The Harris corner detector is a corner detection operator that is commonly used in computer vision algorithms to extract corners and infer features of an image. It was first introduced by Chris Harris and Mike Stephens in 1988 upon the improvement of Moravec's corner detector. Compared to its predecessor, Harris' corner detector takes the differential of the corner score into account with reference to direction directly, instead of using shifting patches for every 45 degree angles, and has been proved to be more accurate in distinguishing between edges and corners. Since then, it has been improved and adopted in many algorithms to preprocess images for subsequent applications. == Introduction == A corner is a point whose local neighborhood stands in two dominant and different edge directions. In other words, a corner can be interpreted as the junction of two edges, where an edge is a sudden change in image brightness. Corners are the important features in the image, and they are generally termed as interest points which are invariant to translation, rotation and illumination. Although corners are only a small percentage of the image, they contain the most important features in restoring image information, and they can be used to minimize the amount of processed data for motion tracking, image stitching, building 2D mosaics, stereo vision, image representation and other related computer vision areas. In order to capture the corners from the image, researchers have proposed many different corner detectors including the Kanade-Lucas-Tomasi (KLT) operator and the Harris operator which are most simple, efficient and reliable for use in corner detection. These two popular methodologies are both closely associated with and based on the local structure matrix. Compared to the Kanade-Lucas-Tomasi corner detector, the Harris corner detector provides good repeatability under changing illumination and rotation, and therefore, it is more often used in stereo matching and image database retrieval. Although there still exist drawbacks and limitations, the Harris corner detector is still an important and fundamental technique for many computer vision applications. == Development of Harris corner detection algorithm == Source: Without loss of generality, we will assume a grayscale 2-dimensional image is used. Let this image be given by I {\displaystyle I} . Consider taking an image patch ( x , y ) ∈ W {\displaystyle (x,y)\in W} (window) and shifting it by ( Δ x , Δ y ) {\displaystyle (\Delta x,\Delta y)} . The sum of squared differences (SSD) between these two patches, denoted f {\displaystyle f} , is given by: f ( Δ x , Δ y ) = ∑ ( x k , y k ) ∈ W ( I ( x k , y k ) − I ( x k + Δ x , y k + Δ y ) ) 2 {\displaystyle f(\Delta x,\Delta y)={\underset {(x_{k},y_{k})\in W}{\sum }}\left(I(x_{k},y_{k})-I(x_{k}+\Delta x,y_{k}+\Delta y)\right)^{2}} I ( x + Δ x , y + Δ y ) {\displaystyle I(x+\Delta x,y+\Delta y)} can be approximated by a Taylor expansion. Let I x {\displaystyle I_{x}} and I y {\displaystyle I_{y}} be the partial derivatives of I {\displaystyle I} , such that I ( x + Δ x , y + Δ y ) ≈ I ( x , y ) + I x ( x , y ) Δ x + I y ( x , y ) Δ y {\displaystyle I(x+\Delta x,y+\Delta y)\approx I(x,y)+I_{x}(x,y)\Delta x+I_{y}(x,y)\Delta y} This produces the approximation f ( Δ x , Δ y ) ≈ ∑ ( x , y ) ∈ W ( I x ( x , y ) Δ x + I y ( x , y ) Δ y ) 2 , {\displaystyle f(\Delta x,\Delta y)\approx {\underset {(x,y)\in W}{\sum }}\left(I_{x}(x,y)\Delta x+I_{y}(x,y)\Delta y\right)^{2},} which can be written in matrix form: f ( Δ x , Δ y ) ≈ ( Δ x Δ y ) M ( Δ x Δ y ) , {\displaystyle f(\Delta x,\Delta y)\approx {\begin{pmatrix}\Delta x&\Delta y\end{pmatrix}}M{\begin{pmatrix}\Delta x\\\Delta y\end{pmatrix}},} where M is the structure tensor, M = ∑ ( x , y ) ∈ W [ I x 2 I x I y I x I y I y 2 ] = [ ∑ ( x , y ) ∈ W I x 2 ∑ ( x , y ) ∈ W I x I y ∑ ( x , y ) ∈ W I x I y ∑ ( x , y ) ∈ W I y 2 ] {\displaystyle M={\underset {(x,y)\in W}{\sum }}{\begin{bmatrix}I_{x}^{2}&I_{x}I_{y}\\I_{x}I_{y}&I_{y}^{2}\end{bmatrix}}={\begin{bmatrix}{\underset {(x,y)\in W}{\sum }}I_{x}^{2}&{\underset {(x,y)\in W}{\sum }}I_{x}I_{y}\\{\underset {(x,y)\in W}{\sum }}I_{x}I_{y}&{\underset {(x,y)\in W}{\sum }}I_{y}^{2}\end{bmatrix}}} == Process of Harris corner detection algorithm == Commonly, Harris corner detector algorithm can be divided into five steps. Color to grayscale Spatial derivative calculation Structure tensor setup Harris response calculation Non-maximum suppression === Color to grayscale === If we use Harris corner detector in a color image, the first step is to convert it into a grayscale image, which will enhance the processing speed. The value of the gray scale pixel can be computed as a weighted sums of the values R, B and G of the color image, ∑ C ∈ { R , G , B } w C ⋅ C {\displaystyle \sum _{C\,\in \,\{R,G,B\}}w_{C}\cdot C} , where, e.g., w R = 0.299 , w G = 0.587 , w B = 1 − ( w R + w G ) = 0.114. {\displaystyle w_{R}=0.299,\ w_{G}=0.587,\ w_{B}=1-(w_{R}+w_{G})=0.114.} === Spatial derivative calculation === Next, we are going to find the derivative with respect to x and the derivative with respect to y, I x ( x , y ) {\displaystyle I_{x}(x,y)} and I y ( x , y ) {\displaystyle I_{y}(x,y)} . This can be approximated by applying Sobel operators. === Structure tensor setup === With I x ( x , y ) {\displaystyle I_{x}(x,y)} , I y ( x , y ) {\displaystyle I_{y}(x,y)} , we can construct the structure tensor M {\displaystyle M} . === Harris response calculation === For x ≪ y {\displaystyle x\ll y} , one has x ⋅ y x + y = x 1 1 + x / y ≈ x . {\displaystyle {\tfrac {x\cdot y}{x+y}}=x{\tfrac {1}{1+x/y}}\approx x.