AI Detector Free Online

AI Detector Free Online — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

Dark mode

A dark mode, dark theme, night mode, or light-on-dark color scheme is a color scheme that uses light-colored text, icons, and graphical user interface elements on a dark background. It is often discussed in terms of computer user interface design and web design. Many modern websites and operating systems offer the user an optional light-on-dark display mode. Some users find dark mode displays more visually appealing, and claim that it can reduce eye strain. Displaying white at full brightness uses roughly six times as much power as pure black on a 2016 Google Pixel, which has an OLED display. However, conventional LED displays may not benefit from reduced power consumption; but if a LED display has the partial dimming features, it still benefits from reduced power consumption. Most modern operating systems support an optional light-on-dark color scheme. == History == Microsoft introduced the high contrast themes in Windows 95. Later, Microsoft introduced a dark theme in the Anniversary Update of Windows 10 in 2016. In 2018, Apple followed in macOS Mojave. In September 2019, iOS 13 and Android 10 both introduced dark modes. Some operating systems provide tools to change the dark mode state automatically at sundown or sunrise. A "prefers-color-scheme" option was created for front-end web developers in 2019, being a CSS property that signals a user's choice for their system to use a light or dark color theme. Firefox and Chromium have optional dark theme for all internal screens. It is also possible for third-party developers to implement their own dark themes. There are also a variety of browser add-ons that can re-theme web sites with dark color schemes, also aligning with system theme. Wikipedia's mobile and desktop versions received a dark mode option in 2024. == Implementation == There is a prefers-color-scheme media query in CSS, to detect if the user has requested light or dark color scheme and serve the requested color scheme. It can be indicated from the user's operating system preference or a user agent. CSS example: JavaScript example: == Energy usage == Light on dark color schemes require less energy to display on OLED displays. This positively impacts battery life and reduces energy consumption. While an OLED will consume around 40% of the power of an LCD displaying an image that is primarily black, it can use more than three times as much power to display an image with a white background, such as a document or web site. This can lead to reduced battery life and higher energy usage unless a light-on-dark color scheme is used. The long-term reduced power usage may also prolong battery life or the useful life of the display and battery. The energy savings that can be achieved using a light-on-dark color scheme are because of how OLED screens work: in an OLED screen, each subpixel generates its own light and it only consumes power when generating light. This is in contrast to how an LCD works: in an LCD, subpixels either block or allow light from an always-on (lit) LED backlight to pass through. "AMOLED Black" color schemes (that use pure black instead of dark gray) do not necessarily save more energy than other light-on-dark color schemes that use dark gray instead of black, as the power consumption on an AMOLED screen decreases proportionately to the average brightness of the displayed pixels. Although it is true that AMOLED black does save more energy than dark gray, the additional energy savings are often negligible; AMOLED black will only give an additional energy saving of less than 1%, for instance, over the dark gray that's used in the dark theme for Google's official Android apps. In November 2018, Google confirmed that dark mode on Android saved battery life. == Web issues == Some argue that a color scheme with light text on a dark background is easier to read on the screen, because the lower overall brightness causes less eyestrain, while others argue to the contrary. Some pages on the web are designed for white backgrounds; Image assets (GIF, PNG, SVG, WOFF, etc) can be used improperly causing visual artifacts if dark mode is forced (instead of designed for) with a plugin like Dark Reader.
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Social collaboration

