AI For Students Writing

AI For Students Writing — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

• 

  1.58-bit large language model

  A 1.58-bit large language model (also known as a ternary LLM) is a type of large language model (LLM) designed to be computationally efficient. It achieves this by using weights that are restricted to only three values: -1, 0, and +1. This restriction significantly reduces the model's memory footprint and allows for faster processing, as computationally expensive multiplication operations can be replaced with lower-cost additions. This contrasts with traditional models that use 16-bit floating-point numbers (FP16 or BF16) for their weights. Studies have shown that for models up to several billion parameters, the performance of 1.58-bit LLMs on various tasks is comparable to their full-precision counterparts. This approach could enable powerful AI to run on less specialized and lower-power hardware. The name "1.58-bit" comes from the fact that a system with three states contains log 2 ⁡ 3 ≈ 1.58 {\displaystyle \log _{2}3\approx 1.58} bits of information. These models are sometimes also referred to as 1-bit LLMs in research papers, although this term can also refer to true binary models (with weights of -1 and +1). == BitNet == In 2024, Ma et al., researchers at Microsoft, declared that their 1.58-bit model, BitNet b1.58 is comparable in performance to the 16-bit Llama 2 and opens the era of 1-bit LLM. BitNet creators did not use the post-training quantization of weights but instead relied on the new BitLinear transform that replaced the nn.Linear layer of the traditional transformer design. In 2025, Microsoft researchers had released an open-weights and open inference code model BitNet b1.58 2B4T demonstrating performance competitive with the full precision models at 2B parameters and 4T training tokens. == Post-training quantization == BitNet derives its performance from being trained natively in 1.58 bit instead of being quantized from a full-precision model after training. Still, training is an expensive process and it would be desirable to be able to somehow convert an existing model to 1.58 bits. In 2024, HuggingFace reported a way to gradually ramp up the 1.58-bit quantization in fine-tuning an existing model down to 1.58 bits. == Critique == Some researchers point out that the scaling laws of large language models favor the low-bit weights only in case of undertrained models. As the number of training tokens increases, the deficiencies of low-bit quantization surface.

  Read more →
• 

  Social media use in hiring

  Social media use in hiring refers to the examination by employers of job applicants' (public) social media profiles as part of the hiring assessment. For example, the vast majority of Fortune 500 companies use social media as a tool to screen prospective employees and as a tool for talent acquisition. This practice raises ethical questions. Employers and recruiters note that they have access only to information that applicants choose to make public. Many Western-European countries restrict employer's use of social media in the workplace. States including Arkansas, California, Colorado, Illinois, Maryland, Michigan, Nevada, New Jersey, New Mexico, Utah, Washington, and Wisconsin protect applicants and employees from surrendering usernames and passwords for social media accounts. Use of social media has caused significant problems for some applicants who are active on social media. A 2013 survey of 17,000 young people in six countries found that one in ten people aged 16 to 34 claimed to have been rejected for a job because of social media activity. Social media services have been reported to affect deception in resumes. While these services do not affect deception frequency, it does increase deception about interests and hobbies. == Ethical implications == This issue raises many ethical questions that some consider an employer's right and others consider discrimination. As of 2016, except in the states of California, Maryland, and Illinois, there are no laws that prohibit employers from using social media profiles as a basis of whether or not someone should be hired. Title VII also prohibits discrimination during any aspect of employment including hiring or firing, recruitment, or testing. Social media has been integrating into the workplace, and this has led to conflicts within employees and employers.[107] Particularly, Facebook has been seen as a popular platform for employers to investigate in order to learn more about potential employees. This conflict first started in Maryland when an employer requested and received an employee's Facebook username and password. State lawmakers first introduced legislation in 2012 to prohibit employers from requesting passwords to personal social accounts in order to get a job or to keep a job. This led to Canada, Germany, the U.S. Congress and 11 U.S. states to pass or propose legislation that prevents employers' access to private social accounts of employees.[108] Many Western European countries have already implemented laws that restrict the regulation of social media in the workplace. States including Arkansas, California, Colorado, Illinois, Maryland, Michigan, Nevada, New Jersey, New Mexico, Utah, Washington, and Wisconsin have passed legislation that protects potential employees and current employees from employers that demand them to give forth their username or password for a social media account. Laws that forbid employers from disciplining an employee based on activity off the job on social media sites have also been put into act in states including California, Colorado, Connecticut, North Dakota, and New York. Several states have similar laws that protect students in colleges and universities from having to grant access to their social media accounts. Eight states have passed the law that prohibits post secondary institutions from demanding social media login information from any prospective or current students and privacy legislation has been introduced or is pending in at least 36 states as of July 2013. As of May 2014, legislation has been introduced and is in the process of pending in at least 28 states and has been enacted in Maine and Wisconsin. In addition, the National Labor Relations Board has been devoting a lot of their attention to attacking employer policies regarding social media that can discipline employees who seek to speak and post freely on social media sites. Use of social media by young people has caused significant problems for some applicants who are active on social media when they try to enter the job market. A survey of 17,000 young people in six countries in 2013 found that 1 in 10 people aged 16 to 34 have been rejected for a job because of online comments they made on social media websites. A 2014 survey of recruiters found that 93% of them check candidates' social media postings. Moreover, professor Stijn Baert of Ghent University conducted a field experiment in which fictitious job candidates applied for real job vacancies in Belgium. They were identical except in one respect: their Facebook profile photos. It was found that candidates with the most wholesome photos were a lot more likely to receive invitations for job interviews than those with the more controversial photos. In addition, Facebook profile photos had a greater impact on hiring decisions when candidates were highly educated. These cases have created some privacy implications as to whether or not companies should have the right to look at employee's Facebook profiles. In March 2012, Facebook decided they might take legal action against employers for gaining access to employee's profiles through their passwords. According to Facebook Chief Privacy Officer for policy, Erin Egan, the company has worked hard to give its users the tools to control who sees their information. He also said users shouldn't be forced to share private information and communications just to get a job. According to the network's Statement of Rights and Responsibilities, sharing or soliciting a password is a violation of Facebook policy. Employees may still give their password information out to get a job, but according to Erin Egan, Facebook will continue to do their part to protect the privacy and security of their users. == Impacts == Use of social media by young people has caused significant problems for some applicants who are active on social media when they try to enter the job market. A survey of 17,000 young people in six countries in 2013 found that 1 in 10 people aged 16 to 34 have been rejected for a job because of online comments they made on social media websites. A 2014 survey of recruiters found that 93% of them check candidates' social media postings. Moreover, in 2015 professor Stijn Baert of Ghent University conducted a field experiment in which fictitious job candidates applied for real job vacancies in Belgium. They were identical except in one respect: their Facebook profile photos. It was found that candidates with the most wholesome photos were a lot more likely to receive invitations for job interviews than those with the more controversial photos. In addition, Facebook profile photos had a greater impact on hiring decisions when candidates were highly educated. These cases have created some privacy implications as to whether or not companies should have the right to look at employee's Facebook profiles. In March 2012, Facebook decided they might take legal action against employers for gaining access to employee's profiles through their passwords. According to Facebook Chief Privacy Officer for policy, Erin Egan, the company has worked hard to give its users the tools to control who sees their information. He also said users shouldn't be forced to share private information and communications just to get a job. According to the network's Statement of Rights and Responsibilities, sharing or soliciting a password is a violation of Facebook policy. Employees may still give their password information out to get a job, but according to Erin Egan, Facebook will continue to do their part to protect the privacy and security of their users. == Policy Responses == 26 US states now have laws against an employer requiring a current or potential employee to give the employer their username and password.