} In this step, we compute the smallest eigenvalue of the structure tensor using that approximation: λ min ≈ λ 1 λ 2 ( λ 1 + λ 2 ) = det ( M ) tr ( M ) {\displaystyle \lambda _{\min }\approx {\frac {\lambda _{1}\lambda _{2}}{(\lambda _{1}+\lambda _{2})}}={\frac {\det(M)}{\operatorname {tr} (M)}}} with the trace t r ( M ) = m 11 + m 22 {\displaystyle \mathrm {tr} (M)=m_{11}+m_{22}} . Another commonly used Harris response calculation is shown as below, R = λ 1 λ 2 − k ( λ 1 + λ 2 ) 2 = det ( M ) − k tr ( M ) 2 {\displaystyle R=\lambda _{1}\lambda _{2}-k(\lambda _{1}+\lambda _{2})^{2}=\det(M)-k\operatorname {tr} (M)^{2}} where k {\displaystyle k} is an empirically determined constant; k ∈ [ 0.04 , 0.06 ] {\displaystyle k\in [0.04,0.06]} . === Non-maximum suppression === In order to pick up the optimal values to indicate corners, we find the local maxima as corners within the window which is a 3 by 3 filter. == Improvement == Sources: Harris-Laplace Corner Detector Differential Morphological Decomposition Based Corner Detector Multi-scale Bilateral Structure Tensor Based Corner Detector == Applications == Image Alignment, Stitching and Registration 2D Mosaics Creation 3D Scene Modeling and Reconstruction Motion Detection Object Recognition Image Indexing and Content-based Retrieval Video Tracking
COVID-19 apps
COVID-19 apps include mobile-software applications for digital contact-tracing—i.e. the process of identifying persons ("contacts") who may have been in contact with an infected individual—deployed during the COVID-19 pandemic. Numerous tracing applications have been developed or proposed, with official government support in some territories and jurisdictions. Several frameworks for building contact-tracing apps have been developed. Privacy concerns have been raised, especially about systems that are based on tracking the geographical location of app users. Less overtly intrusive alternatives include the co-option of Bluetooth signals to log a user's proximity to other cellphones. (Bluetooth technology has form in tracking cell-phones' locations.)) On 10 April 2020, Google and Apple jointly announced that they would integrate functionality to support such Bluetooth-based apps directly into their Android and iOS operating systems. India's COVID-19 tracking app Aarogya Setu became the world's fastest growing application—beating Pokémon Go—with 50 million users in the first 13 days of its release. == Rationale == Contact tracing is an important tool in infectious disease control, but as the number of cases rises time constraints make it more challenging to effectively control transmission. Digital contact tracing, especially if widely deployed, may be more effective than traditional methods of contact tracing. In a March 2020 model by the University of Oxford Big Data Institute's Christophe Fraser's team, a coronavirus outbreak in a city of one million people is halted if 80% of all smartphone users take part in a tracking system; in the model, the elderly are still expected to self-isolate en masse, but individuals who are neither symptomatic nor elderly are exempt from isolation unless they receive an alert that they are at risk of carrying the disease. Some proponents advocate for legislation exempting certain COVID-19 apps from general privacy restrictions. == Issues == === Uptake === Ross Anderson, professor of security engineering at Cambridge University, listed a number of potential practical problems with app-based systems, including false positives and the potential lack of effectiveness if takeup of the app is limited to only a small fraction of the population. In Singapore, only one person in three had downloaded the TraceTogether app by the end of June 2020, despite legal requirements for most workers; the app was also underused, as it required users to keep it open at all times on iOS. A team at the University of Oxford simulated the effect of a contact tracing app on a city of 1 million. They estimated that if the app was used in conjunction with the shielding of over-70s, then 56% of the population would have to be using the app for it to suppress the virus. This would be equivalent to 80% of smartphone users in the United Kingdom. They found that the app could still slow the spread of the virus if fewer people downloaded it, with one infection being prevented for every one or two users. In August 2020, the American Civil Liberties Union (ACLU) argued that there were disparities in smartphone use between demographics and minority groups, and that "even the most comprehensive, all-seeing contact tracing system is of little use without social and medical systems in place to help those who may have the virus — including access to medical care, testing, and support for those who are quarantined." === App store restrictions === Addressing concerns about the spread of misleading or harmful apps, Apple, Google and Amazon set limits on which types of organizations could add coronavirus-related apps to its App Store, limiting them to only "official" or otherwise reputable