Social collaboration refers to processes that help multiple people or groups interact and share information to achieve common goals. Such processes find their 'natural' environment on the Internet, where collaboration and social dissemination of information are made easier by current innovations and the proliferation of the web. Sharing concepts on a digital collaboration environment often facilitates a "brainstorming" process, where new ideas may emerge due to the varied contributions of individuals. These individuals may hail from different walks of life, different cultures and different age groups, their diverse thought processes help in adding new dimensions to ideas, dimensions that previously may have been missed. A crucial concept behind social collaboration is that 'ideas are everywhere.' Individuals are able to share their ideas in an unrestricted environment as anyone can get involved and the discussion is not limited to only those who have domain knowledge. Social collaboration is also known as enterprise social networking, and the products to support it are often branded enterprise social networks (ESNs). It is important that we understand the rhythm of social collaboration. There needs to be a balance, with ease to move from focused solitary work to brainstorming for problem solving in group work. This critical balance can be achieved by creating structures or a work environment where it is not too rigid to prevent brainstorming in group work nor too loose to result in total chaos. Social collaboration should happen at the edge of chaos. Work practices should support social collaboration. The most effective environment is one that supports opportunistic planning. Opportunistic planning provides a general plan but then gives enough room for flexibility to change activities and tasks until the last moment. This way, people are able to cope up with unforeseen developments and not throwing away everything with one grand plan. == Comparison to social networking == Social collaboration is related to social networking, with the distinction that while social networking is individual-centric, social collaboration is entirely group-centric. Generally speaking, social networking means socializing for personal, professional or entertainment purposes, for example, LinkedIn and Facebook. Social collaboration, on the other hand, means working socially to achieve a common goal, for example, GitHub and Quora. Social networking services generally focus on individuals sharing messages in a more-or-less undirected way and receiving messages from many sources into a single personalized activity feed. Social collaboration services, on the other hand, focus on the identification of groups and collaboration spaces in which messages are explicitly directed at the group and the group activity feed is seen the same way by everyone. Social collaboration may refer to time-bound collaborations with an explicit goal to be completed or perpetual collaborations in which the goal is knowledge sharing (e.g. community of practice, online community). == Comparison to crowdsourcing == Social collaboration is similar to crowdsourcing as it involves individuals working together towards a common goal. Crowdsourcing is a method for harnessing specific information from a large, diverse group of people. Unlike social collaboration, which involves much communication and cooperation among a large group of people, crowdsourcing is more like individuals working towards the common goal relatively independently. Therefore, the process of working involves less communication. Andrea Grover, curator of a crowdsourcing art show, explained that collaboration among individuals is an appealing experience, because participation is "a low investment, with the possibility of a high return." == Social collaboration software == Notable social collaboration software includes Glip messaging, Google Apps, Knowledge Plaza Electronic Document System and Social Intranet, Microsoft Lync social collaboration tool for businesses, Slack, Weekdone for managers, and Wrike. == Future == Social collaboration is going to be used as a tool in companies to enhance productivity. Social workers could be able to use social collaboration tools to manage personal tasks, professional projects and social networks with other colleagues within the same organization. Social collaboration will serve as a platform to get people involved and connected. This kind of platform provides a spiritual training practice for social workers. Social collaboration software could help enhance the communication between customers and employees and build trust in the organization. When we need real-time chat, it would be excellent to include every participant in a shared and archived forum which keeps a record of important information and logs. So collaborators need not worry about losing important records while working towards the common goal. The interactive communication and synchronous environment promote understanding among colleagues. Collaboration helps in building strong relationships between workers, which in turn leads to faster problem solving. The close connection between workers and customers creates a scalable organization which naturally increases the trust and faith that customers have in the company. Therefore, the interactive customer relationship levels up customer satisfaction in ways that traditional collaboration methods cannot. Apart from its effect on the way work will be conducted in the future, social collaboration will also affect society. In the coming years social collaboration will be the driving force in societal change as more and more people work together to get their vision across to governments and governing agencies. An example of this is Change.org, an online petition tool where users can help bring their government's attention to pressing social issues that need to be addressed.
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G.9970

G.9970 (also known as G.hnta) is a Recommendation developed by ITU-T that describes the generic transport architecture for home networks and their interfaces to a provider's access network. G.9970 was developed by Study Group 15, Question 1. G.9970 received Consent on December 12, 2008 and was Approved on January 13, 2009. == Relationship with G.hn == G.9970 (G.hnta) and G.9960 (G.hn) are two ITU-T Recommendations that address home networking in a complementary manner. While G.9970 addresses layer 3 (network layer) of the home network architecture, G.9960 addresses layers 1 (physical layer) and 2 (data link layer).
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Secret London

Secret London is a Facebook group started by 21-year-old Bristol University graduate, Tiffany Philippou, on 19 January 2010 in response to a Saatchi & Saatchi competition. The group grew rapidly (180,000 members as of 8 February 2010) and is composed mostly of Londoners who use the site to share suggestions and photos of London. After the group's early success, the founder announced her intention to launch a website of the same name by crowdsourcing the design and development. The website was launched on 16 February 2010. == Other secret cities == Following the initial success of Secret London, a number of other secret groups were independently started around the world, some of which already have over 100,000 users. As of 19 February 2010, the list of other groups includes: Secret Frankfurt, Secret Tel Aviv, Secret Paris, Secret New York, Secret Tokyo, Secret Toronto, Secret Los Angeles, Secret Exeter, Secret Boston, Secret Norwich, Secret Singapore, Secret Brighton, Secret Minneapolis, Secret Sydney, Secret Canberra, Secret Brisbane, Secret Wellington, Secret Christchurch, Secret Madeira, Secret Funchal, Secret Bristol and Secret Cardiff. == Controversy == Some commentators have questioned whether it possible to share secrets without compromising them, and whether sharing tips publicly will lead to over-exposure of the businesses who are recommended.
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Pose (computer vision)

In the fields of computing and computer vision, pose (or spatial pose) represents the position and the orientation of an object, each usually in three dimensions. Poses are often stored internally as transformation matrices. The term “pose” is largely synonymous with the term “transform”, but a transform may often include scale, whereas pose does not. In computer vision, the pose of an object is often estimated from camera input by the process of pose estimation. This information can then be used, for example, to allow a robot to manipulate an object or to avoid moving into the object based on its perceived position and orientation in the environment. Other applications include skeletal action recognition. == Pose estimation == The specific task of determining the pose of an object in an image (or stereo images, image sequence) is referred to as pose estimation. Pose estimation problems can be solved in different ways depending on the image sensor configuration, and choice of methodology. Three classes of methodologies can be distinguished: Analytic or geometric methods: Given that the image sensor (camera) is calibrated and the mapping from 3D points in the scene and 2D points in the image is known. If also the geometry of the object is known, it means that the projected image of the object on the camera image is a well-known function of the object's pose. Once a set of control points on the object, typically corners or other feature points, has been identified, it is then possible to solve the pose transformation from a set of equations which relate the 3D coordinates of the points with their 2D image coordinates. Algorithms that determine the pose of a point cloud with respect to another point cloud are known as point set registration algorithms, if the correspondences between points are not already known. Genetic algorithm methods: If the pose of an object does not have to be computed in real-time a genetic algorithm may be used. This approach is robust especially when the images are not perfectly calibrated. In this particular case, the pose represent the genetic representation and the error between the projection of the object control points with the image is the fitness function. Learning-based methods: These methods use artificial learning-based system which learn the mapping from 2D image features to pose transformation. In short, this means that a sufficiently large set of images of the object, in different poses, must be presented to the system during a learning phase. Once the learning phase is completed, the system should be able to present an estimate of the object's pose given an image of the object. == Camera pose ==
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Social advertising (social relationships)