  Read more →
• 

  Telenet

  Telenet was an American commercial packet-switched network which went into service in August 16, 1975. It was the first FCC-licensed public data network in the United States. Various commercial and government interests paid monthly fees for dedicated lines connecting their computers and local networks to this backbone network. Free public dialup access to Telenet, for those who wished to access these systems, was provided in hundreds of cities throughout the United States. == History == After establishing that commercial operation of "value added carriers" was legal in the U.S., Bolt Beranek and Newman (BBN), who were the private contractors for constructing packet switching nodes (Interface Message Processor) for the ARPANET, set out to create a private sector version. The original founding company, Telenet Inc., was established by BBN. In January 1975, Telenet Communications Corporation announced that they had acquired the necessary venture capital after a two-year quest. Initially, Bob Kahn was the first President of Telenet; he then moved to ARPA as Larry Roberts left to become President of the company. Barry Wessler also joined from ARPA. On August 16 of the same year they began operating the first public data network. The network offered an email service called Telemail. Telenet had its first offices in downtown Washington, D.C., then moved to McLean, Virginia. It was acquired by GTE in 1979, and then moved to offices in Reston, Virginia. It was later acquired by Sprint and called "Sprintnet". Sprint migrated customers from Telenet to the modern-day Sprintlink IP network, one of many networks composing today's Internet. == Coverage == Originally, the public network had switching nodes in seven US cities: Washington, D.C. (network operations center as well as switching) Boston, Massachusetts New York, New York Chicago, Illinois Dallas, Texas San Francisco, California Los Angeles, California The switching nodes were fed by Telenet Access Controller (TAC) terminal concentrators both colocated and remote from the switches. By 1980, there were over 1000 switches in the public network. At that time, the next largest network using Telenet switches was that of Southern Bell, which had approximately 250 switches. In 1977, Telenet added a London node and a Network Control Centre in a London building of Britain's Post Office Telecommunications. == Internal network technology == Telenet initially used a proprietary virtual connection host interface. The network used statically defined hop-by-hop routing, using Prime commercial minicomputers as switches, but then migrated to a purpose-built multiprocessing switch based on 6502 microprocessors. Among the innovations of this second-generation switch was a patented arbitrated bus interface that created a switched fabric among the microprocessors. By contrast, a typical microprocessor-based system of the time used a bus; switched fabrics did not become common until about twenty years later, with the advent of PCI Express and HyperTransport. Most interswitch lines ran at 56 kbit/s, with a few, such as New York-Washington, at T1 (i.e., 1.544 Mbit/s). Originally, the switching tables could not be altered separately from the main executable code, and topology updates had to be made by deliberately crashing the switch code and forcing a reboot from the network management center. Improvements in the software allowed new tables to be loaded, but the network never used dynamic routing protocols. Multiple static routes, on a switch-by-switch basis, could be defined for fault tolerance. Network management functions continued to run on Prime minicomputers. Roberts and Barry Wessler joined the international effort to standardize the a protocol for packet-switched data communication based on virtual circuits shortly before it was finalized. The CCITT proposal for X.25 was being prepared by Rémi Després and other international experts. A few minor changes, which complemented the proposed specification, were accommodated to enable Telenet to join the agreement. Telenet adopted X.25 shortly after the protocol was published in March 1976. Its X.25 host interface was the first in the industry. The main internal protocol was a proprietary variant on X.75; Telenet also ran standard X.75 gateways to other packet switching networks. == Accessing the network == === Basic asynchronous access === Users could use modems on the Public Switched Telephone Network to dial TAC ports, calling either from "dumb" terminals or from computers emulating such terminals. Organizations with a large number of local terminals could install a TAC on their own site, which used a dedicated line, at up to 56 kbit/s, to connect to a switch at the nearest Telenet location. Dialup modems supported had a maximum speed of 1200 bit/s, and later 4800 bit/s. For example, a customer in NYC could dial into the local number, then type in a command similar to: which would connect (that "c") them to a computer system designated as number "555" located in the same vicinity as the standard telephone "area code" 301. One significant customer was an early (what would now be called) internet service provider The Source which had their equipment in Mclean, Va. Telenet offered a much lower nighttime rate when there were few corporate customers, and this let The Source set up a modestly priced offering to tens of thousands of customers. Another prominent customer in the 1980s was Quantum Link (now AOL). === Other access protocols === Telenet supported remote concentrators for IBM 3270 family intelligent terminals, which communicated, via X.25 to Telenet-written software that ran in IBM 370x series front-end processors. Telenet also supported Block Mode Terminal Interfaces (BMTI) for IBM Remote Job Entry terminals supporting the 2780/3780 and HASP Bisync protocols. === PC Pursuit === In the late 1980s, Telenet offered a service called PC Pursuit. For a flat monthly fee, customers could dial into the Telenet network in one city, then dial out on the modems in another city to access bulletin board systems and other services. PC Pursuit was popular among computer hobbyists because it sidestepped long-distance charges. In this sense, PC Pursuit was similar to the Internet, allowing any user to call any system as if it were local. On connection to the network, the user entered a 5-letter code for the target city they wished to call. This consisted of a 2-letter state code and a 3-letter acronym for the city. For instance, to call a system in Cleveland, Ohio, the user would enter the code OHCLV, for "OHio", "CLeVeland". Once connected, the user could dial out to any local number, and the system simulated a direct connection between the two endpoints.