organizations. === Ethical principles of mass surveillance using COVID-19 contact tracing apps === The advent of COVID-19 contact tracing apps has led to concerns around privacy, the rights of app users, and governmental authority. The European Convention on Human Rights, the International Covenant on Civil and Political Rights (ICCPR) and the United Nations and the Siracusa Principles have outlined 4 principles to consider when looking at the ethical principles of mass surveillance with COVID-19 contact tracing apps. These are necessity, proportionality, scientific validity, and time boundedness. Necessity is defined as the idea that governments should only interfere with a person's rights when deemed essential for public health interests. The potential risks associated with infringements of personal privacy must be outweighed by the possibility of reducing significant harm to others. Potential benefits of contact-tracing apps that may be considered include allowing for blanket population-level quarantine measures to be lifted sooner and the minimization of people under quarantine. Hence, some contend that contact-tracing apps are justified as they may be less intrusive than blanket quarantine measures. Furthermore, the delay of an effective contact-tracing app with significant health and economic benefits may be considered unethical. Proportionality refers to the concept that a contact tracing app's potential negative impact on a person's rights should be justifiable by the severity of the health risks that are being addressed. Apps must use the most privacy-preserving options available to achieve their goals, and the selected option should not only be a logical option for achieving the goal but also an effective one. Scientific validity evaluates whether an app is effective, timely and accurate. Traditional manual contact-tracing procedures are not efficient enough for the COVID-19 pandemic, and do not consider asymptomatic transmission. Contact-tracing apps, on the other hand, can be effective COVID-19 contact-tracing tools that reduce R value to less than 1, leading to sustained epidemic suppression. However, for apps to be effective, there needs to be a minimum 56-60% uptake in the population. Apps should be continually modified to reflect current knowledge on the diseases being monitored. Some argue that contact-tracing apps should be considered societal experimental trials where results and adverse effects are evaluated according to the stringent guidelines of social experiments. Analyses should be conducted by independent research bodies and published for wide dissemination. Despite the current urgency of our pandemic situation, we should still adhere to the standard rigors of scientific evaluation. Time boundedness describe the need for establishing legal and technical sunset clauses so that they are only allowed to operate as long as necessary to address the pandemic situation. Apps should be withdrawn as soon as possible after the end of the pandemic. If the end of the pandemic cannot be predicted, the use of apps should be regularly reviewed and decisions about continued use should be made at each review. Collected data should only be retained by public health authorities for research purposes with clear stipulations on how long the data will be held for and who will be responsible for security, oversight, and ownership. === Privacy, discrimination and marginalisation concerns === The American Civil Liberties Union (ACLU) has published a set of principles for technology-assisted contact tracing and Amnesty International and over 100 other organizations issued a statement calling for limits on this kind of surveillance. The organisations declared eight conditions on governmental projects: surveillance would have to be "lawful, necessary and proportionate"; extensions of monitoring and surveillance would have to have sunset clauses; the use of data would have to be limited to COVID-19 purposes; data security and anonymity would have to be protected and shown to be protected based on evidence; digital surveillance would have to address the risk of exacerbating discrimination and marginalisation; any sharing of data with third parties would have to be defined in law; there would have to be safeguards against abuse and the rights of citizens to respond to abuses; "meaningful participation" by all "relevant stakeholders" would be required, including that of public health experts and marginalised groups. The German Chaos Computer Club (CCC) and Reporters Without Borders also issued checklists. The Exposure Notification service intends to address the problem of persistent surveillance by removing the tracing mechanism from their device operating systems once it is no longer needed. On 20 April 2020, it was reported that over 300 academics had signed a statement favouring decentralised proximity tracing applications over centralised models, given the difficulty in precluding centralised options being used "to enable unwarranted discrimination and surveillance." In a centralised model, a central database records the ID codes of meetings between users. In a decentralised model, this information is recorded on individual phones, with the role of the central
Brand networking
