Latent semantic analysis (LSA) is a technique in natural language processing, in particular distributional semantics, of analyzing relationships between a set of documents and the terms they contain by producing a set of concepts related to the documents and terms. LSA assumes that words that are close in meaning will occur in similar pieces of text (the distributional hypothesis). A matrix containing word counts per document (rows represent unique words and columns represent each document) is constructed from a large piece of text and a mathematical technique called singular value decomposition (SVD) is used to reduce the number of rows while preserving the similarity structure among columns. Documents are then compared by cosine similarity between any two columns. Values close to 1 represent very similar documents while values close to 0 represent very dissimilar documents. An information retrieval technique using latent semantic structure was patented in 1988 by Scott Deerwester, Susan Dumais, George Furnas, Richard Harshman, Thomas Landauer, Karen Lochbaum and Lynn Streeter. In the context of its application to information retrieval, it is sometimes called latent semantic indexing (LSI). == Overview == === Occurrence matrix === LSA can use a document-term matrix which describes the occurrences of terms in documents; it is a sparse matrix whose rows correspond to terms and whose columns correspond to documents. A typical example of the weighting of the elements of the matrix is tf-idf (term frequency–inverse document frequency): the weight of an element of the matrix is proportional to the number of times the terms appear in each document, where rare terms are upweighted to reflect their relative importance. This matrix is also common to standard semantic models, though it is not necessarily explicitly expressed as a matrix, since the mathematical properties of matrices are not always used. === Rank lowering === After the construction of the occurrence matrix, LSA finds a low-rank approximation to the term-document matrix. There could be various reasons for these approximations: The original term-document matrix is presumed too large for the computing resources; in this case, the approximated low rank matrix is interpreted as an approximation (a "least and necessary evil"). The original term-document matrix is presumed noisy: for example, anecdotal instances of terms are to be eliminated. From this point of view, the approximated matrix is interpreted as a de-noisified matrix (a better matrix than the original). The original term-document matrix is presumed overly sparse relative to the "true" term-document matrix. That is, the original matrix lists only the words actually in each document, whereas we might be interested in all words related to each document—generally a much larger set due to synonymy. The consequence of the rank lowering is that some dimensions are combined and depend on more than one term: {(car), (truck), (flower)} → {(1.3452 car + 0.2828 truck), (flower)} This mitigates the problem of identifying synonymy, as the rank lowering is expected to merge the dimensions associated with terms that have similar meanings. It also partially mitigates the problem with polysemy, since components of polysemous words that point in the "right" direction are added to the components of words that share a similar meaning. Conversely, components that point in other directions tend to either simply cancel out, or, at worst, to be smaller than components in the directions corresponding to the intended sense. === Derivation === Let X {\displaystyle X} be a matrix where element ( i , j ) {\displaystyle (i,j)} describes the occurrence of term i {\displaystyle i} in document j {\displaystyle j} (this can be, for example, the frequency). X {\displaystyle X} will look like this: d j ↓ t i T → [ x 1 , 1 … x 1 , j … x 1 , n ⋮ ⋱ ⋮ ⋱ ⋮ x i , 1 … x i , j … x i , n ⋮ ⋱ ⋮ ⋱ ⋮ x m , 1 … x m , j … x m , n ] {\displaystyle {\begin{matrix}&{\textbf {d}}_{j}\\&\downarrow \\{\textbf {t}}_{i}^{T}\rightarrow &{\begin{bmatrix}x_{1,1}&\dots &x_{1,j}&\dots &x_{1,n}\\\vdots &\ddots &\vdots &\ddots &\vdots \\x_{i,1}&\dots &x_{i,j}&\dots &x_{i,n}\\\vdots &\ddots &\vdots &\ddots &\vdots \\x_{m,1}&\dots &x_{m,j}&\dots &x_{m,n}\\\end{bmatrix}}\end{matrix}}} Now a row in this matrix will be a vector corresponding to a term, giving its relation to each document: t i T = [ x i , 1 … x i , j … x i , n ] {\displaystyle {\textbf {t}}_{i}^{T}={\begin{bmatrix}x_{i,1}&\dots &x_{i,j}&\dots &x_{i,n}\end{bmatrix}}} Likewise, a column in this matrix will be a vector corresponding to a document, giving its relation to each term: d j = [ x 1 , j ⋮ x i , j ⋮ x m , j ] {\displaystyle {\textbf {d}}_{j}={\begin{bmatrix}x_{1,j}\\\vdots \\x_{i,j}\\\vdots \\x_{m,j}\\\end{bmatrix}}} Now the dot product t i T t p {\displaystyle {\textbf {t}}_{i}^{T}{\textbf {t}}_{p}} between two term vectors gives the correlation between the terms over the set of documents. The matrix product X X T {\displaystyle XX^{T}} contains all these dot products. Element ( i , p ) {\displaystyle (i,p)} (which is equal to element ( p , i ) {\displaystyle (p,i)} ) contains the dot product t i T t p {\displaystyle {\textbf {t}}_{i}^{T}{\textbf {t}}_{p}} ( = t p T t i {\displaystyle ={\textbf {t}}_{p}^{T}{\textbf {t}}_{i}} ). Likewise, the matrix X T X {\displaystyle X^{T}X} contains the dot products between all the document vectors, giving their correlation over the terms: d j T d q = d q T d j {\displaystyle {\textbf {d}}_{j}^{T}{\textbf {d}}_{q}={\textbf {d}}_{q}^{T}{\textbf {d}}_{j}} . Now, from the theory of linear algebra, there exists a decomposition of X {\displaystyle X} such that U {\displaystyle U} and V {\displaystyle V} are orthogonal matrices and Σ {\displaystyle \Sigma } is a diagonal matrix. This is called a singular value decomposition (SVD): X = U Σ V T {\displaystyle {\begin{matrix}X=U\Sigma V^{T}\end{matrix}}} The matrix products giving us the term and document correlations then become X X T = ( U Σ V T ) ( U Σ V T ) T = ( U Σ V T ) ( V T T Σ T U T ) = U Σ V T V Σ T U T = U Σ Σ T U T X T X = ( U Σ V T ) T ( U Σ V T ) = ( V T T Σ T U T ) ( U Σ V T ) = V Σ T U T U Σ V T = V Σ T Σ V T {\displaystyle {\begin{matrix}XX^{T}&=&(U\Sigma V^{T})(U\Sigma V^{T})^{T}=(U\Sigma V^{T})(V^{T^{T}}\Sigma ^{T}U^{T})=U\Sigma V^{T}V\Sigma ^{T}U^{T}=U\Sigma \Sigma ^{T}U^{T}\\X^{T}X&=&(U\Sigma V^{T})^{T}(U\Sigma V^{T})=(V^{T^{T}}\Sigma ^{T}U^{T})(U\Sigma V^{T})=V\Sigma ^{T}U^{T}U\Sigma V^{T}=V\Sigma ^{T}\Sigma V^{T}\end{matrix}}} Since Σ Σ T {\displaystyle \Sigma \Sigma ^{T}} and Σ T Σ {\displaystyle \Sigma ^{T}\Sigma } are diagonal we see that U {\displaystyle U} must contain the eigenvectors of X X T {\displaystyle XX^{T}} , while V {\displaystyle V} must be the eigenvectors of X T X {\displaystyle X^{T}X} . Both products have the same non-zero eigenvalues, given by the non-zero entries of Σ Σ T {\displaystyle \Sigma \Sigma ^{T}} , or equally, by the non-zero entries of Σ T Σ {\displaystyle \Sigma ^{T}\Sigma } . Now the decomposition looks like this: X U Σ V T ( d j ) ( d ^ j ) ↓ ↓ ( t i T ) → [ x 1 , 1 … x 1 , j … x 1 , n ⋮ ⋱ ⋮ ⋱ ⋮ x i , 1 … x i , j … x i , n ⋮ ⋱ ⋮ ⋱ ⋮ x m , 1 … x m , j … x m , n ] = ( t ^ i T ) → [ [ u 1 ] … [ u l ] ] ⋅ [ σ 1 … 0 ⋮ ⋱ ⋮ 0 … σ l ] ⋅ [ [ v 1 ] ⋮ [ v l ] ] {\displaystyle {\begin{matrix}&X&&&U&&\Sigma &&V^{T}\\&({\textbf {d}}_{j})&&&&&&&({\hat {\textbf {d}}}_{j})\\&\downarrow &&&&&&&\downarrow \\({\textbf {t}}_{i}^{T})\rightarrow &{\begin{bmatrix}x_{1,1}&\dots &x_{1,j}&\dots &x_{1,n}\\\vdots &\ddots &\vdots &\ddots &\vdots \\x_{i,1}&\dots &x_{i,j}&\dots &x_{i,n}\\\vdots &\ddots &\vdots &\ddots &\vdots \\x_{m,1}&\dots &x_{m,j}&\dots &x_{m,n}\\\end{bmatrix}}&=&({\hat {\textbf {t}}}_{i}^{T})\rightarrow &{\begin{bmatrix}{\begin{bmatrix}\,\\\,\\{\textbf {u}}_{1}\\\,\\\,\end{bmatrix}}\dots {\begin{bmatrix}\,\\\,\\{\textbf {u}}_{l}\\\,\\\,\end{bmatrix}}\end{bmatrix}}&\cdot &{\begin{bmatrix}\sigma _{1}&\dots &0\\\vdots &\ddots &\vdots \\0&\dots &\sigma _{l}\\\end{bmatrix}}&\cdot &{\begin{bmatrix}{\begin{bmatrix}&&{\textbf {v}}_{1}&&\end{bmatrix}}\\\vdots \\{\begin{bmatrix}&&{\textbf {v}}_{l}&&\end{bmatrix}}\end{bmatrix}}\end{matrix}}} The values σ 1 , … , σ l {\displaystyle \sigma _{1},\dots ,\sigma _{l}} are called the singular values, and u 1 , … , u l {\displaystyle u_{1},\dots ,u_{l}} and v 1 , … , v l {\displaystyle v_{1},\dots ,v_{l}} the left and right singular vectors. Notice the only part of U {\displaystyle U} that contributes to t i {\displaystyle {\textbf {t}}_{i}} is the i 'th {\displaystyle i{\textrm {'th}}} row. Let this row vector be called t ^ i T {\displaystyle {\hat {\textrm {t}}}_{i}^{T}} . Likewise, the only part of V T {\displaystyle V^{T}} that contributes to d j {\displaystyle {\textbf {d}}_{j}} is the j 'th {\displaystyle j{\textrm {'th}}} column, d ^ j {\displaystyle {\hat {\textrm {d}}}_{j}} . These are not the eigenvectors, but depend on all the eigenvectors. I
Image stitching