  Read more →
• 

  Localhost

  In computer networking, localhost is a hostname that refers to the current computer used to access it. The name localhost is reserved for loopback purposes. It is used to access the network services that are running on the host via the loopback network interface. Using the loopback interface bypasses any local network interface hardware. == Loopback == The local loopback mechanism may be used to run a network service on a host without requiring a physical network interface, or without making the service accessible from the networks the computer may be connected to. For example, a locally installed website may be accessed from a Web browser by the URL http://localhost to display its home page. IPv4 network standards reserve the entire address block 127.0.0.0/8 (more than 16 million addresses) for loopback purposes. That means any packet sent to any of those addresses is looped back. The address 127.0.0.1 is the standard address for IPv4 loopback traffic; the rest are not supported by all operating systems. However, they can be used to set up multiple server applications on the host, all listening on the same port number. In the IPv6 addressing architecture there is only a single address assigned for loopback: ::1. The standard precludes the assignment of that address to any physical interface, as well as its use as the source or destination address in any packet sent to remote hosts. == Name resolution == The name localhost normally resolves to the IPv4 loopback address 127.0.0.1, and to the IPv6 loopback address ::1. This resolution is normally configured by the following lines in the operating system's hosts file: 127.0.0.1 localhost ::1 localhost The name may also be resolved by Domain Name System (DNS) servers, but there are special considerations governing the use of this name: An IPv4 or IPv6 address query for the name localhost must always resolve to the respective loopback address. Applications may resolve the name to a loopback address themselves, or pass it to the local name resolver mechanisms. When a name resolver receives an address (A or AAAA) query for localhost, it should return the appropriate loopback addresses, and negative responses for any other requested record types. Queries for localhost should not be sent to caching name servers. To avoid burdening the Domain Name System root servers with traffic, caching name servers should never request name server records for localhost, or forward resolution to authoritative name servers. When authoritative name servers receive queries for 'localhost' in spite of the provisions mentioned above, they should resolve them appropriately. In addition to the mapping of localhost to the loopback addresses (127.0.0.1 and ::1), localhost may also be mapped to other IPv4 (loopback) addresses and it is also possible to assign other, or additional, names to any loopback address. The mapping of localhost to addresses other than the designated loopback address range in the hosts file or in DNS is not guaranteed to have the desired effect, as applications may map the name internally. In the Domain Name System, the name .localhost is reserved as a top-level domain name, originally set aside to avoid confusion with the hostname localhost. Domain name registrars are precluded from delegating domain names in the top-level .localhost domain. == Historical notes == In 1981, the block 127.0.0.0/8 got a 'reserved' status, as not to assign it as a general purpose class A IP network. This block was officially assigned for loopback purposes in 1986. Its purpose as a Special Use IPv4 Address block was confirmed in 1994,, 2002, 2010,, and last in 2013. From the outset, in 1995, the single IPv6 loopback address ::1 was defined. Its purpose and definition was unchanged in 1998,, 2003,, and up to the current definition, in 2006. == Packet processing == The processing of any packet sent to a loopback address, is implemented in the link layer of the TCP/IP stack. Such packets are never passed to any network interface controller (NIC) or hardware device driver and must not appear outside of a computing system, or be routed by any router. This permits software testing and local services, even in the absence of any hardware network interfaces. Looped-back packets are distinguished from any other packets traversing the TCP/IP stack only by the special IP address they were addressed to. Thus, the services that ultimately receive them respond according to the specified destination. For example, an HTTP service could route packets addressed to 127.0.0.99:80 and 127.0.0.100:80 to different Web servers, or to a single server that returns different web pages. To simplify such testing, the hosts file may be configured to provide appropriate names for each address. Packets received on a non-loopback interface with a loopback source or destination address must be dropped. Such packets are sometimes referred to as Martian packets. As with any other bogus packets, they may be malicious and any problems they might cause can be avoided by applying bogon filtering. == Special cases == The releases of the MySQL database differentiate between the use of the hostname localhost and the use of the addresses 127.0.0.1 and ::1. When using localhost as the destination in a client connector interface of an application, the MySQL application programming interface connects to the database using a Unix domain socket, while a TCP connection via the loopback interface requires the direct use of the explicit address. One notable exception to the use of the 127.0.0.0/8 addresses is their use in Multiprotocol Label Switching (MPLS) traceroute error detection, in which their property of not being routable provides a convenient means to avoid delivery of faulty packets to end users.

  Read more →
• 

  Cloud testing

  Cloud testing is a form of software testing in which web applications use cloud computing environments (a "cloud") to simulate real-world user traffic. == Steps == Companies simulate real world Web users by using cloud testing services that are provided by cloud service vendors such as Advaltis, Compuware, HP, Keynote Systems, Neotys, RadView and SOASTA. Once user scenarios are developed and the test is designed, these service providers leverage cloud servers (provided by cloud platform vendors such as Amazon.com, Google, Rackspace, Microsoft, etc.) to generate web traffic that originates from around the world. Once the test is complete, the cloud service providers deliver results and analytics back to corporate IT professionals through real-time dashboards for a complete analysis of how their applications and the internet will perform during peak volumes. == Applications == Cloud testing is often seen as only performance or load tests, however, as discussed earlier it covers many other types of testing. Cloud computing itself is often referred to as the marriage of software as a service (SaaS) and utility computing. In regard to test execution, the software offered as a service may be a transaction generator and the cloud provider's infrastructure software, or may just be the latter. Distributed Systems and Parallel Systems mainly use this approach for testing, because of their inherent complex nature. D-Cloud is an example of such a software testing environment. == Tools == Leading cloud computing service providers include, among others, Amazon, Microsoft, Google, RadView, Skytap, HP and SOASTA. == Benefits == The ability and cost to simulate web traffic for software testing purposes has been an inhibitor to overall web reliability. The low cost and accessibility of the cloud's extremely large computing resources provides the ability to replicate real world usage of these systems by geographically distributed users, executing wide varieties of user scenarios, at scales previously unattainable in traditional testing environments. Minimal start-up time along with quality assurance can be achieved by cloud testing. Following are some of the key benefits: Reduction in capital expenditure Highly scalable