Brand networking is the engagement of a social networking service around a brand by providing consumers with a platform of relevant content, elements of participation, and a currency, score, or ranking. Brand networking creates communities that serve as interactive destinations to encourage brand participation online and off. This evolved level of user participation with the brand facilitates strong relationships with consumers, leverages sales, and generates fan equity. The concept builds on the marketing literature on brand communities, which describes specialized, non-geographically bound groups of consumers organized around shared interest in a brand, and on subsequent research on social-media-based brand communities that examines how such groups operate when embedded in general-purpose networking platforms. == History == The development and growth of social networking in the early 2000s gave birth to brand networking. Brands saw the immediate potential to reach and interact with consumers through online platforms like Facebook and MySpace. At first, the ability to reach consumers through these platforms was inadequate; brands had the option to join as members or simply advertise on these sites. The potential existed to not only display advertisements to consumers, but to encourage them to interact with the brand. This is when brands made the shift to create their own networking platforms. Less evolved attempts to connect brands with consumers via networking are typically built as online platforms meant only to complement a product/service and are limited in functionality. Typically these sites offer consumers the opportunity to interact through discussion boards and group pages. The Guiding Light Community was built to complement the popular CBS television soap opera. The site offers members reward points for contributing content to discussion boards and blogs (which is all geared toward the show). == Structure == Brand networking is more than the utilization of a social networking platform; it is connecting consumers together and constructing relationships directly with the brand. Three key elements, in unity, create effective brand networking: relevant content, elements of participation, and a competitive currency. Websites in conjunction with other media types (television, radio, print) present content around a vertical industry, sector of interest, or cultural and social issues for a brand. This can be in areas such as health, marketing, or business, or any content relevant to the brand message. Such content is not only provided by the brand but also in the form of consumer-generated media. Research on brand-related user-generated content across major platforms suggests that the form and tone of consumer contributions vary by platform, with promotional content more common on some networks and response-oriented content on others. A brand provides participation with consumers online and offline. This is accomplished through the combination of typical social networking features online, such as personalised pages, friend lists, groups, and messaging, alongside elements of involvement offline. This is not simply connecting an online platform with mobile devices, but providing separate mobile features jointly with a secondary media type to drive online usage and build relationships with the brand on the go. By participating in mobile campaigns, users are interacting with the brand outside of traditional brick and mortar or e-commerce destinations. Empirical work on consumer brand engagement in social media frames such participation along cognitive, affective, and behavioural dimensions. The final element of brand networking involves incentivising participation with the other two elements. The addition of a currency or point system acts as an anchor to the brand and network and creates a competitive dynamic between consumers. These points are distributed for activity carried out outside of the networking site. By incentivising usage offline, the brand image is reinforced for the consumer and strengthens the relationship. Consumers are turned into promoters for both the brand and the users' benefit. The use of points, badges, leaderboards, and similar mechanics is described in the marketing literature as gamification, and has been linked to higher participation rates in mobile and loyalty programmes. == Fan equity == Fan equity is the idea that by locking in consumers to a brand, they are turned into fans of the brand. As fans, they promote, interact, and consume on a daily basis and become assets. Apple Inc. is one example of a company often cited as possessing fan equity. Customers of Apple are extremely brand loyal and are assets to the company. Creating a fan-generated brand is a difficult but effective method of business. Through the use of brand networking, a company is able to build a consumer or fan base that provides a strong relationship between business and consumers. The trust is formed and fans do a lot of work for the brand by word of mouth. Peer-to-peer channels are the strongest means of communication for a brand, but also one in which the brand can only influence and not control. Subsequent research links community engagement with brand trust, identifying community engagement as a mediator between social-media brand community participation and trust. This method of business is argued to be a relationship handled by the brand generally for its own