Brevity codes that are specifically designed for use between communications operators and to support communication operations are referred to as "operating signals". These include: Prosigns for Morse code 92 Code, Western Union telegraph brevity codes Q code, initially developed for commercial radiotelegraph communication, later adopted by other radio services, especially amateur radio. Used since circa 1909. QN Signals, published by the ARRL and used by Amateur radio operators to assist in the transmission of ARRL Radiograms in the National Traffic System. R and S brevity codes, published by the British Post Office in 1908 for coastal wireless stations and ships, superseded in 1912 by Q codes X code, used by European military services as a wireless telegraphy code in the 1930s and 1940s Z code, also used in the early days of radiotelegraph communication. == Other == Morse code is commonly used in amateur radio. Morse code abbreviations are a type of brevity code. Procedure words used in radiotelephony procedure, are a type of radio code. Spelling alphabets, including the ICAO spelling alphabet, are commonly used in communication over radios and telephones. == Other meanings == Many car audio systems (car radios) have a so-called 'radio code' number which needs to be entered after a power disconnection. This was introduced as a measure to deter theft of these devices. If the code is entered correctly, the radio is activated for use. Entering the code incorrectly several times in a row will cause a temporary or permanent lockout. Some car radios have another check which operates in conjunction with car electronics. If the VIN or another vehicle ID matches the previously stored one, the radio is activated. If the radio cannot verify the vehicle, it is considered to be moved into another vehicle. The radio will then request for the code number or simply refuse to operate and display an error message such as "CANCHECK" or "SECURE".

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  • Deconfliction line

    Deconfliction line

    A deconfliction line is an official line of communications established between militaries who are or could be hostile, to avoid dangerous misunderstandings and miscalculations based on ignorance. The ultimate aim is to avoid accidents and conflict escalation. In the 2010s and 2020s, the US and Russia set up deconfliction lines during the Syrian civil war and Russo-Ukrainian War. They were regularly tested by military staff, and used by air traffic controllers and senior military officers. They were used to avoid midair collisions between aircraft in the same or adjacent airspace, and sometimes to give warning of airstrikes. In April 2017, Russia severed the Syrian line in retaliation for a called strike.