  Read more →
• 

  Social bot

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

  Read more →
• 

  Content engineering

  Content engineering is a term applied to an engineering specialty dealing with the complexities around the use of content in computer-facilitated environments. Content authoring and production, content management, content modeling, content conversion, and content use and repurposing are all areas involving this practice. It is not a specialty with wide industry recognition and is often performed on an ad hoc basis by members of software development or content production or marketing staff, but is beginning to be recognized as a necessary function in any complex content-centric project involving both content production as well as software system development mainly involving content management systems (CMS) or digital experience platforms (DXP). Content engineering tends to bridge the gap between groups involved in the production of content (publishing and editorial staff, marketing, sales, human resources) and more technologically oriented departments such as software development, or IT that put this content to use in web or other software-based environments, and requires an understanding of the issues and processes of both sides. Typically, content engineering involves extensive use of embedded XML technologies, XML being the most widespread language for representing structured content. Content management systems are a key technology often used in the practice of content engineering. == Definition == Content engineering is the practice of organizing the shape and structure of content by deploying content and metadata models, in authoring and publishing processes in a manner that meets the requirements of an organization's Content Strategy, and its implementation through the use of technology such as CMS, XML, schema markup, artificial intelligence, APIs and others. == Purpose and goal == In very general terms, content engineering practices aim to maximize the ROI of content through content reuse and improving efficiency of content marketing, content operations, content strategy. Content engineering can help address content challenges that fairly typical organizations face: Siloed content supply chains Duplicate content in a myriad of formats Inefficient content authoring workflows Chunky, unstructured content Outdated technology Technology in place does not match needs Inability to reuse content across channels (multi-channel content) Metadata and schema are not used Lack of standards for metadata Lack of findability of content for internal and external use Poor SEO performance Inability to implement personalization == Key skills == Content engineering draws on a combination of technical, strategic, and editorial competencies. Practitioners typically require proficiency across several domains: === Content modeling and information architecture === Content engineers design structured content models that define how content is created, stored, and distributed. This includes building taxonomies, ontologies, and metadata schemas that enable content reuse across channels and platforms. === Structured content and markup languages === Proficiency in XML, JSON, HTML, and schema.org markup is fundamental. Content engineers use these languages to structure content for machine readability, search engine optimization, and interoperability between systems. === Content management systems and platforms === Content engineers require working knowledge of content management systems (CMS), digital experience platforms (DXP), and headless CMS architectures. This includes configuring content types, workflows, and publishing pipelines within these systems. === Workflow design and automation === Designing and implementing content workflows - from authoring through review, approval, and distribution - is a core function. Increasingly, this involves configuring AI-assisted and agentic workflows that automate research, drafting, repurposing, and distribution tasks at scale. === Content strategy and editorial understanding === Unlike purely technical roles, content engineering requires a working understanding of content strategy, brand management, editorial standards, and audience analysis. Content engineers must translate strategic objectives into technical content structures and system configurations. === API integration and data interoperability === Content engineers work with APIs to connect content systems, analytics platforms, distribution channels, and third-party services. Understanding how content flows between systems is essential for enabling multi-channel publishing and content personalization. === Analytics and performance measurement === Measuring content effectiveness through web analytics, SEO performance data, and engagement metrics informs how content engineers refine structures, metadata, and distribution workflows. == The role of a content engineer == Content engineers bridge the divide between content strategists and producers and the developers and content managers who publish and distribute content. But rather than simply wedging themselves between these players, content engineers help define and facilitate the content structure during the entire content strategy, production and distribution cycle from beginning to end. As the role has evolved, content engineers are increasingly expected to build and manage AI-powered content systems, moving beyond traditional CMS configuration into agentic workflows that automate content research, production, and distribution. By integrating skills in business and technology, content engineers do not see content as static or finished. Rather, they look at the value of the content and how it can best be adapted and personalized to serve customers and emerging content platforms, technologies, and opportunities. === Create customer experience === Content marketing suffers from two fundamental limitations that constrain the true power and potential that a great content marketing plan can bring to a business' bottom line: Content relevance: how to make content more relevant and personalized to their audiences. The marketer and content strategist direct the customer experience itself, and the content engineer makes it happen with content structure, schema, metadata, microdata, taxonomy, and CMS topology. Content agility: Marketers who are burdened with one-size-fits-all content remain stuck managing their content rather than their customers' experience. Content engineers give marketers the "super powers" to move content-powered experiences across interfaces and personalization variants. === Break down barriers === Empower content strategists: Content engineers work with content strategists by helping them connect content not as a fixed message, but as a modular construct which can be channeled and manipulated. Enable content producers: A content engineer will work with a content producer by helping to find new sources of content and ways the content can be combined and presented. Guide and free developers: The content engineer helps translate marketing strategy into clear technical needs and functions developers can build into content management systems Enhance content management: Develop content structures that make it easier for content writers and content managers to author to a single, very usable, interface for even complex content types that might contain dozens of elements. Engineer content for success: Content engineers help all members of a marketing team work more smoothly, with the support and structures needed to get the most out of the content they produce. === Salary benchmarks === Content engineering roles command significantly higher salaries than traditional content marketing positions. In the United States, IC-level content engineers earn between $120,000 and $165,000 annually, while senior roles reach $160,000 to $220,000. Head of content engineering positions range from $200,000 to $280,000, and VP-level roles can exceed $375,000. The emergence of dedicated content engineer job postings from companies such as Exit Five reflects the growing recognition of the role as a distinct function within marketing organizations.

  Read more →
• 

  Semiotics of social networking

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

  Read more →
• 

  VueScan

  VueScan is a computer program for image scanning, especially of photographs, including negatives. It supports optical character recognition (OCR) of text documents. The software can be downloaded and used free of charge, but adds a watermark on scans until a license is purchased. == Purpose == VueScan is intended to work with a large number of image scanners, excluding specialised professional scanners such as drum scanners, on many computer operating systems (OS), even if drivers for the scanner are not available for the OS. These scanners are supplied with device drivers and software to operate them, included in their price. A 2014 review considered that the reasons to purchase VueScan are to allow older scanners not supported by drivers for newer operating systems to be used in more up-to-date systems and for better scanning and processing of photographs (prints; also slides and negatives when supported by scanners) than is afforded by manufacturers' software. The review did not report any advantages to VueScan's processing of documents over other software. The reviewer considered VueScan comparable to SilverFast, a similar program, with support for some specific scanners better in one or the other. Vuescan supports more scanners, with a single purchase giving access to the full range of both film and flatbed scanners, and costs less. The VueScan program can be used with its own drivers or with drivers supplied by the scanner manufacturer, if supported by the operating system. VueScan drivers can also be used without the VueScan program by application software that supports scanning directly, such as Adobe Photoshop, again enabling the use of scanners without current manufacturers' drivers. In 2019 when Apple released macOS Catalina, they removed support for running 32-bit programs, including 32-bit drivers for scanning equipment. In response, Hamrick released VueScan 9.7, effectively saving thousands of scanners from being rendered obsolete. == Overview == VueScan enables the user to modify and fine-tune the scanning parameters. The program uses its own independent method to interface with scanner hardware, and can support many older scanners under computer operating systems for which drivers are not available, allowing old scanners to be used with newer platforms that do not otherwise support them. VueScan supports an increasing number of scanners and digital cameras; 2,400 on Windows, 2,100 on Mac OS X and 1,900 on Linux in 2018. VueScan is supplied as one downloadable file for each operating system, which supports the full range of scanners. Without the purchase of a license, the program runs in fully functional demonstration mode, identical to Professional mode, except that watermarks are superimposed on saved and printed images. Purchase of a license removes the watermark. A standard license allows updates for one year; a professional license allows unlimited updates and provides some additional features. VueScan supports optical character recognition (OCR), with English included, and 32 additional language packages available on its website. In September 2011, VueScan co-developer Ed Hamrick said that he was selling US$3 million per year of VueScan licenses.