gain. Many fans do not realise the work they are doing for companies by using their product or service. Facebook is a fan-based brand that has become a global phenomenon through customer use, with social media features such as sharing and commenting. With the growth of social media, marketing and advertising through social media has continued to expand. Brands can display and promote their products or services at a fast rate, with consumers sharing and contributing to the brand on a global scale. This can also be seen as online word of mouth exposure that can produce positive or negative feedback for brands. Once consumers become fans they are typically loyal, which can create positive word of mouth for a brand. Fans become a valuable asset, boosting the status and reputation of a brand. Different perceptions of brands can be linked to a person's origin or religion, which creates a difficulty when trying to enter a market or gain market share. Businesses need to be aware of the types of products or services they introduce to a specific market, ensuring they are culturally sensitive. Fan pages are created on social media to maintain the relationship between brands and consumers. By engaging and interacting with consumers, brands obtain fans and produce positive imaging. Some fans become attached to brands and are often encouraged to remain as fans through the use of celebrities endorsing the brand. Research on parasocial interaction in social-media environments suggests that one-sided emotional bonds that consumers form with endorsers and brand personae help convert ordinary followers into engaged fans.
Mortimer Rogoff
Mortimer Alan Rogoff (May 2, 1921 – August 1, 2008) was an American inventor, businessman, and author as well as an amateur photographer and radio operator. He is recognized for his work in spread spectrum technology which is the technology that modern cell phones and GPS systems are based on. He is also considered the grandfather of the electronic navigation chart. == Early life == Rogoff was born in Brooklyn, New York. He earned his B.S.E.E. from Rensselaer Polytechnic Institute in 1943 and his M.S.E.E. from Columbia University in 1948. While at Rensselaer he was a member of Kappa Nu fraternity and the Features Editor for the student newspaper. During World War II, he enlisted in the United States Navy and worked on developing radio communication and aerial navigation systems. One of the techniques he developed was undetectable by Axis forces because its power was below that of the background noise and its frequency varied in random ways. This secure transmission was the beginning of spread spectrum technology which would become the basis for GPS and CDMA cellular telephone systems. Although he was never able to patent the technology because it was a military secret he did get some recognition for it almost forty years later when he received the Institute of Electrical and Electronics Engineers’ Pioneer Award in 1981. == Career == Rogoff worked for twenty-two years (1946 to 1968) for ITT Laboratories in New Jersey. In 1958, he became their deputy director of Engineering. He was Vice President of ITT Laboratories from 1962 to 1963. From 1963 to 1968, he was promoted to the corporate staff where he became head of European operations. In 1968 he left ITT to work for the Diebold Group where he became an Executive Vice President. After leaving the Diebold Group he founded several technology and automation businesses, including his own consulting firm, and Teletext Communications Corporation. Later in the 1970s, he was a Principal with Booz Allen Hamilton. In 1979, his book ‘’Calculator Navigation’’ was published. This book demonstrated practical methods for calculating precise ship locations using radio navigation with a consumer calculator. In 1981, he founded a new company, Navigation Sciences Inc., in Bethesda, Maryland. With this company he patented a method for marine navigation that combined radar maps with electronic charts in 1986. This was a major advancement in field. Today, this system is known as the Electronic Chart Display and Information System (ECDIS). Rogoff had seen the need for a new charting system in 1968 from his apartment at 180 East End Avenue in New York City. From there, he saw a boating accident where a life was lost and decided there had to be a way to automate navigation. Rogoff then became of member of the International Maritime Organization’s (IMO) sub-committee on Safety of Navigation, a representative to the International Electrotechnical Commission, and became the chairman of the Radio Technical Commission for Maritime Services Special Committee 109 on Electronic Charts. He was able to use his influence on these boards to push through a proposal of ECDIS standards in 1989 where none has been before. As his friend Giuseppe Carnevali said, “Although nobody could argue against the need for a standard, no one was ready to endorse one; however, nobody was brave enough to oppose it.” A Test Bed project on these proposals was conducted by the United States Coast Guard. The amended standards were accepted by the IMO in November, 1995. In 2000, he was named as a Fellow of the Institute of Navigation. He was also a Fellow of the Institute of Electrical and Electronics Engineers. During this time, he was also president of the Navigational