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

Azuqua is an American cloud-based integration and automation company headquartered in Seattle, Washington. As such, they integrate SaaS applications and create automations that are designed to eliminate manual work. Azuqua's platform has the ability to set up workflows between multiple applications so disparate teams can stay in the loop. Azuqua's customers include companies such as Charles Schwab, General Electric, General Motors, HubSpot, and Airbnb. == History == Nikhil Hasija and Craig Unger founded Azuqua in 2011. In 2013, the team participated in Techstars Microsoft's Windows Azure Accelerator, a Seattle-based incubator that helps entrepreneurs gain traction through deep mentor engagement and rapid iteration cycles. Azuqua announced in 2014 that they have received their Series A funding from Ignition Partners which amounted to $5 million. 2017 included a 65% growth in new customers, a doubling of new SaaS connectors, and a 50% growth in overall employee headcount. Azuqua also received their Series B funding which totaled to $10.8 million. This funding was led by Insight Ventures Partners, with DFJ and Ignition Partners also joining the round In March 2018, Azuqua hired Todd Owens as CEO. Owens was previously CEO of Appuri, a customer data platform. Hasija has transitioned to the role of Chief Product Officer. Azuqua also hired on Dan Kogan who has taken on the role of Chief Marketing Officer. Kogan previously worked at Tableau, a BI and analytics company, as a Senior Director of Product Marketing. Okta acquired Azuqua in 2019. == Product Description/Features == Logic Library: Logic functions that can be used for data processing, branching logic, and business rules Drag and Drop Visual Designer: No-code visual designer Use of API's for each cloud service a business is using to allow the various apps to communicate and share data API Publishing: Integrations and automations can be made available as secure endpoints, webhooks, or open services Connector Builder: Build a connector to an application Connector Library: Pre-built connectors to SaaS applications Error Handling: Automations that execute when an error is detected
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Key-agreement protocol