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  • Quality of experience

    Quality of experience

    Quality of experience (QoE) is a measure of the delight or annoyance of a customer's experiences with a service (e.g., web browsing, phone call, TV broadcast). QoE focuses on the entire service experience; it is a holistic concept, similar to the field of user experience, but with its roots in telecommunication. QoE is an emerging multidisciplinary field based on social psychology, cognitive science, economics, and engineering science, focused on understanding overall human quality requirements. == Definition and concepts == In 2013, within the context of the COST Action QUALINET, QoE has been defined as:The degree of delight or annoyance of the user of an application or service. It results from the fulfillment of his or her expectations with respect to the utility and / or enjoyment of the application or service in the light of the user’s personality and current state.This definition has been adopted in 2016 by the International Telecommunication Union in Recommendation ITU-T P.10/G.100. Before, various definitions of QoE had existed in the domain, with the above-mentioned definition now finding wide acceptance in the community. QoE has historically emerged from Quality of Service (QoS), which attempts to objectively measure service parameters (such as packet loss rates or average throughput). QoS measurement is most of the time not related to a customer, but to the media or network itself. QoE however is a purely subjective measure from the user's perspective of the overall quality of the service provided, by capturing people's aesthetic and hedonic needs. QoE looks at a vendor's or purveyor's offering from the standpoint of the customer or end user, and asks, "What mix of goods, services, and support, do you think will provide you with the perception that the total product is providing you with the experience you desired and/or expected?" It then asks, "Is this what the vendor/purveyor has actually provided?" If not, "What changes need to be made to enhance your total experience?" In short, QoE provides an assessment of human expectations, feelings, perceptions, cognition and satisfaction with respect to a particular product, service or application. QoE is a blueprint of all human subjective and objective quality needs and experiences arising from the interaction of a person with technology and with business entities in a particular context. Although QoE is perceived as subjective, it is an important measure that counts for customers of a service. Being able to measure it in a controlled manner helps operators understand what may be wrong with their services and how to improve them. == QoE factors == QoE aims at taking into consideration every factor that contributes to a user's perceived quality of a system or service. This includes system, human and contextual factors. The following so-called "influence factors" have been identified and classified by Reiter et al.: Human Influence Factors Low-level processing (visual and auditory acuity, gender, age, mood, …) Higher-level processing (cognitive processes, socio-cultural and economic background, expectations, needs and goals, other personality traits…) System Influence Factors Content-related Media-related (encoding, resolution, sample rate, …) Network-related (bandwidth, delay, jitter, …) Device-related (screen resolution, display size, …) Context Influence Factors Physical context (location and space) Temporal context (time of day, frequency of use, …) Social context (inter-personal relations during experience) Economic context Task context (multitasking, interruptions, task type) Technical and information context (relationship between systems) Studies in the field of QoE have typically focused on system factors, primarily due to its origin in the QoS and network engineering domains. Through the use of dedicated test laboratories, the context is often sought to be kept constant. == QoE versus User Experience == QoE is strongly related to but different from the field of User Experience (UX), which also focuses on users' experiences with services. Historically, QoE has emerged from telecommunication research, while UX has its roots in Human–Computer Interaction. Both fields can be considered multi-disciplinary. In contrast to UX, the goal of improving QoE for users was more strongly motivated by economic needs. Wechsung and De Moor identify the following key differences between the fields: == QoE measurement == As a measure of the end-to-end performance at the service level from the user's perspective, QoE is an important metric for the design of systems and engineering processes. This is particularly relevant for video services because – due to their high traffic demands –, bad network performance may highly affect the user's experience. So, when designing systems, the expected output, i.e. the expected QoE, is often taken into account – also as a system output metric and optimization goal. To measure this level of QoE, human ratings can be used. The mean opinion score (MOS) is a widely used measure for assessing the quality of media signals. It is a limited form of QoE measurement, relating to a specific media type, in a controlled environment and without explicitly taking into account user expectations. The MOS as an indicator of experienced quality has been used for audio and speech communication, as well as for the assessment of quality of Internet video, television and other multimedia signals, and web browsing. Due to inherent limitations in measuring QoE in a single scalar value, the usefulness of the MOS is often debated. Subjective quality evaluation requires a lot of human resources, establishing it as a time-consuming process. Objective evaluation methods can provide quality results faster, but require dedicated computing resources. Since such instrumental video quality algorithms are often developed based on a limited set of subjective data, their QoE prediction accuracy may be low when compared to human ratings. QoE metrics are often measured at the end devices and can conceptually be seen as the remaining quality after the distortion introduced during the preparation of the content and the delivery through the network, until it reaches the decoder at the end device. There are several elements in the media preparation and delivery chain, and some of them may introduce distortion. This causes degradation of the content, and several elements in this chain can be considered as "QoE-relevant" for the offered services. The causes of degradation are applicable for any multimedia service, that is, not exclusive to video or speech. Typical degradations occur at the encoding system (compression degradation), transport network, access network (e.g., packet loss or packet delay), home network (e.g. WiFi performance) and end device (e.g. decoding performance). == QoE management == Several QoE-centric network management and bandwidth management solutions have been proposed, which aim to improve the QoE delivered to the end-users. When managing a network, QoE fairness may be taken into account in order to keep the users sufficiently satisfied (i.e., high QoE) in a fair manner. From a QoE perspective, network resources and multimedia services should be managed in order to guarantee specific QoE levels instead of classical QoS parameters, which are unable to reflect the actual delivered QoE. A pure QoE-centric management is challenged by the nature of the Internet itself, as the Internet protocols and architecture were not originally designed to support today's complex and high demanding multimedia services. As an example for an implementation of QoE management, network nodes can become QoE-aware by estimating the status of the multimedia service as perceived by the end-users. This information can then be used to improve the delivery of the multimedia service over the network and proactively improve the users' QoE. This can be achieved, for example, via traffic shaping. QoE management gives the service provider and network operator the capability to minimize storage and network resources by allocating only the resources that are sufficient to maintain a specific level of user satisfaction. As it may involve limiting resources for some users or services in order to increase the overall network performance and QoE, the practice of QoE management requires that net neutrality regulations are considered.