Electronic Charts System Association. == Personal == In 1979, he moved to Washington, D.C. and bought a home in Nantucket, Massachusetts. He married Sheila Zunser in 1943 and they were together for sixty-five years. They had three daughters: Louisa Thompson, Alice Rogoff, and Julia Peach. His sister was sociologist Natalie Rogoff Ramsøy of the University of Oslo. He was a member of the Cosmos Club and President of The Navigational Electronic Chart System Association (NECSA). He was a very good amateur photographer and liked amateur radio (call sign W2EE). He died in Nantucket from bladder cancer. == Patents == Patent number: 4176316 – Secure Communication System – November 27, 1979 With Louis A. DeRosa Patent number: 4590569 – Electronic Navigation System – May 20, 1986 With Peter M. Winkler and John N. Ackley Patent number: RE34004 – Secure Communication System – July 21, 1992 With Louis A. DeRosa == Publications == Rogoff, Mortimer September 1957. Automatic Analysis of Infrared Spectra. Annals of the New York Academy of Sciences; vol. 69: no. 1: 27–37. Gen. P.C. Sandretto and Mortimer Rogoff. 1958 “A Novel Concept for Application to the Control of Airways Traffic.” NAVIGATION: Journal of The Institute of Navigation; vol. 6: no. 2: 102–107 Rogoff, Mortimer 1979. Calculator Navigation; ISBN 0-393-03192-6. Published by W.W. Norton & Company (New York and London). Rogoff, Mortimer December 1985. Electronic Charting. Yachting; vol. 158: no. 6: 54–57. Rogoff, Mortimer Winter 1990. Electronic Charts in the Nineties. NAVIGATION: Journal of The Institute of Navigation; vol. 37: no. 4: 305–318.
Digital asset
A digital asset is anything that exists only in digital form and comes with a distinct usage right or distinct permission for use. Data that do not possess those rights are not considered assets. Digital assets include, but are not limited to: digital documents, audio content, motion pictures, and other relevant digital data currently in circulation or stored on digital appliances, such as personal computers, laptops, portable media players, tablets, data storage devices, and telecommunication devices. This encompasses any apparatus that currently exists or will exist as technology progresses to accommodate the conception of new modalities capable of carrying digital assets. This holds true regardless of the ownership of the physical device on which the digital asset is located. == Types == Types of digital assets include, but are not limited to: software, photography, logos, illustrations, animations, audiovisual media, presentations, spreadsheets, digital paintings, word documents, electronic mails, websites, and various other digital formats with their respective metadata. The number of different types of digital assets is exponentially increasing due to the rising number of devices that leverage these assets, such as smartphones, serving as conduits for digital media. In Intel's presentation at the 'Intel Developer Forum 2013,' they introduced several new types of digital assets related to medicine, education, voting, friendships, conversations, and reputation, among others. == Digital asset management system == A digital asset management (DAM) is an integrated structure that combines software, hardware, and/or other services to manage, store, ingest, organize, and retrieve digital assets. These systems enable users to find and use content when needed. == Digital asset metadata == Metadata is data about other data. Any structured information that defines a specification of any form of data is referred to as metadata. Metadata is also a claimed relationship between two entities, often used to establish connections or associations. Librarian Lorcan Dempsey says "Think of metadata as data which removes from a user (human or machine) the need to have full advance knowledge of the existence or characteristics of things of potential interest in the environment". At first, the term metadata was used for digital data exclusively, but nowadays metadata can apply to both physical and digital data. Catalogs, inventories, registers, and other similar standardized forms of organizing, managing, and retrieving resources contain metadata. Metadata can be stored and contained directly within the file it refers to or independently from it with the help of other forms of data management such as a DAM system. The more metadata is assigned to an asset the easier it gets to categorize it, especially as the amount of information grows. The asset's value rises the more metadata it has for it becomes more accessible, easier to manage, and more complex. Structured metadata can be shared with open protocols like OAI-PMH to allow further aggregation and processing. Open data sources like institutional repositories have thus been aggregated to form large datasets and academic search engines comprising tens of millions of open access works, like BASE, CORE, and Unpaywall. == Issues == Due to a lack of either legislation or legal precedent, there is limited existing governmental control and regulation surrounding digital assets in the United States and other large economies globally. Many of the control issues relating to access and transferability are maintained by individual companies. Some consequences of this include 'What is to become of the assets once their owner is deceased?' as well as can, and, if so, how, may they be inherited. This subject was broached in a bogus story about Bruce Willis allegedly looking to sue Apple as the end user agreement prevented him from bequeathing his iTunes collection to his children. Another case of this was when a soldier died on duty and the family requested access to the Yahoo! account. When Yahoo! refused to grant access, the probate judge ordered them to give the emails to the family but Yahoo! still was not required to give access. The Music Modernization Act was passed in September 2018 by the U.S. Congress to create a new music licensing system, with the aim to help songwriters get paid more.