  Read more →
• 

  Atomicity (database systems)

  In database systems, atomicity (; from Ancient Greek: ἄτομος, romanized: átomos, lit. 'undividable') is the property of a database transaction consisting of an indivisible and irreducible series of database operations such that either all occur, or none occur. It is one of the ACID transaction properties: Atomicity, Consistency, Isolation, Durability. A guarantee of atomicity prevents partial database updates from occurring, because they can cause greater problems than rejecting the whole series outright. As a consequence, an atomic transaction cannot be observed to be in progress by another database client: at one moment in time, it has not yet happened, and at the next it has already occurred in whole (or nothing happened if the transaction was cancelled in progress). An example of transaction atomicity could be a digital monetary transfer from bank account A to account B. It consists of two operations, debiting the money from account A and crediting it to account B. Performing both of these operations inside of an atomic transaction ensures that the database remains in a consistent state, if either operation fails there will not be any unaccountable credits or debits affecting either account. The same term is also used in the definition of First normal form in database systems, where it instead refers to the concept that the values for fields may not consist of multiple smaller values to be decomposed, such as a string into which multiple names, numbers, dates, or other types may be packed. == Orthogonality == Atomicity does not behave completely orthogonally with regard to the other ACID properties of transactions. For example, isolation relies on atomicity to roll back the enclosing transaction in the event of an isolation violation such as a deadlock; consistency also relies on atomicity to roll back the enclosing transaction in the event of a consistency violation by an illegal transaction. As a result of this, a failure to detect a violation and roll back the enclosing transaction may cause an isolation or consistency failure. == Implementation == Typically, systems implement Atomicity by providing some mechanism to indicate which transactions have started and which finished; or by keeping a copy of the data before any changes occurred (Read-copy-update). Several filesystems have developed methods for avoiding the need to keep multiple copies of data, using journaling (see journaling file system). Databases usually implement this using some form of logging/journaling to track changes. The system synchronizes the logs (often the metadata) as necessary after changes have successfully taken place. Afterwards, crash recovery ignores incomplete entries. Although implementations vary depending on factors such as concurrency issues, the principle of atomicity – i.e. complete success or complete failure – remain. Ultimately, any application-level implementation relies on operating-system functionality. At the file-system level, POSIX-compliant systems provide system calls such as open(2) and flock(2) that allow applications to atomically open or lock a file. At the process level, POSIX Threads provide adequate synchronization primitives. The hardware level requires atomic operations such as Test-and-set, Fetch-and-add, Compare-and-swap, or Load-Link/Store-Conditional, together with memory barriers. Portable operating systems cannot simply block interrupts to implement synchronization, since hardware that lacks concurrent execution such as hyper-threading or multi-processing is now extremely rare. In distributed and sharded databases, atomicity is complicated by network latency and the potential for partial failures. While traditional distributed systems often employ locking protocols (like 2PC) to ensure cross-shard atomicity, these can introduce performance bottlenecks. Recent research into distributed ledger consensus suggests alternative models, such as "braided synchronization". This technique, utilized in protocols like Cerberus, intertwines the consensus phases of multiple shards to enforce atomic guarantees without a global ordering of all transactions.