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  • SlideRocket

    SlideRocket

    SlideRocket was an online presentation platform that let users create, manage, share and measure presentations. SlideRocket was provided via a SaaS model. The company was acquired by VMware in April 2011, who sold it to ClearSlide, a similar SaaS application, in March 2013. It is no longer offering independent signups, as the platform is being integrated into ClearSlide. == History == SlideRocket was founded in Jan 2006, and launched as a private beta in March 2008 at the Under The Radar Spring event. A public beta was announced in September 2008 followed shortly by public release on October 28, 2008. SlideRocket is most commonly credited with inventing the PResuMÉ or Presentation Résumé in early 2009. On April 26, 2011, SlideRocket was acquired by VMware. On March 5, 2013, VMware sold SlideRocket to ClearSlide. SlideRocket is based in San Francisco.

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  • Pridgen v University of Calgary

    Pridgen v University of Calgary

    Pridgen v University of Calgary was freedom of speech case which took place in Alberta, Canada, in 2010. The case deals with two university students, Keith and Steven Pridgen, who were found guilty and punished by the University of Calgary in 2008, on grounds of "non-academic misconduct". The University of Calgary defines "non-academic misconduct" as:(a) conduct which causes injury to a person and/or damage to University property and/or the property of any member of the University community; (b) unauthorized removal and/or unauthorized possession of University property; and (c) conduct which seriously disrupts the lawful educational and related activities of other students and/or University staff.The Court of the Queen's Bench of Alberta found the University of Calgary to be wrong in prosecuting ten students, including the Pridgen brothers, in regards to comments made about a professor on Facebook. The key ruling in this case was that the universities are not exempt from, and that these students were in fact protected under, section 2(b) of the Charter of Rights and Freedoms. This case is notable as it highlights the jurisdiction of the Charter in terms of both new media technologies and university institutions in Canada. == Background == Keith and Steven Pridgen were undergraduate students at the University of Calgary in 2008. The twin brothers shared a Law and Society class being taught by Aruna Mitra. Professor Mitra was teaching this class for the first time in her career, and many of the students were very critical of her knowledge of the course. A Facebook page entitled “I NO Longer Fear Hell, I Took a Course with Aruna Mitra” was created, and many students began posting comments. In particular, Steven Pridgen's comment on November 13, 2007, read: “Somehow I think she just got lazy and gave everybody a 65....that's what I got. Does anybody know how to apply to have it remarked?” Many students had similar concerns to Pridgen's and after having their work re-marked, a number of them did in fact receive higher grades. Keith Pridgen also commented on August 26, 2008: “Hey fellow LWSO. Homees.. So I am quite sure Mitra is NO LONGER TEACHING ANY COURSES WITH THE U OF C !!!!! Remember when she told us she was a long-term professor? Well, Actually she was only sessional and picked up our class at the last moment because another prof wasn't able to do it ...lucky us. Well, anyways I think we should all congratulate ourselves for leaving a Mitra-free legacy for future students!” On September 4, 2008, Aruna Mitra complained about the Facebook page to the Interim Dean of the Faculty of Communication and Culture at the University of Calgary. Dean Tettey called a meeting for the ten students who posted material about Mitra on the Facebook page. The meeting took place on September 18, 2008, and included four professors from the department as well as the Dean. At this meeting, all ten students, including the Pridgen brothers, were found guilty of non-academic misconduct. On November 20, 2008, the Appellant's received a letter from Dean Tettey advising them that their comments “clearly caused unwarranted professional and personal injury to Prof. Mitra and clearly meets the criteria for non-academic misconduct as outlined in the University of Calgary Calendar”. Keith Pridgen was put on probation for 24 months, and both brothers were required to write a letter of apology to Prof. Mitra and refrain from posting or circulating defamatory material regarding any faculty members of the University of Calgary. The Pridgen brothers appealed the decision to the University of Calgary Review Committee and later to the Board of Governors of the University of Calgary however neither of these attempts succeeded in having the decision overturned. == Opinion of the Court == Eight main issues to be determined were laid out by the Honourable Madam Justice J. Strekaf: (a) Does the Charter apply to the disciplinary proceedings taken by the Respondent; (b) If, so were the Applicants' Charter rights infringed; (c) Were the actions taken by the University ultra vires the jurisdiction of the Province of Alberta; (d) Did the Board of Governors err in refusing to hear the Applicants appeals; (e) Were the Applicants' denied a fair hearing; (f) Did the Review Committee provide adequate reasons for its decisions; (g) Did the Review Committee err in concluding that the activities of the Applicants constituted non-academic misconduct; and (h) What, if any, remedy should be granted to the Applicants. The Court determined from previous cases that "a non-government entity may still be subject to the Charter of Rights and freedoms when implementing a specific government policy or program". Justice Strekaf distinguished that the University was acting as agent of the provincial government in providing accessible post-secondary education services to students in Alberta pursuant to the provisions of the PSL Act. Justice Strekaf felt there was sufficient evidence to show that universities in Alberta have some level of reliance on government funds and therefore they are not a "Charter free zone". Justice Strekaf concluded that comments made by Keith and Steven Pridgen, regarding Professor Mitra, on Facebook did not constitute academic misconduct and the Pridgen brothers' right to freedom of expression, under section 2(b) of the Charter, was infringed by the University of Calgary Review Committee.