In cryptography, a key-agreement protocol is a protocol whereby two (or more) parties generate a cryptographic key as a function of information provided by each honest party so that no party can predetermine the resulting value. In particular, all honest participants influence the outcome. A key-agreement protocol is a specialisation of a key-exchange protocol. At the completion of the protocol, all parties share the same key. A key-agreement protocol precludes undesired third parties from forcing a key choice on the agreeing parties. A secure key agreement can ensure confidentiality and data integrity in communications systems, ranging from simple messaging applications to complex banking transactions. Secure agreement is defined relative to a security model, for example the Universal Model. More generally, when evaluating protocols, it is important to state security goals and the security model. For example, it may be required for the session key to be authenticated. A protocol can be evaluated for success only in the context of its goals and attack model. An example of an adversarial model is the Dolev–Yao model. In many key exchange systems, one party generates the key, and sends that key to the other party; the other party has no influence on the key. == Exponential key exchange == The first publicly known public-key agreement protocol that meets the above criteria was the Diffie–Hellman key exchange, in which two parties jointly exponentiate a generator with random numbers, in such a way that an eavesdropper cannot feasibly determine what the resultant shared key is. Exponential key agreement in and of itself does not specify any prior agreement or subsequent authentication between the participants. It has thus been described as an anonymous key agreement protocol. == Symmetric key agreement == Symmetric key agreement (SKA) is a method of key agreement that uses solely symmetric cryptography and cryptographic hash functions as cryptographic primitives. It is related to symmetric authenticated key exchange. SKA may assume the use of initial shared secrets or a trusted third party with whom the agreeing parties share a secret is assumed. If no third party is present, then achieving SKA can be trivial: we tautologically assume that two parties that share an initial secret and have achieved SKA. SKA contrasts with key-agreement protocols that include techniques from asymmetric cryptography, such as key encapsulation mechanisms. The initial exchange of a shared key must be done in a manner that is private and integrity-assured. Historically, this was achieved by physical means, such as by using a trusted courier. An example of a SKA protocol is the Needham–Schroeder protocol. It establishes a session key between two parties on the same network, using a server as a trusted third party. The original Needham–Schroeder protocol is vulnerable to a replay attack. Timestamps and nonces are included to fix this attack. It forms the basis for the Kerberos protocol. === Types of key agreement === Boyd et al. classify two-party key agreement protocols according to two criteria as follows: whether a pre-shared key already exists or not the method of generating the session key. The pre-shared key may be shared between the two parties, or each party may share a key with a trusted third party. If there is no secure channel (as may be established via a pre-shared key), it is impossible to create an authenticated session key. The session key may be generated via: key transport, key agreement and hybrid. If there is no trusted third party, then the cases of key transport and hybrid session key generation are indistinguishable. SKA is concerned with protocols in which the session key is established using only symmetric primitives. == Authentication == Anonymous key exchange, like Diffie–Hellman, does not provide authentication of the parties, and is thus vulnerable to man-in-the-middle attacks. A wide variety of cryptographic authentication schemes and protocols have been developed to provide authenticated key agreement to prevent man-in-the-middle and related attacks. These methods generally mathematically bind the agreed key to other agreed-upon data, such as the following: public–private key pairs shared secret keys passwords === Public keys === A widely used mechanism for defeating such attacks is the use of digitally signed keys that must be integrity-assured: if Bob's key is signed by a trusted third party vouching for his identity, Alice can have considerable confidence that a signed key she receives is not an attempt to intercept by Eve. When Alice and Bob have a public-key infrastructure, they may digitally sign an agreed Diffie–Hellman key, or exchanged Diffie–Hellman public keys. Such signed keys, sometimes signed by a certificate authority, are one of the primary mechanisms used for secure web traffic (including HTTPS, SSL or TLS protocols). Other specific examples are MQV, YAK and the ISAKMP component of the IPsec protocol suite for securing Internet Protocol communications. However, these systems require care in endorsing the match between identity information and public keys by certificate authorities in order to work properly. === Hybrid systems === Hybrid systems use public-key cryptography to exchange secret keys, which are then used in a symmetric-key cryptography systems. Most practical applications of cryptography use a combination of cryptographic functions to implement an overall system that provides all of the four desirable features of secure communications (confidentiality, integrity, authentication, and non-repudiation). === Passwords === Password-authenticated key agreement protocols require the separate establishment of a password (which may be smaller than a key) in a manner that is both private and integrity-assured. These are designed to resist man-in-the-middle and other active attacks on the password and the established keys. For example, DH-EKE, SPEKE, and SRP are password-authenticated variations of Diffie–Hellman. === Other tricks === If one has an integrity-assured way to verify a shared key over a public channel, one may engage in a Diffie–Hellman key exchange to derive a short-term shared key, and then subsequently authenticate that the keys match. One way is to use a voice-authenticated read-out of the key, as in PGPfone. Voice authentication, however, presumes that it is infeasible for a man-in-the-middle to spoof one participant's voice to the other in real-time, which may be an undesirable assumption. Such protocols may be designed to work with even a small public value, such as a password. Variations on this theme have been proposed for Bluetooth pairing protocols. In an attempt to avoid using any additional out-of-band authentication factors, Davies and Price proposed the use of the interlock protocol of Ron Rivest and Adi Shamir, which has been subject to both attack and subsequent refinement.
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Report generator

A report generator is a computer program whose purpose is to take data from a source such as a database, XML stream or a spreadsheet, and use it to produce a document in a format which satisfies a particular human readership. Report generation functionality is almost always present in database systems, where the source of the data is the database itself. It can also be argued that report generation is part of the purpose of a spreadsheet. Standalone report generators may work with multiple data sources and export reports to different document formats. Information systems theory specifies that information delivered to a target human reader must be timely, accurate and relevant. Report generation software targets the final requirement by making sure that the information delivered is presented in the way most readily understood by the target reader. == History == An early report writer was part of NOMAD developed in the 1970s. The evolution of reporting software has a rich history dating back to the mid-20th century, driven by the increasing need for businesses to efficiently analyze and present data. Initially, manual extraction and tabulation were commonplace, but the advent of computers in the 1960s marked a transformative phase with the emergence of basic reporting tools. The 1980s saw the widespread adoption of database management systems, laying the groundwork for more sophisticated reporting capabilities. Notable dedicated reporting software, such as Crystal Reports and BusinessObjects, gained prominence in the 1990s amidst the growing demand for business intelligence. The 21st century witnessed a paradigm shift towards web-based reporting solutions and the rise of self-service BI tools, empowering users to create reports independently. Presently, reporting software continues to evolve with a focus on data visualization, integration of artificial intelligence, and the imperative for real-time analytics in decision-making.
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Data governance