  Read more →
• 

  Social bot

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

  Read more →
• 

  Social media use in African politics

  Since the Egyptian Revolution in 2011 and the Tunisian Revolution, social media, especially Facebook, Twitter, and YouTube, began to gain traction as a political tool in Africa. Various political actors have used social media to pursue a wide range of political objectives. State actors can use social media to encourage political discourse, campaign, or implement censorship and surveillance. Non-state actors, such as civil society organizations and opposition movements, can use social media to address political concerns and to organize widespread uprisings, such as the 2014 Burkinabé uprising. Meanwhile, extremist organizations can use social media to further their propaganda and recruitment. However, social media has been criticized for its limited accessibility and for facilitating the spread of misinformation, causing some skepticism about its effectiveness. Due to low entry barriers and user-generated content, social media provides a platform where people from different social classes can engage and interact with one another. Under traditional media, the public had limited opportunities to voice their political opinions. Social media enables people to both create and consume content. The public has become increasingly comfortable and confident in expressing political opinions online, often away from government scrutiny. Scholars argue that social media use has democratizing effects in African countries. == State actors == === Promoting political discourse === Through social media, the government and its citizens can discuss policy ideas, policy implementation, and political actions. Regardless of geographical location and distance, people are able to voice their opinions to the government. Social media includes citizens who were previously not able to express their discontent or share their ideas to the government. As state actors keep the public informed, social media can increase civic engagement. With more civic engagement, policies can be discussed without politicization. Before the commonplace use of social media, African countries faced weak feedback mechanisms that effectively excluded the average African citizen from policy discourse. In South Africa, the government uses social media to connect with constituencies. The South African president runs an official Twitter, Facebook, YouTube, and Flickr accounts to engage with the public. === Campaigning === Political parties also use social media for political campaigns during election periods. In South Africa, the ANC (African National Congress) and DA (Democratic Alliance) use social media for political purposes. These parties specifically use Facebook as a tool for campaigning and engaging with the public to improve their relationship with citizens. Nigerian President Goodluck Jonathan employed social media to campaign for the presidential election in 2011, which he won. When President Goodluck Jonathan announced his bid for the presidency on social media in 2010, it reached about 217,000 people. As his campaign progressed, President Goodluck Jonathan was able to increase his followers to half a million by early 2011. === Censorship & Surveillance === While state actors can use social media to encourage their party or discourse, social media can be used to censor and surveil citizens. For example, the ANC and DA use Facebook to monitor South Africans. The government is able to track down people who have spoken against the government and translate this information into physical action to stop any possibility of a revolution. Social media platforms can be shut down to manipulate the flow of information. In Chad, citizens cannot access information through online platforms. This censorship blocked "Facebook, Twitter, WhatsApp and Viber". In the Democratic Republic of Congo, the government shut down the internet before contested elections. In Zimbabwe, the government shut down the internet to hide civilian protests against fuel price increases. == Non-state actors == === Civil society organizations (CSOs) === Civil society organizations have also used social media networks in an effort to recruit supporters and communicate with the public. CSOs can use social media to mobilize people to support their cause, such as the Ghanaian Committee for Joint Action (CJA). In 2005 and 2006, the CJA gathered support to protest against the 50% fuel price increase. CSOs can play the role of a counterforce against state actors and state propaganda during times of crises, such as protests and military clashes. In some cases, CSOs release their own videos and photos on social media which challenges traditional forms of media. CSOs have also served to monitor elections to reduce corruption and violence during election day. For instance, the Zambian Bantu Watch started the #bantuwatch social media campaign to monitor the 2011 presidential election. Zambians used Facebook and Twitter to report polling station results to mitigate election fraud and election violence. In South Africa, CSOs created 'amandla.mobi' to campaign for public policies by creating petitions. Through 'amandla.mobi', CSOs are able to circulate petitions on social media to collect signatures. South African CSOs reported how social media helped their organizations to gain support and share ideas. However, CSOs struggle to attract media attention and often have to pay for media coverage. === Opposition forces against the government === Social media is also used by the public or opposition forces against the government. Through horizontal social media, organizing can lead to street protests and revolutions, some of which are successful. For instance, during the Egyptian revolution of 2011, "The Day of the Revolution Against Torture, Poverty, Corruption, and Unemployment" and "We Are All Khaled Said" gathered support against President Hosni Mubarak. In particular, "We Are All Khaled Said" had Egyptian citizens gather around the death of Khaled Said who was brutally tortured and killed by the Egyptian government because Said wanted to uncover government corruption. As unrest erupted into public demonstrations, President Hosni Mubarak was forced to resign. Witnessing the success of social media during the Egyptian revolution, the Tunisian Revolution, or the Jasmine Revolution, mobilized through Facebook and Twitter. Likewise, in South Africa, Malawi, and Mozambique, these countries have used social media as "new protest drums." Due to social media's low entry barrier, opposition forces against the government can facilitate political discourse that can lead to accountability. Whistleblowers and opposition forces are able to expose corruption through social media, where they face less repression while reaching a larger audience. For example, the youth of Zimbabwe and South Africa use Facebook to discuss politics without judgment. Specifically, in Zimbabwe, political youth used Facebook to avoid state surveillance. Social media is used as a supplemental tool for activism. In 2015, South African student activists started the hashtag #RhodesMustFall to push the issue of colonialism and racism at the forefront of the public. === Extremist organizations === Social media is easily accessible and created by user-based content. Therefore, marginalized groups are able to use social media to spread extremist ideas. For instance, Boko Haram created the Media Office of West Africa Province and perpetuated propaganda through Twitter and YouTube. Boko Haram's online propaganda campaign targets and persuades young dissuaded Nigerians to join their cause. It is important to note that social media has also been used against Boko Haram. In April 2014, Boko Haram kidnapped 276 schoolgirls and an international campaign fought for their return through #BringBackOurGirls. Another extremist group, Al-Shabaab, has created an online presence through Twitter and YouTube. Through these social media networks, Al-Shabaab recruits new members to their extremist group through their propaganda which emphasizes the group's successes. Albeit their efforts, Al-Shabaab has not been very successful in coordinating their members but they are successful in financing their group. Furthermore, the Islamic State of Iraq and the Levant (ISIL) use social media to target and recruit individuals to their cause. ISIL's social media usage is more diverse compared to Boko Haram and Al-Shabaab; ISIL uses "Facebook, Twitter, YouTube, WhatsApp, Telegram, JustPaste.it, Kik and Ask.fm." Since ISIL's Twitter accounts kept getting shut down, ISIL uses Telegram and WhatsApp chat rooms to privately conduct meetings. Due to the spread of extremist ideology, Zhuravskaya et al. acknowledge social media's potential to be misused. == Challenges == Although social media can be used as a political tool, it faces challenges in Africa. Due to low literacy rates in Africa, social media networks exclude many of the population members. In addition, lack of access to electricity and the internet can fur

  Read more →
• 

  Swap chain

  In computer graphics, a swap chain (also swapchain) is a series of virtual framebuffers used by the graphics card and graphics API for frame rate stabilization, stutter reduction, and several other purposes. Because of these benefits, many graphics APIs require the use of a swap chain. The swap chain usually exists in graphics memory, but it can exist in system memory as well. A swap chain with two buffers is a kind of double buffer. == Function == In every swap chain there are at least two buffers. The first framebuffer, the screenbuffer, is the buffer that is rendered to the output of the video card. The remaining buffers are known as backbuffers. Each time a new frame is displayed, the first backbuffer in the swap chain takes the place of the screenbuffer, this is called presentation or swapping. A variety of other actions may be taken on the previous screenbuffer and other backbuffers (if they exist). The screenbuffer may be simply overwritten or returned to the back of the swap chain for further processing. The action taken is decided by the client application and is API dependent. == Direct3D == Microsoft Direct3D implements a SwapChain class. Each host device has at least one swap chain assigned to it, and others may be created by the client application. The API provides three methods of swapping: copy, discard, and flip. When the SwapChain is set to flip, the screenbuffer is copied onto the last backbuffer, then all the existing backbuffers are copied forward in the chain. When copy is set, each backbuffer is copied forward, but the screenbuffer is not wrapped to the last buffer, leaving it unchanged. Flip does not work when there is only one backbuffer, as the screenbuffer is copied over the only backbuffer before it can be presented. In discard mode, the driver selects the best method. == Comparison with triple buffering == Outside the context of Direct3D, triple buffering refers to the technique of allowing an application to draw to whichever back buffer was least recently updated. This allows the application to always proceed with rendering, regardless of the pace at which frames are being drawn by the application or the pace at which frames are being sent to the display. Triple buffering may result in a frame being discarded without being displayed if two or more newer frames are completely rendered in the time it takes for one frame to be sent to the display. By contrast, Direct3D swap chains are a strict first-in, first-out queue, so every frame that is drawn by the application will be displayed even if newer frames are available. Direct3D does not implement a most-recent buffer swapping strategy, and Microsoft's documentation calls a Direct3D swap chain of three buffers "triple buffering". Triple buffering as described above is superior for interactive purposes such as gaming, but Direct3D swap chains of more than three buffers can be better for tasks such as presenting frames of a video where the time taken to decode each frame may be highly variable.