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  • Temporal resolution

    Temporal resolution

    Temporal resolution (TR) refers to the discrete resolution of a measurement with respect to time. It is defined as the amount of time needed to revisit and acquire data for the same location. When applied to remote sensing, this amount of time is influenced by the sensor platform's orbital characteristics and the features of the sensor itself. The temporal resolution is low when the revisiting delay is high and vice versa. Temporal resolution is typically expressed in days. == Physics == Often there is a trade-off between the temporal resolution of a measurement and its spatial resolution, due to Heisenberg's uncertainty principle. In some contexts, such as particle physics, this trade-off can be attributed to the finite speed of light and the fact that it takes a certain period of time for the photons carrying information to reach the observer. In this time, the system might have undergone changes itself. Thus, the longer the light has to travel, the lower the temporal resolution. == Technology == === Computing === In another context, there is often a tradeoff between temporal resolution and computer storage. A transducer may be able to record data every millisecond, but available storage may not allow this, and in the case of 4D PET imaging the resolution may be limited to several minutes. === Electronic displays === In some applications, temporal resolution may instead be equated to the sampling period, or its inverse, the refresh rate, or update frequency in Hertz, of a TV, for example. The temporal resolution is distinct from temporal uncertainty. This would be analogous to conflating image resolution with optical resolution. One is discrete, the other, continuous. The temporal resolution is a resolution somewhat the 'time' dual to the 'space' resolution of an image. In a similar way, the sample rate is equivalent to the pixel pitch on a display screen, whereas the optical resolution of a display screen is equivalent to temporal uncertainty. Note that both this form of image space and time resolutions are orthogonal to measurement resolution, even though space and time are also orthogonal to each other. Both an image or an oscilloscope capture can have a signal-to-noise ratio, since both also have measurement resolution. === Oscilloscopy === An oscilloscope is the temporal equivalent of a microscope, and it is limited by temporal uncertainty the same way a microscope is limited by optical resolution. A digital sampling oscilloscope has also a limitation analogous to image resolution, which is the sample rate. A non-digital non-sampling oscilloscope is still limited by temporal uncertainty. The temporal uncertainty can be related to the maximum frequency of continuous signal the oscilloscope could respond to, called the bandwidth and given in Hertz. But for oscilloscopes, this figure is not the temporal resolution. To reduce confusion, oscilloscope manufacturers use 'Sa/s' instead of 'Hz' to specify the temporal resolution. Two cases for oscilloscopes exist: either the probe settling time is much shorter than the real time sampling rate, or it is much larger. The case where the settling time is the same as the sampling time is usually undesirable in an oscilloscope. It is more typical to prefer a larger ratio either way, or if not, to be somewhat longer than two sample periods. In the case where it is much longer, the most typical case, it dominates the temporal resolution. The shape of the response during the settling time also has as strong effect on the temporal resolution. For this reason probe leads usually offer an arrangement to 'compensate' the leads to alter the trade off between minimal settling time, and minimal overshoot. If it is much shorter, the oscilloscope may be prone to aliasing from radio frequency interference, but this can be removed by repeatedly sampling a repetitive signal and averaging the results together. If the relationship between the 'trigger' time and the sample clock can be controlled with greater accuracy than the sampling time, then it is possible to make a measurement of a repetitive waveform with much higher temporal resolution than the sample period by upsampling each record before averaging. In this case the temporal uncertainty may be limited by clock jitter.