Data governance is a term used on both a macro and a micro level. The former is a political concept and forms part of international relations and Internet governance; the latter is a data management concept and forms part of corporate/organizational data governance. Data governance involves delegating authority over data and exercising that authority through decision-making processes. It plays a role in enhancing the value of data assets. == Macro level == Data governance at the macro level involves regulating cross-border data flows among countries, which is more precisely termed international data governance. This field was first formed in the early 2000s, and consists of "norms, principles and rules governing various types of data." There have been several international groups established by research organizations that aim to grant access to their data. These groups that enable an exchange of data are, as a result, exposed to domestic and international legal interpretations that ultimately decide how data is used. However, as of 2023, there are no international laws or agreements specifically focused on data protection. == Data governance (Data Management) == Data governance is the set of principles, policies, and processes that guide the effective and responsible use of data within an organization. It creates a framework for decision making, accountability, and oversight across the data lifecycle, from creation and storage to sharing and disposal. Data governance is closely linked with data management, which provides the practical methods to carry out governance objectives. These methods include data quality assurance, metadata management, master data management, security controls, and compliance monitoring. Together, governance and management aim to maximize the value of data as a strategic asset, reduce risks from misuse or inaccuracy, and ensure compliance with regulatory, ethical, and business requirements. The importance of this discipline has grown with the rise of big data, cloud computing, and artificial intelligence, where consistent standards and stewardship are essential for privacy protection, interoperability, and informed decision making. == Data governance drivers == While data governance initiatives can be driven by a desire to improve data quality, they are often driven by C-level leaders responding to external regulations. In a recent report conducted by the CIO WaterCooler community, 54% stated the key driver was efficiencies in processes; 39% - regulatory requirements; and only 7% customer service. Examples of these regulations include Sarbanes–Oxley Act, Basel I, Basel II, HIPAA, GDPR, cGMP, and a number of data privacy regulations. To achieve compliance with these regulations, business processes and controls require formal management processes to govern the data subject to these regulations. Successful programs identify drivers that are meaningful to both supervisory and executive leadership. Common themes among the external regulations center on the need to manage risk. The risks can be financial misstatement, inadvertent release of sensitive data, or poor data quality for key decisions. Methods to manage these risks vary from industry to industry. Examples of commonly referenced best practices and guidelines include COBIT, ISO/IEC 38500, and others. The proliferation of regulations and standards creates challenges for data governance professionals, particularly when multiple regulations overlap the data being managed. Organizations often launch data governance initiatives to address these challenges. == Data governance initiatives (Dimensions) == Data governance initiatives improve the quality of data by assigning a team responsible for data's accuracy, completeness, consistency, timeliness, validity, and uniqueness. This team usually consists of executive leadership, project management, line-of-business managers, and data stewards. The team usually employs a methodology for tracking and improving enterprise data, such as Six Sigma, and tools for data mapping, profiling, cleansing, and monitoring data. Data governance initiatives may be aimed at achieving a number of objectives including offering better visibility to internal and external customers (such as supply chain management), compliance with regulatory law, improving operations after rapid company growth or corporate mergers, or to aid the efficiency of enterprise knowledge workers by reducing confusion and error and increasing their scope of knowledge. Many data governance initiatives are also inspired by past attempts to fix information quality at the departmental level, which can lead to incongruent and redundant data quality processes. Most large companies have many applications and databases that can not easily share information. Therefore, knowledge workers within large organizations may not have access to the data they need to best do their jobs. When they do have access to the data, the data quality may be poor. By setting up a data governance practice or corporate data authority (individual or area responsible for determining how to proceed, in the best interest of the business, when a data issue arises), these problems can be mitigated. == Implementation == Implementation of a data governance initiative may vary in scope as well as origin. Sometimes, an executive mandate will arise to initiate an enterprise-wide effort. Sometimes the mandate will be to create a pilot project or projects, limited in scope and objectives, aimed at either resolving existing issues or demonstrating value. Sometimes, an initiative originates from lower down in the organization's hierarchy and will be deployed in a limited scope to demonstrate value to potential sponsors higher up in the organization. The initial scope of an implementation can vary greatly as well, from review of a one-off IT system to a cross-organization initiative. == Data governance tools == Leaders of successful data governance programs declared at the Data Governance Conference in Orlando, FL, in December 2006, that data governance is about 80 to 95 percent communication. That stated, it is a given that many of the objectives of a data governance program must be accomplished with appropriate tools. Many vendors are now positioning their products as data governance tools. Due to the different focus areas of various data governance initiatives, a given tool may or may not be appropriate. Additionally, many tools that are not marketed as governance tools address governance needs and demands.
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Cypherpunks (book)

Cypherpunks: Freedom and the Future of the Internet is a 2012 book by Julian Assange, in discussion with Internet activists and cypherpunks Jacob Appelbaum, Andy Müller-Maguhn and Jérémie Zimmermann. Its primary topic is society's relationship with information security. In the book, the authors warn that the Internet has become a tool of the police state, and that the world is inadvertently heading toward a form of totalitarianism. They promote the use of cryptography to protect against state surveillance. In the introduction, Assange says that the book is "not a manifesto [...] [but] a warning". He told Guardian journalist Decca Aitkenhead: A well-defined mathematical algorithm can encrypt something quickly, but to decrypt it would take billions of years – or trillions of dollars' worth of electricity to drive the computer. So cryptography is the essential building block of independence for organisations on the Internet, just like armies are the essential building blocks of states, because otherwise one state just takes over another. There is no other way for our intellectual life to gain proper independence from the security guards of the world, the people who control physical reality. Assange later wrote in The Guardian: "Strong cryptography is a vital tool in fighting state oppression." saying that was the message of his book, Cypherpunks. Cypherpunks is published by OR Books. It is primarily a transcript of World Tomorrow episode eight, a two-part interview between Assange, Jacob Appelbaum, Andy Müller-Maguhn, and Jérémie Zimmermann. In the foreword, Assange said, "the Internet, our greatest tool for emancipation, has been transformed into the most dangerous facilitator of totalitarianism we have ever seen".
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Memory-hard function