  Read more →
• 

  Cut, copy, and paste

  Cut, copy, and paste are essential commands of modern human–computer interaction and user interface design. They offer an interprocess communication technique for transferring data through a computer's user interface. The cut command removes the selected data from its original position, and the copy command creates a duplicate; in both cases the selected data is kept in temporary storage called the clipboard. Clipboard data is later inserted wherever a paste command is issued. The data remains available to any application supporting the feature, thus allowing easy data transfer between applications. The command names are a (skeuomorphic) interface metaphor based on the physical procedure used in manuscript print editing to create a page layout, like with paper. The commands were pioneered into computing by Xerox PARC in 1974, popularized by Apple Computer in the 1983 Lisa workstation and the 1984 Macintosh computer, and in a few home computer applications such as the 1984 word processor Cut & Paste. This interaction technique has close associations with related techniques in graphical user interfaces (GUIs) that use pointing devices such as a computer mouse (by drag and drop, for example). Typically, clipboard support is provided by an operating system as part of its GUI and widget toolkit. The capability to replicate information with ease, changing it between contexts and applications, involves privacy concerns because of the risks of disclosure when handling sensitive information. Terms like cloning, copy forward, carry forward, or re-use refer to the dissemination of such information through documents, and may be subject to regulation by administrative bodies. == History == === Origins === The term "cut and paste" comes from the traditional practice in manuscript editing, whereby people cut paragraphs from a page with scissors and paste them onto another page. This practice remained standard into the 1980s. Stationery stores sold "editing scissors" with blades long enough to cut an 8½"-wide page. The advent of photocopiers made the practice easier and more flexible. The act of copying or transferring text from one part of a computer-based document ("buffer") to a different location within the same or different computer-based document was a part of the earliest on-line computer editors. As soon as computer data entry moved from punch-cards to online files (in the mid/late 1960s) there were "commands" for accomplishing this operation. This mechanism was often used to transfer frequently-used commands or text snippets from additional buffers into the document, as was the case with the QED text editor. === Early methods === The earliest editors (designed for teleprinter terminals) provided keyboard commands to delineate a contiguous region of text, then delete or move it. Since moving a region of text requires first removing it from its initial location and then inserting it into its new location, various schemes had to be invented to allow for this multi-step process to be specified by the user. Often this was done with a "move" command, but some text editors required that the text be first put into some temporary location for later retrieval/placement. In 1983, the Apple Lisa became the first text editing system to call that temporary location "the clipboard". Earlier control schemes such as NLS used a verb—object command structure, where the command name was provided first and the object to be copied or moved was second. The inversion from verb—object to object—verb on which copy and paste are based, where the user selects the object to be operated before initiating the operation, was an innovation crucial for the success of the desktop metaphor as it allowed copy and move operations based on direct manipulation. === Popularization === Inspired by early line and character editors, such as Pentti Kanerva's TV-Edit, that broke a move or copy operation into two steps—between which the user could invoke a preparatory action such as navigation—Lawrence G. "Larry" Tesler proposed the names "cut" and "copy" for the first step and "paste" for the second step. Beginning in 1974, he and colleagues at Xerox PARC implemented several text editors that used cut/copy-and-paste commands to move and copy text. Apple Computer popularized this paradigm with its Lisa (1983) and Macintosh (1984) operating systems and applications. The functions were mapped to key combinations using the ⌘ Command key as a special modifier, which is held down while also pressing X for cut, C for copy, or V for paste. These few keyboard shortcuts allow the user to perform all the basic editing operations, and the keys are clustered at the left end of the bottom row of the standard QWERTY keyboard. These are the standard shortcuts: Control-Z (or ⌘ Command+Z) to undo Control-X (or ⌘ Command+X) to cut Control-C (or ⌘ Command+C) to copy Control-V (or ⌘ Command+V) to paste The IBM Common User Access (CUA) standard also uses combinations of the Insert, Del, Shift and Control keys. Early versions of Windows used the IBM standard. Microsoft later also adopted the Apple key combinations with the introduction of Windows, using the control key as modifier key. Similar patterns of key combinations, later borrowed by others, are widely available in most GUI applications. The original cut, copy, and paste workflow, as implemented at PARC, utilizes a unique workflow: With two windows on the same screen, the user could use the mouse to pick a point at which to make an insertion in one window (or a segment of text to replace). Then, by holding shift and selecting the copy source elsewhere on the same screen, the copy would be made as soon as the shift was released. Similarly, holding shift and control would copy and cut (delete) the source. This workflow requires many fewer keystrokes/mouse clicks than the current multi-step workflows, and did not require an explicit copy buffer. It was dropped, one presumes, because the original Apple and IBM GUIs were not high enough density to permit multiple windows, as were the PARC machines, and so multiple simultaneous windows were rarely used. == Cut and paste == Computer-based editing can involve very frequent use of cut-and-paste operations. Most software-suppliers provide several methods for performing such tasks, and this can involve (for example) key combinations, pulldown menus, pop-up menus, or toolbar buttons. The user selects or "highlights" the text or file for moving by some method, typically by dragging over the text or file name with the pointing-device or holding down the Shift key while using the arrow keys to move the text cursor. The user performs a "cut" operation via key combination Ctrl+x (⌘+x for Macintosh users), menu, or other means. Visibly, "cut" text immediately disappears from its location. "Cut" files typically change color to indicate that they will be moved. Conceptually, the text has now moved to a location often called the clipboard. The clipboard typically remains invisible. On most systems only one clipboard location exists, hence another cut or copy operation overwrites the previously stored information. Many UNIX text-editors provide multiple clipboard entries, as do some Macintosh programs such as Clipboard Master, and Windows clipboard-manager programs such as the one in Microsoft Office. The user selects a location for insertion by some method, typically by clicking at the desired insertion point. A paste operation takes place which visibly inserts the clipboard text at the insertion point. (The paste operation does not typically destroy the clipboard text: it remains available in the clipboard and the user can insert additional copies at other points). Whereas cut-and-paste often takes place with a mouse-equivalent in Windows-like GUI environments, it may also occur entirely from the keyboard, especially in UNIX text editors, such as Pico or vi. Cutting and pasting without a mouse can involve a selection (for which Ctrl+x is pressed in most graphical systems) or the entire current line, but it may also involve text after the cursor until the end of the line and other more sophisticated operations. The clipboard usually stays invisible, because the operations of cutting and pasting, while actually independent, usually take place in quick succession, and the user (usually) needs no assistance in understanding the operation or maintaining mental context. Some application programs provide a means of viewing, or sometimes even editing, the data on the clipboard. == Copy and paste == The term "copy-and-paste" refers to the popular, simple method of reproducing text or other data from a source to a destination. It differs from cut and paste in that the original source text or data does not get deleted or removed. The popularity of this method stems from its simplicity and the ease with which users can move data between various applications visually – without resorting to permanent storage. Use in healthcare do