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  • Digital citizen

    Digital citizen

    The term digital citizen is used with different meanings. According to the definition provided by Karen Mossberger, one of the authors of Digital Citizenship: The Internet, Society, and Participation, digital citizens are "those who use the internet regularly and effectively". In this sense, a digital citizen is a person who uses information technology (IT) to engage in society, politics, and government. More recent elaborations of the concept define digital citizenship as the self-enactment of people’s role in society through the use of digital technologies, stressing the empowering and democratizing characteristics of the citizenship idea. These theories aim at taking into account the ever-increasing datafication of contemporary societies (symbolically linked to the Snowden leaks), which has called into question the meaning of “being (digital) citizens in a datafied society”. This condition is also referred to as the “algorithmic society”, characterised by the increasing datafication of social life and the pervasive presence of surveillance practices – see surveillance and surveillance capitalism, the use of artificial intelligence, and Big Data. Datafication presents crucial challenges for the very notion of citizenship, so that data collection can no longer be seen as an issue of privacy alone so that:We cannot simply assume that being a citizen online already means something (whether it is the ability to participate or the ability to stay safe) and then look for those whose conduct conforms to this meaning Instead, the idea of digital citizenship shall reflect the idea that we are no longer mere “users” of technologies since they shape our agency both as individuals and as citizens. Digital citizenship refers to the responsible and respectful use of technology to engage online, evaluate information, and protect human rights. It encompasses skills for communication, collaboration, empathy, privacy protection, and security to prevent data breaches and identity theft. == Digital citizenship in the "algorithmic society" == In the context of the algorithmic society, the question of digital citizenship "becomes one of the extents to which subjects are able to challenge, avoid or mediate their data double in this datafied society”. These reflections put the emphasis on the idea of the digital space (or cyberspace) as a political space where the respect of fundamental rights of the individual shall be granted (with reference both to the traditional ones as well as to new specific rights of the internet [see “digital constitutionalism”]) and where the agency and the identity of the individuals as citizens is at stake. This idea of digital citizenship is thought to be not only active but also performative, in the sense that “in societies that are increasingly mediated through digital technologies, digital acts become important means through which citizens create, enact and perform their role in society.” In particular, for Isin and Ruppert this points towards an active meaning of (digital) citizenship based on the idea that we constitute ourselves as digital citizen by claiming rights on the internet, either by saying or by doing something. == Types of digital participation == People who characterize themselves as digital citizens often use IT extensively—creating blogs, using social networks, and participating in online journalism. Although digital citizenship begins when any child, teen, or adult signs up for an email address, posts pictures online, uses e-commerce to buy merchandise online, and/or participates in any electronic function that is B2B or B2C, the process of becoming a digital citizen goes beyond simple internet activity. According to Thomas Humphrey Marshall, a British sociologist known for his work on social citizenship, a primary framework of citizenship comprises three different traditions: liberalism, republicanism, and ascriptive hierarchy. Within this framework, the digital citizen needs to exist in order to promote equal economic opportunities and increase political participation. In this way, digital technology helps to lower the barriers to entry for participation as a citizen within a society. They also have a comprehensive understanding of digital citizenship, which is the appropriate and responsible behavior when using technology. Since digital citizenship evaluates the quality of an individual's response to membership in a digital community, it