In cryptography, a memory-hard function (MHF) is a function that costs a significant amount of memory to efficiently evaluate. It differs from a memory-bound function, which incurs cost by slowing down computation through memory latency. MHFs have found use in key stretching and proof of work as their increased memory requirements significantly reduce the computational efficiency advantage of custom hardware over general-purpose hardware compared to non-MHFs. == Introduction == MHFs are designed to consume large amounts of memory on a computer in order to reduce the effectiveness of parallel computing. In order to evaluate the function using less memory, a significant time penalty is incurred. As each MHF computation requires a large amount of memory, the number of function computations that can occur simultaneously is limited by the amount of available memory. This reduces the efficiency of specialised hardware, such as application-specific integrated circuits and graphics processing units, which utilise parallelisation, in computing a MHF for a large number of inputs, such as when brute-forcing password hashes or mining cryptocurrency. == Motivation and examples == Bitcoin's proof-of-work uses repeated evaluation of the SHA-256 function, but modern general-purpose processors, such as off-the-shelf CPUs, are inefficient when computing a fixed function many times over. Specialized hardware, such as application-specific integrated circuits (ASICs) designed for Bitcoin mining, can use 30,000 times less energy per hash than x86 CPUs whilst having much greater hash rates. This led to concerns about the centralization of mining for Bitcoin and other cryptocurrencies. Because of this inequality between miners using ASICs and miners using CPUs or off-the shelf hardware, designers of later proof-of-work systems utilised hash functions for which it was difficult to construct ASICs that could evaluate the hash function significantly faster than a CPU. As memory cost is platform-independent, MHFs have found use in cryptocurrency mining, such as for Litecoin, which uses scrypt as its hash function. They are also useful in password hashing because they significantly increase the cost of trying many possible passwords against a leaked database of hashed passwords without significantly increasing the computation time for legitimate users. == Measuring memory hardness == There are various ways to measure the memory hardness of a function. One commonly seen measure is cumulative memory complexity (CMC). In a parallel model, CMC is the sum of the memory required to compute a function over every time step of the computation. Other viable measures include integrating memory usage against time and measuring memory bandwidth consumption on a memory bus. Functions requiring high memory bandwidth are sometimes referred to as "bandwidth-hard functions". == Variants == MHFs can be categorized into two different groups based on their evaluation patterns: data-dependent memory-hard functions (dMHF) and data-independent memory-hard functions (iMHF). As opposed to iMHFs, the memory access pattern of a dMHF depends on the function input, such as the password provided to a key derivation function. Examples of dMHFs are scrypt and Argon2d, while examples of iMHFs are Argon2i and catena. Many of these MHFs have been designed to be used as password hashing functions because of their memory hardness. A notable problem with dMHFs is that they are prone to side-channel attacks such as cache timing. This has resulted in a preference for using iMHFs when hashing passwords. However, iMHFs have been mathematically proven to have weaker memory hardness properties than dMHFs.
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ReactiveX