  Read more →
• 

  G.hn

  Gigabit Home Networking (G.hn) is a specification for wired home networking that supports speeds up to 2 Gbit/s and operates over four types of legacy wires: telephone wiring, coaxial cables, power lines and plastic optical fiber. Some benefits of a multi-wire standard are lower equipment development costs and lower deployment costs for service providers (by allowing customer self-install). == History == G.hn was developed under the International Telecommunication Union's Telecommunication Standardization sector (the ITU-T) and promoted by the HomeGrid Forum and several other organizations. ITU-T Recommendation (the ITU's term for standard) G.9960, which received approval on October 9, 2009, specified the physical layers and the architecture of G.hn. The Data Link Layer (Recommendation G.9961) was approved on June 11, 2010. Prominent organizations, including CEPca, HomePNA, and UPA, who were creators of some of these interfaces, rallied behind the latest version of the standard, emphasizing its potential and significance in the home networking domain. Moreover, the ITU-T extended the technology with multiple input, multiple output (MIMO) technology to increase data rates and signaling distance. This new feature was approved in March 2012 under G.9963 Recommendation. The development and promotion of G.hn have been significantly supported by the HomeGrid Forum and several other organizations. The technology was not only designed to address home-networking challenges but also found applications beyond this initial scope, showcasing its versatility and potential in the networking domain. == Technical specifications == === Technical overview === G.hn specifies a single physical layer based on fast Fourier transform (FFT) orthogonal frequency-division multiplexing (OFDM) modulation and low-density parity-check code (LDPC) forward error correction (FEC) code. G.hn includes the capability to notch specific frequency bands to avoid interference with amateur radio bands and other licensed radio services. G.hn includes mechanisms to avoid interference with legacy home networking technologies and also with other wireline systems such as VDSL2 or other types of DSL used to access the home. OFDM systems split the transmitted signal into multiple orthogonal sub-carriers. In G.hn each one of the sub-carriers is modulated using QAM. The maximum QAM constellation supported by G.hn is 4096-QAM (12-bit QAM). The G.hn media access control is based on a time division multiple access (TDMA) architecture, in which a "domain master" schedules Transmission Opportunities (TXOPs) that can be used by one or more devices in the "domain". There are two types of TXOPs: Contention-Free Transmission Opportunities (CFTXOP), which have a fixed duration and are allocated to a specific pair of transmitter and receiver. CFTXOP are used for implementing TDMA Channel Access for specific applications that require quality of service (QoS) guarantees. Shared Transmission Opportunities (STXOP), which are shared among multiple devices in the network. STXOP are divided into Time Slots (TS). There are two types of TS: Contention-Free Time Slots (CFTS), which are used for implementing "implicit" token passing Channel Access. In G.hn, a series of consecutive CFTS is allocated to a number of devices. The allocation is performed by the "domain master" and broadcast to all nodes in the network. There are pre-defined rules that specify which device can transmit after another device has finished using the channel. As all devices know "who is next", there is no need to explicitly send a "token" between devices. The process of "passing the token" is implicit and ensures that there are no collisions during Channel access. Contention-Based Time Slots (CBTS), which are used for implementing CSMA/CARP Channel Access. In general, CSMA systems cannot completely avoid collisions, so CBTS are only useful for applications that do not have strict Quality of Service requirements. ==== Optimization for each medium ==== Although most elements of G.hn are common for all three media supported by the standard (power lines, phone lines and coaxial cable), G.hn includes media-specific optimizations for each media. Some of these media-specific parameters include: OFDM Carrier Spacing: 195.31 kHz in coaxial, 48.82 kHz in phone lines, 24.41 kHz in power lines. FEC Rates: G.hn's FEC can operate with code rates 1/2, 2/3, 5/6, 16/18 and 20/21. Although these rates are not media specific, it is expected that the higher code rates will be used in cleaner media (such as coaxial) while the lower code rates will be used in noisy environments such as power lines. Automatic repeat request (ARQ) mechanisms: G.hn supports operation both with and without ARQ (re-transmission). Although this is not media specific, it is expected that ARQ-less operation is sometimes appropriate for cleaner media (such as coaxial) while ARQ operation is appropriate for noisy environments such as power lines. Power levels and frequency bands: G.hn defines different power masks for each medium. MIMO support: Recommendation G.9963 includes provisions for transmitting G.hn signals over multiple AC wires (phase, neutral, ground), if they are physically available. In July 2016, G.9963 was updated to include MIMO support over twisted pairs. ==== Security ==== G.hn uses the Advanced Encryption Standard (AES) encryption algorithm (with a 128-bit key length) using the CCMP protocol to ensure confidentiality and message integrity. Authentication and key exchange is done following ITU-T Recommendation X.1035. G.hn specifies point-to-point security inside a domain, which means that each pair of transmitter and receiver uses a unique encryption key which is not shared by other devices in the same domain. For example, if node Alice sends data to node Bob, node Eve (in the same domain as Alice and Bob) will not be able to easily eavesdrop their communication. G.hn supports the concept of relays, in which one device can receive a message from one node and deliver it to another node farther away in the same domain. Relaying becomes critical for applications with complex network topologies that need to cover large distances, such as those found in industrial or utility applications. While a relay can read the source and target addresses, it cannot read the message's content due to its body being end-to-end-encrypted. ==== Profiles ==== The G.hn architecture includes the concept of profiles. Profiles are intended to address G.hn nodes with significantly different levels of complexity. In G.hn the higher complexity profiles are proper supersets of lower complexity profiles, so that devices based on different profiles can interoperate with each other. Examples of G.hn devices based on high complexity profiles are Residential Gateways or Set-Top Boxes. Examples of G.hn devices based on low complexity profiles are home automation, home security and smart grid devices. ==== Technical parameters ==== The chart depicts a summary of the crucial technical specifications of the G.hn standard. Many of these technical elements are consistent across different physical media, with variations seen in areas such as Tone Spacing and frequency ranges. This uniformity is essential as it allows silicon manufacturers to produce a singular chip capable of implementing all three media types, leading to cost savings. Presently, G.hn chipsets are compatible with all three media types. This compatibility allows system manufacturers to create devices that can adjust to any wiring type simply by modifying a software configuration in the equipment. === Spectrum === The G.hn spectrum depends on the medium as shown in the diagram below: === Protocol stack === G.hn specifies the physical layer and the data link layer, according to the OSI model. The G.hn Data Link Layer (Recommendation G.9961) is divided into three sub-layers: The Application Protocol Convergence (APC) Layer, which accepts frames (usually in Ethernet format) from the upper layer (Application Entity) and encapsulates them into G.hn APC protocol data units (APDUs). The maximum payload of each APDU is 214 bytes. The logical link control (LLC), which is responsible for encryption, aggregation, segmentation and automatic repeat-request. This sub-layer is also responsible for "relaying" of APDUs between nodes that may not be able to communicate through a direct connection. The medium access control (MAC), which schedules channel access. The G.hn physical layer (Recommendation G.9960) is divided into three sub-layers: The Physical Coding Sub-layer (PCS), responsible for generating PHY headers. The Physical Medium Attachment (PMA), responsible for scrambling and forward error correction coding/decoding. The Physical Medium Dependent (PMD), responsible for bit-loading and OFDM modulation. The interface between the Application Entity and the Data Link Layer is called A-interface. The interface between the Data Link Layer and the ph

  Read more →