often requires the participation of all community members, both visible and those who are less visible. A large part in being a responsible digital citizen encompasses digital literacy, etiquette, online safety, and an acknowledgement of private versus public information. The development of digital citizen participation can be divided into two main stages. The first stage is through information dissemination, which includes subcategories of its own: static information dissemination, characterized largely by citizens who use read-only websites where they take control of data from credible sources in order to formulate judgments or facts. Many of these websites where credible information may be found are provided by the government. dynamic information dissemination, which is more interactive and involves citizens as well as public servants. Both questions and answers can be communicated, and citizens have the opportunity to engage in question-and-answer dialogues through two-way communication platforms The second stage of digital citizen participation is citizen deliberation, which evaluates what type of participation and role that they play when attempting to ignite some sort of policy change. static citizen participants can play a role by engaging in online polls as well as through complaints and recommendations sent up, mainly toward the government who can create changes in policy decisions. dynamic citizen participants can deliberate amongst others on their thoughts and recommendations in town hall meetings or various media sites. One potential advantage of online participation through digital citizenship is increased social inclusion. In a report on civic engagement, citizen-powered democracy can be initiated either through information shared through the web, direct communication signals made by the state toward the public, and social media tactics from both private and public companies. In fact, it was found that the community-based nature of social media platforms allow individuals to feel more socially included and informed about political issues that peers have also been found to engage with, otherwise known as a "second-order effect." Understanding strategic marketing on social media would further explain social media customers’ participation. Two types of opportunities rise as a result, the first being the ability to lower barriers that can make exchanges much easier. In addition, they have the chance to participate in transformative disruption, giving people who have a historically lower political engagement to mobilize in a much easier and convenient fashion. Nonetheless, there are several challenges that face the presence of digital technologies in political participation. Both current as well as potential challenges can create significant risks for democratic processes. Not only is digital technology still seen as relatively ambiguous, it was also seen to have "less inclusivity in democratic life." Demographic groups differ considerably in the use of technology, and thus, one group could potentially be more represented than another as a result of digital participation. Another primary challenge consists in the ideology of a "filter bubble" effect. Alongside a tremendous spread of false information, internet users could reinforce existing prejudices and assist in polarizing disagreements in the public sphere. This can lead to misinformed voting and decisions based on exposure rather than on pure knowledge. A communication technology director, Van Dijk, stated, "Computerized information campaigns and mass public information systems have to be designed and supported in such a way that they help to narrow the gap between the 'information rich' and 'information poor' otherwise the spontaneous development of ICT will widen it." Access and equivalent amounts of knowledge behind digital technology must be equivalent in order for a fair system to put into place. Alongside a lack of evidenced support for technology that can be proven to be safe for citizens, the OECD has identified five struggles for the online engagement of citizens: Scale: To what extent can a society allow every individual's voice to be heard, but also not be lost in the mass debate? This can be extremely challenging for the government, which may not effectively know how to listen and respond to each individual contribution. Capacity: How can digital technology offer citizens more information on public policy-making? The opportunity for citizens to debate with one another is lacking for acti