ReactiveX (Rx, also known as Reactive Extensions) is a software library originally created by Microsoft that allows imperative programming languages to operate on sequences of data regardless of whether the data is synchronous or asynchronous. It provides a set of sequence operators that operate on each item in the sequence. It is an implementation of reactive programming and provides a blueprint for the tools to be implemented in multiple programming languages. == Overview == ReactiveX is an API for asynchronous programming with observable streams. Asynchronous programming allows programmers to call functions and then have the functions "callback" when they are done, usually by giving the function the address of another function to execute when it is done. Programs designed in this way often avoid the overhead of having many threads constantly starting and stopping. Observable streams (i.e. streams that can be observed) in the context of Reactive Extensions are like event emitters that emit three events: next, error, and complete. An observable emits next events until it either emits an error event or a complete event. However, at that point it will not emit any more events, unless it is subscribed to again. The examples below use the RxJS implementation of Reactive Extensions for the JavaScript programming language. === Motivation === For sequences of data, it combines the advantages of iterators with the flexibility of event-based asynchronous programming. It also works as a simple promise, eliminating the pyramid of doom that results from multiple layers of callbacks. === Observables and observers === ReactiveX is a combination of ideas from the observer and the iterator patterns and from functional programming. An observer subscribes to an observable sequence. The sequence then sends the items to the observer one at a time, usually by calling the provided callback function. The observer handles each one before processing the next one. If many events come in asynchronously, they must be stored in a queue or dropped. In ReactiveX, an observer will never be called with an item out of order or (in a multi-threaded context) called before the callback has returned for the previous item. Asynchronous calls remain asynchronous and may be handled by returning an observable. It is similar to the iterators pattern in that if a fatal error occurs, it notifies the observer separately (by calling a second function). When all the items have been sent, it completes (and notifies the observer by calling a third function). The Reactive Extensions API also borrows many of its operators from iterator operators in other programming languages. Reactive Extensions is different from functional reactive programming as the Introduction to Reactive Extensions explains: It is sometimes called "functional reactive programming" but this is a misnomer. ReactiveX may be functional, and it may be reactive, but "functional reactive programming" is a different animal. One main point of difference is that functional reactive programming operates on values that change continuously over time, while ReactiveX operates on discrete values that are emitted over time. (See Conal Elliott's work for more-precise information on functional reactive programming.) === Reactive operators === An operator is a function that takes one observable (the source) as its first argument and returns another observable (the destination, or outer observable). Then for every item that the source observable emits, it will apply a function to that item, and then emit it on the destination Observable. It can even emit another Observable on the destination observable. This is called an inner observable. An operator that emits inner observables can be followed by another operator that in some way combines the items emitted by all the inner observables and emits the item on its outer observable. Examples include: switchAll – subscribes to each new inner observable as soon as it is emitted and unsubscribes from the previous one. mergeAll – subscribes to all inner observables as they are emitted and outputs their values in whatever order it receives them. concatAll – subscribes to each inner observable in order and waits for it to complete before subscribing to the next observable. Operators can be chained together to create complex data flows that filter events based on certain criteria. Multiple operators can be applied to the same observable. Some of the operators that can be used in Reactive Extensions may be familiar to programmers who use functional programming language, such as map, reduce, group, and zip. There are many other operators available in Reactive Extensions, though the operators available in a particular implementation for a programming language may vary. ==== Reactive operator examples ==== Here is an example of using the map and reduce operators. We create an observable from a list of numbers. The map operator will then multiply each number by two and return an observable. The reduce operator will then sum up all the numbers provided to it (the value of 0 is the starting point). Calling subscribe will register an observer that will observe the values from the observable produced by the chain of operators. With the subscribe method, we are able to pass in an error-handling function, called whenever an error is emitted in the observable, and a completion function when the observable has finished emitting items. ==== Usage in stream-oriented programming ==== Certain RxJS primitives such as BehaviorSubject make it possible to create pure stateful streams to track application state of arbitrary complexity in simple terms. The button below will feed an event to the stream, which in turn will re-emit the next natural number every time, back into the tag that follows and displays the count of clicks detected. Libraries such as Rimmel.js, designed around RxJS Observables, enable integration between reactive streams and the HTML DOM: == History == Reactive Extensions was created by the Cloud Programmability Team at Microsoft around 2011, as a byproduct of a larger effort called Volta. It was originally intended to provide an abstraction for events across different tiers in an application to support tier splitting in Volta. The project's logo represents an electric eel, which is a reference to Volta. The extensions suffix in the name is a reference to the Parallel Extensions technology which was invented around the same time; the two are considered complementary. The initial implementation of Rx was for .NET Framework and was released on June 21, 2011. Later, the team started the implementation of Rx for other platforms, including JavaScript and C++. The technology was released as open source in late 2012, initially on CodePlex. Later, the code moved to GitHub and has been ported to several other languages, including Go, Java, Kotlin, PHP and Rust.
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Computer network engineering