Mistral Vibe
Mistral Vibe or Vibe (Le Chat until May 2026), is a chatbot that uses generative artificial intelligence developed in France by Mistral AI. Mistral Vibe is available in iOS and Android. Its services are operated on a freemium model. == History == In February 2024, Mistral AI released Le Chat. In January 2025, Mistral AI made a content deal with Agence France-Presse (AFP) that lets Le Chat query AFP's entire archive dating back to 1983. On 6 February 2025, a mobile app for Le Chat was released for iOS and Android, and a subscription tier, Pro, was introduced at a cost of $14.99 per month. In July 2025, Mistral AI released Voxtral, an open-source language model that understands and generates audio. Mistral introduced a voice mode for chatting that uses Voxtral, and projects, which allows grouping chats and files. In September 2025, Le Chat introduced the capability to remember previous conversations. In May 2026, Mistral AI announced the rebrand from Le Chat to Mistral Vibe and new features were introduced at the same time.
Problematic social media use
Excessive use of social media can lead to problems including impaired functioning and a reduction in overall wellbeing, for both users and those around them. Such usage is associated with a risk of mental health problems, sleep problems, academic struggles, and daytime fatigue. Psychological or behavioural dependence on social media platforms can result in significant negative functions in peoples daily lives. The risk of problems is also related to the type of platform of social media or online community being used. People of different ages and genders may be affected in different ways by problematic social media use. == Signs and symptoms == Signs of social media addiction or excessive use of social media include many behaviours similar to substance use disorders, including mood modification, salience, tolerance, stress withdrawal symptoms, psychological distress, anxiety and depression, conflict, and relapse, and low self esteem. People with problematic social media habits are at risk of being addicted and may require more time on social media as time passes. Frequent social media use may also be associated with self-reported symptoms of attention deficit hyperactivity disorder. Social anxiety (or fear of missing out) is another potential symptom. Social anxiety is defined as having intense anxiety or fear of being judged, negatively evaluated, or rejected in a social or performance situation. The fear of missing out can contribute to excessive usage due to frequent checking the media constantly throughout the day to check in and see what others are doing instead of doing other activities. Common signs include displacement, or replacing meaningful other activities with social media, and loneliness. == Causes and mechanisms == There are many theories for the mechanism or cause behind a person having problematic social media use. The transition from normal to problematic social media use occurs when a person relies on it to relieve stress, loneliness, depression, or provide continuous rewards. Cognitive-behavioral model – People increase their use of social media when they are in unfamiliar environments or awkward situations; Social skill model – People pull out their phones and use social media when they prefer virtual communication as opposed to face-to-face interactions because they lack self-presentation skills; Socio-cognitive model – This person uses social media because they love the feeling of people liking and commenting on their photos and tagging them in pictures. They are attracted to the positive outcomes they receive on social media. There are parallels to the gambling industry inherent to the design of various social media sites, with "'ludic loops' or repeated cycles of uncertainty, anticipation and feedback" potentially contributing to problematic social media use. Another factor directly facilitating the development of addiction to social media is the implicit attitude toward the IT artifact. Social media use may also stimulate the reward pathway in the brain. There is also a theory that social media addiction fulfills a basic evolutionary drives in the wake of mass urbanization worldwide. The basic psychological needs of "secure, predictable community life that evolved over millions of years" remain unchanged, leading some to find online communities to cope with the new individualized way of life in some modern societies. The "Evolutionary Mismatch" hypothesis holds that modern digital platforms amplify social competition and comparison in ways our ancestors never faced, possibly triggering maladaptive patterns such as anxiety, depression, or