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  • Automated machine learning

    Automated machine learning

    Automated machine learning (AutoML) is the process of automating the tasks of applying machine learning to real-world problems. It is the combination of automation and ML. AutoML potentially includes every stage from beginning with a raw dataset to building a machine learning model ready for deployment. AutoML was proposed as an artificial intelligence-based solution to the growing challenge of applying machine learning. The high degree of automation in AutoML aims to allow non-experts to make use of machine learning models and techniques without requiring them to become experts in machine learning. Automating the process of applying machine learning end-to-end additionally offers the advantages of producing simpler solutions, faster creation of those solutions, and models that often outperform hand-designed models. Common techniques used in AutoML include hyperparameter optimization, meta-learning and neural architecture search. == Comparison to the standard approach == In a typical machine learning application, practitioners have a set of input data points to be used for training. The raw data may not be in a form that all algorithms can be applied to. To make the data amenable for machine learning, an expert may have to apply appropriate data pre-processing, feature engineering, feature extraction, and feature selection methods. After these steps, practitioners must then perform algorithm selection and hyperparameter optimization to maximize the predictive performance of their model. If deep learning is used, the architecture of the neural network must also be chosen manually by the machine learning expert. Each of these steps may be challenging, resulting in significant hurdles to using machine learning. AutoML aims to simplify these steps for non-experts, and to make it easier for them to use machine learning techniques correctly and effectively. AutoML plays an important role within the broader approach of automating data science, which also includes challenging tasks such as data engineering, data exploration and model interpretation and prediction. == Targets of automation == Automated machine learning can target various stages of the machine learning process. Steps to automate are: Data preparation and ingestion (from raw data and miscellaneous formats) Column type detection; e.g., Boolean, discrete numerical, continuous numerical, or text Column intent detection; e.g., target/label, stratification field, numerical feature, categorical text feature, or free text feature Task detection; e.g., binary classification, regression, clustering, or ranking Feature engineering Feature selection Feature extraction Meta-learning and transfer learning Detection and handling of skewed data and/or missing values Model selection - choosing which machine learning algorithm to use, often including multiple competing software implementations Ensembling - a form of consensus where using multiple models often gives better results than any single model Hyperparameter optimization of the learning algorithm and featurization Neural architecture search Pipeline selection under time, memory, and complexity constraints Selection of evaluation metrics and validation procedures Problem checking Leakage detection Misconfiguration detection Analysis of obtained results Creating user interfaces and visualizations == Challenges and Limitations == There are a number of key challenges being tackled around automated machine learning. A big issue surrounding the field is referred to as "development as a cottage industry". This phrase refers to the issue in machine learning where development relies on manual decisions and biases of experts. This is contrasted to the goal of machine learning which is to create systems that can learn and improve from their own usage and analysis of the data. Basically, it's the struggle between how much experts should get involved in the learning of the systems versus how much freedom they should be giving the machines. However, experts and developers must help create and guide these machines to prepare them for their own learning. To create this system, it requires labor intensive work with knowledge of machine learning algorithms and system design. Additionally, other challenges include meta-learning and computational resource allocation.

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  • European Information Technology Observatory

    European Information Technology Observatory

    The European Information Technology Observatory (EITO) gathers information on European and global markets for information technology, telecommunications and consumer electronics. The EITO is managed by Bitkom Research GmbH, a wholly owned subsidiary of BITKOM, the German Association for Information Technology, Telecommunications and New Media. EITO is sponsored by Deutsche Telekom, KPMG and Telecom Italia. The research activities of the EITO Task Force are supported by the European Commission and the OECD. The EITO exists thanks to an initiative of Enore Deotto from MIlan and the support of Luis-Alberto Petit Herrera (Madrid), Jörg Schomburg (Hanover) and Günther Möller (Frankfurt). Between 1993 and 2007, the market reports were published as printed annual reports ("EITO yearbook"). Since 2008 the market reports are available in electronic version and can be purchased on the EITO online portal. Currently, the ICT market reports are divided in following categories: International Reports International Reports include ICT market information of all EITO countries and all market segments or only specific segments. The newest ICT Market Report 2013/14, published in October 2013, includes market data of 36 countries: 28 European markets, BRIC countries, Japan, Turkey and the US as well as a deep analysis of ICT market developments in 9 European countries. The detailed market data and forecasts are available for the period 2010–2014. Country Reports This category includes EITO reports on a single country's ICT market. The Country ICT Market Reports are published biannually for France, Germany, Italy, Spain and the United Kingdom. Thematic Reports Thematic studies focusing on a specific topic. Customized Reports Market Reports made upon order.