Computer network engineering is a technology discipline within engineering that deals with the design, implementation, and management of computer networks. These systems contain both physical components, such as routers, switches, cables, and some logical elements, such as protocols and network services. Computer network engineers attempt to ensure that the data is transmitted efficiently, securely, and reliably over both local area networks (LANs) and wide area networks (WANs), as well as across the Internet. Computer networks often play a large role in modern industries ranging from telecommunications to cloud computing, enabling processes such as email and file sharing, as well as complex real-time services like video conferencing and online gaming. == Background == The evolution of network engineering is marked by significant milestones that have greatly impacted communication methods. These milestones particularly highlight the progress made in developing communication protocols that are vital to contemporary networking. This discipline originated in the 1960s with projects like ARPANET, which initiated important advancements in reliable data transmission. The advent of protocols such as TCP/IP revolutionized networking by enabling interoperability among various systems, which, in turn, fueled the rapid growth of the Internet. Key developments include the standardization of protocols and the shift towards increasingly complex layered architectures. These advancements have profoundly changed the way devices interact across global networks. == Network infrastructure design == The foundation of computer network engineering lies in the design of the network infrastructure. This involves planning both the physical layout of the network and its logical topology to ensure optimal data flow, reliability, and scalability. === Physical infrastructure === The physical infrastructure consists of the hardware used to transmit data, which is represented by the first layer of the OSI model. ==== Cabling ==== Copper cables such as ethernet over twisted pair are commonly used for short-distance connections, especially in local area networks (LANs), while fiber optic cables are favored for long-distance communication due to their high-speed transmission capabilities and lower susceptibility to interference. Fiber optics play a significant role in the backbone of large-scale networks, such as those used in data centers and internet service provider (ISP) infrastructures. ==== Wireless networks ==== In addition to wired connections, wireless networks have become a common component of physical infrastructure. These networks facilitate communication between devices without the need for physical cables, providing flexibility and mobility. Wireless technologies use a range of transmission methods, including radio frequency (RF) waves, infrared signals, and laser-based communication, allowing devices to connect to the network. Wi-Fi based on IEEE 802.11 standards is the most widely used wireless technology in local area networks and relies on RF waves to transmit data between devices and access points. Wireless networks operate across various frequency bands, including 2.4 GHz and 5 GHz, each offering unique ranges and data rates; the 2.4 GHz band provides broader coverage, while the 5 GHz band supports faster data rates with reduced interference, ideal for densely populated environments. Beyond Wi-Fi, other wireless transmission methods, such as infrared and laser-based communication, are used in specific contexts, like short-range, line-of-sight links or secure point-to-point communication. In mobile networks, cellular technologies like 3G, 4G, and 5G enable wide-area wireless connectivity. 3G introduced faster data rates for mobile browsing, while 4G significantly improved speed and capacity, supporting advanced applications like video streaming. The latest evolution, 5G, operates across a range of frequencies, including millimeter-wave bands, and provides high data rates, low latency, and support for more device connectivity, useful for applications like the Internet of Things (IoT) and autonomous systems. Together, these wireless technologies allow networks to meet a variety of connectivity needs across local and wide areas. ==== Network devices ==== Routers and switches help direct data traffic and assist in maintaining network security; network engineers configure these devices to optimize traffic flow and prevent network congestion. In wireless networks, wireless access points (WAP) allow devices to connect to the network. To expand coverage, multiple access points can be placed to create a wireless infrastructure. Beyond Wi-Fi, cellular network components like base stations and repeaters support connectivity in wide-area networks, while network controllers and firewalls manage traffic and enforce security policies. Together, these devices enable a secure, flexible, and scalable network architecture suitable for both local and wide-area coverage. === Logical topology === Beyond the physical infrastructure, a network must be organized logically, which defines how data is routed between devices. Various topologies, such as star, mesh, and hierarchical designs, are employed depending on the network’s requirements. In a star topology, for example, all devices are connected to a central hub that directs traffic. This configuration is relatively easy to manage and troubleshoot but can create a single point of failure. In contrast, a mesh topology, where each device is interconnected with several others, offers high redundancy and reliability but requires a more complex design and larger hardware investment. Large networks, especially those in enterprises, often employ a hierarchical model, dividing the network into core, distribution, and access layers to enhance scalability and performance. == Network protocols and communication standards == Communication protocols dictate how data in a network is transmitted, routed, and delivered. Depending on the goals of the specific network, protocols are selected to ensure that the network functions efficiently and securely. The Transmission Control Protocol/Internet Protocol (TCP/IP) suite is fundamental to modern computer networks, including the Internet. It defines how data is divided into packets, addressed, routed, and reassembled. The Internet Protocol (IP) is critical for routing packets between different networks. In addition to traditional protocols, advanced protocols such as Multiprotocol Label Switching (MPLS) and Segment Routing (SR) enhance traffic management and routing efficiency. For intra-domain routing, protocols like Open Shortest Path First (OSPF) and Enhanced Interior Gateway Routing Protocol (EIGRP) provide dynamic routing capabilities. On the local area network (LAN) level, protocols like Virtual Extensible LAN (VXLAN) and Network Virtualization using Generic Routing Encapsulation (NVGRE) facilitate the creation of virtual networks. Furthermore, Internet Protocol Security (IPsec) and Transport Layer Security (TLS) secure communication channels, ensuring data integrity and confidentiality. For real-time applications, protocols such as Real-time Transport Protocol (RTP) and WebRTC provide low-latency communication, making them suitable for video conferencing and streaming services. Additionally, protocols like QUIC enhance web performance and security by establishing secure connections with reduced latency. == Network security == As networks have become essential for business operations and personal communication, the demand for robust security measures has increased. Network security is a critical component of computer network engineering, concentrating on the protection of networks against unauthorized access, data breaches, and various cyber threats. Engineers are responsible for designing and implementing security measures that ensure the integrity and confidentiality of data transmitted across networks. Firewalls serve as barriers between trusted internal networks and external environments, such as the Internet. Network engineers configure firewalls, including next-generation firewalls (NGFW), which incorporate advanced features such as deep packet inspection and application awareness, thereby enabling more refined control over network traffic and protection against sophisticated attacks. In addition to firewalls, engineers use encryption protocols, including Internet Protocol Security (IPsec) and Transport Layer Security (TLS), to secure data in transit. These protocols provide a means of safeguarding sensitive information from interception and tampering. For secure remote access, Virtual Private Networks (VPNs) are deployed, using technologies to create encrypted tunnels for data transmission over public networks. These VPNs are often used for maintaining security when remote users access corporate networks but are also used ion other settings. To enhance threat detection and r
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Why We Post

Why We Post is a research project funded by the European Research Council and launched in 2012 by Daniel Miller with the objective of examining the global impact of new social media. The study is based on ethnographic data collected through the course of 15 months in China, India, Turkey, Italy, United Kingdom, Trinidad, Chile and Brazil. The results of this project were released on 29 February 2016. This included the first three of eleven Open Access books (available via UCL Press), a five-week e-course (MOOC) on FutureLearn in English, also available in Chinese, Portuguese, Hindi, Tamil, Italian, Turkish, and Spanish on UCLeXtend. In addition a website containing key discoveries, stories and over 100 films is available in the same 8 languages.
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