compulsive use. Similarly, some scholars compare social media to "junk food": The approach taken to develop social media platforms may contribute to problematic social media use. The ability to scroll and stream content endlessly and how app developers distort time by affecting the 'flow' of content when scrolling, potentially resulting in the Zeigarnik effect (the human brain will continue to pursue an unfinished task until a satisfying closure. Autoplay modes, the personalized nature of the content results in emotional attachment (the user values this above its actual value, which is referred to as the endowment effect), and the exposure effect (repeated exposure to a distinct stimulus by the user can condition the user into an enhanced or improved attitude toward it). The interactive nature of the platforms, including the ability to "like" content has also been linked. Even though social media can satisfy personal communication needs, those who use it at higher rates are shown to have higher levels of psychological distress. == Diagnosis == While there is no official diagnostic term or measurement, problematic social media use is conceptualized as a non-substance-related disorder, resulting in preoccupation and compulsion to engage excessively in social media platforms despite negative consequences. No diagnosis exists for problematic social media use in either the ICD-11 or DSM-5. Excessive use of an activity, like social media, does not directly equate with addiction. There are other factors that could lead to someone's social media addiction including personality traits and pre-existing tendencies. While the extent of social media use and addiction are positively correlated, it is erroneous to employ use (the degree to which one makes use of the site's features, the effort exerted during use sessions, access frequency, etc.) as a proxy for addiction. Indicators of a potential dependence on social media include: Mood swings: a person uses social media to regulate his or her mood, or as a means of escaping real world conflicts. Relevance: social media starts to dominate a person's thoughts at the expense of other activities. Salience: social media becomes the most important part of someone's life. Tolerance: a person increases their time spent on social media to experience previously associated feelings they had while using social media. Withdrawal: when a person can not access social media their sleeping or eating habits change or signs of depression or anxiety can become present. Conflicts in real life: when social media is used excessively, it can affect real-life relationships with family and friends. Relapse: the tendency for previously affected individuals to revert to previous patterns of excessive social media use. There have been several scales developed and validated that help to understand the issues regarding problematic social media use. There is not one single scale that is being used by all researchers. == Treatment == Screen time recommendations for children and families have been developed by the American Academy of Pediatrics. Possible therapeutic interventions published include: Self-help interventions, including application-specific timers; Cognitive behavioural therapy; and Organisational and schooling support. Medications have not been shown to be effective in randomized, controlled trials for the related conditions of Internet addiction disorder or gaming disorder. == Prevention == Prevention approaches include screen time monitoring apps and other tech-based approaches to improve efficiency and decrease screen time and tools to help with addiction to online platform products. Parents' methods for monitoring, regulating, and understanding their children's social media use are referred to as parental mediation. Parental mediation strategies include active, restrictive, and co-using methods. Active mediation involves direct parent-child conversations that are intended to educate children on social media norms and safety, as well as the variety and purposes of online content. Restrictive mediation entails the implementation of rules, expectations, and limitations regarding children's social media use and interactions. Co-use is when parents jointly use social media alongside their children, and is most effective when parents are actively participating (like asking questions, making inquisitive/supportive comments) versus being passive about it. Active mediation is the most common strategy used by parents, though the key to success for any mediation strategy is consistency/reliability. When parents reinforce rules inconsistently, have no mediation strategy, or use highly restrictive strategies for monitoring their children's social media use, there is an observable increase in children's aggressive behaviours. When parents openly express that they are supportive of their child's autonomy and provide clear, consistent rules for media use, problematic usage and aggression decreases. Knowing that consistent, autonomy-supportive mediation has more positive outcomes than inconsistent, controlling mediation, parents can consciously foster more direct, involved, and genuine dialogue with their children. This can help prevent or reduce problematic social media use in children and teenagers. == Outcomes == === Adolescents and teens === Increased social medi