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  • Pridgen v University of Calgary

    Pridgen v University of Calgary

    Pridgen v University of Calgary was freedom of speech case which took place in Alberta, Canada, in 2010. The case deals with two university students, Keith and Steven Pridgen, who were found guilty and punished by the University of Calgary in 2008, on grounds of "non-academic misconduct". The University of Calgary defines "non-academic misconduct" as:(a) conduct which causes injury to a person and/or damage to University property and/or the property of any member of the University community; (b) unauthorized removal and/or unauthorized possession of University property; and (c) conduct which seriously disrupts the lawful educational and related activities of other students and/or University staff.The Court of the Queen's Bench of Alberta found the University of Calgary to be wrong in prosecuting ten students, including the Pridgen brothers, in regards to comments made about a professor on Facebook. The key ruling in this case was that the universities are not exempt from, and that these students were in fact protected under, section 2(b) of the Charter of Rights and Freedoms. This case is notable as it highlights the jurisdiction of the Charter in terms of both new media technologies and university institutions in Canada. == Background == Keith and Steven Pridgen were undergraduate students at the University of Calgary in 2008. The twin brothers shared a Law and Society class being taught by Aruna Mitra. Professor Mitra was teaching this class for the first time in her career, and many of the students were very critical of her knowledge of the course. A Facebook page entitled “I NO Longer Fear Hell, I Took a Course with Aruna Mitra” was created, and many students began posting comments. In particular, Steven Pridgen's comment on November 13, 2007, read: “Somehow I think she just got lazy and gave everybody a 65....that's what I got. Does anybody know how to apply to have it remarked?” Many students had similar concerns to Pridgen's and after having their work re-marked, a number of them did in fact receive higher grades. Keith Pridgen also commented on August 26, 2008: “Hey fellow LWSO. Homees.. So I am quite sure Mitra is NO LONGER TEACHING ANY COURSES WITH THE U OF C !!!!! Remember when she told us she was a long-term professor? Well, Actually she was only sessional and picked up our class at the last moment because another prof wasn't able to do it ...lucky us. Well, anyways I think we should all congratulate ourselves for leaving a Mitra-free legacy for future students!” On September 4, 2008, Aruna Mitra complained about the Facebook page to the Interim Dean of the Faculty of Communication and Culture at the University of Calgary. Dean Tettey called a meeting for the ten students who posted material about Mitra on the Facebook page. The meeting took place on September 18, 2008, and included four professors from the department as well as the Dean. At this meeting, all ten students, including the Pridgen brothers, were found guilty of non-academic misconduct. On November 20, 2008, the Appellant's received a letter from Dean Tettey advising them that their comments “clearly caused unwarranted professional and personal injury to Prof. Mitra and clearly meets the criteria for non-academic misconduct as outlined in the University of Calgary Calendar”. Keith Pridgen was put on probation for 24 months, and both brothers were required to write a letter of apology to Prof. Mitra and refrain from posting or circulating defamatory material regarding any faculty members of the University of Calgary. The Pridgen brothers appealed the decision to the University of Calgary Review Committee and later to the Board of Governors of the University of Calgary however neither of these attempts succeeded in having the decision overturned. == Opinion of the Court == Eight main issues to be determined were laid out by the Honourable Madam Justice J. Strekaf: (a) Does the Charter apply to the disciplinary proceedings taken by the Respondent; (b) If, so were the Applicants' Charter rights infringed; (c) Were the actions taken by the University ultra vires the jurisdiction of the Province of Alberta; (d) Did the Board of Governors err in refusing to hear the Applicants appeals; (e) Were the Applicants' denied a fair hearing; (f) Did the Review Committee provide adequate reasons for its decisions; (g) Did the Review Committee err in concluding that the activities of the Applicants constituted non-academic misconduct; and (h) What, if any, remedy should be granted to the Applicants. The Court determined from previous cases that "a non-government entity may still be subject to the Charter of Rights and freedoms when implementing a specific government policy or program". Justice Strekaf distinguished that the University was acting as agent of the provincial government in providing accessible post-secondary education services to students in Alberta pursuant to the provisions of the PSL Act. Justice Strekaf felt there was sufficient evidence to show that universities in Alberta have some level of reliance on government funds and therefore they are not a "Charter free zone". Justice Strekaf concluded that comments made by Keith and Steven Pridgen, regarding Professor Mitra, on Facebook did not constitute academic misconduct and the Pridgen brothers' right to freedom of expression, under section 2(b) of the Charter, was infringed by the University of Calgary Review Committee.

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