Image stitching or photo stitching is the process of combining multiple photographic images with overlapping fields of view to produce a segmented panorama or high-resolution image. Commonly performed through the use of computer software, most approaches to image stitching require nearly exact overlaps between images and identical exposures to produce seamless results, although some stitching algorithms actually benefit from differently exposed images by doing high-dynamic-range imaging in regions of overlap. Some digital cameras can stitch their photos internally. == Applications == Image stitching is widely used in modern applications, such as the following: Document mosaicing Image stabilization feature in camcorders that use frame-rate image alignment High-resolution image mosaics in digital maps and satellite imagery Medical imaging Multiple-image super-resolution imaging Video stitching Object insertion == Process == The image stitching process can be divided into three main components: image registration, calibration, and blending. === Image stitching algorithms === In order to estimate image alignment, algorithms are needed to determine the appropriate mathematical model relating pixel coordinates in one image to pixel coordinates in another. Algorithms that combine direct pixel-to-pixel comparisons with gradient descent (and other optimization techniques) can be used to estimate these parameters. Distinctive features can be found in each image and then efficiently matched to rapidly establish correspondences between pairs of images. When multiple images exist in a panorama, techniques have been developed to compute a globally consistent set of alignments and to efficiently discover which images overlap one another. A final compositing surface onto which to warp or projectively transform and place all of the aligned images is needed, as are algorithms to seamlessly blend the overlapping images, even in the presence of parallax, lens distortion, scene motion, and exposure differences. === Image stitching issues === Since the illumination in two views cannot be guaranteed to be identical, stitching two images could create a visible seam. Other reasons for seams could be the background changing between two images for the same continuous foreground. Other major issues to deal with are the presence of parallax, lens distortion, scene motion, and exposure differences. In a non-ideal real-life case, the intensity varies across the whole scene, and so does the contrast and intensity across frames. Additionally, the aspect ratio of a panorama image needs to be taken into account to create a visually pleasing composite. For panoramic stitching, the ideal set of images will have a reasonable amount of overlap (at least 15–30%) to overcome lens distortion and have enough detectable features. The set of images will have consistent exposure between frames to minimize the probability of seams occurring. === Keypoint detection === Feature detection is necessary to automatically find correspondences between images. Robust correspondences are required in order to estimate the necessary transformation to align an image with the image it is being composited on. Corners, blobs, Harris corners, and differences of Gaussians of Harris corners are good features since they are repeatable and distinct. One of the first operators for interest point detection was developed by Hans Moravec in 1977 for his research involving the automatic navigation of a robot through a clustered environment. Moravec also defined the concept of "points of interest" in an image and concluded these interest points could be used to find matching regions in different images. The Moravec operator is considered to be a corner detector because it defines interest points as points where there are large intensity variations in all directions. This often is the case at corners. However, Moravec was not specifically interested in finding corners, just distinct regions in an image that could be used to register consecutive image frames. Harris and Stephens improved upon Moravec's corner detector by considering the differential of the corner score with respect to direction directly. They needed it as a processing step to build interpretations of a robot's environment based on image sequences. Like Moravec, they needed a method to match corresponding points in consecutive image frames, but were interested in tracking both corners and edges between frames. SIFT and SURF are recent key-point or interest point detector algorithms but a point to note is that SURF is patented and its commercial usage restricted. Once a feature has been detected, a descriptor method like SIFT descriptor can be applied to later match them. === Registration === Image registration involves matching features in a set of images or using direct alignment methods to search for image alignments that minimize the sum of absolute differences between overlapping pixels. When using direct alignment methods one might first calibrate one's images to get better results. Additionally, users may input a rough model of the panorama to help the feature matching stage, so that e.g. only neighboring images are searched for matching features. Since there are smaller group of features for matching, the result of the search is more accurate and execution of the comparison is faster. To estimate a robust model from the data, a common method used is known as RANSAC. The name RANSAC is an abbreviation for "RANdom SAmple Consensus". It is an iterative method for robust parameter estimation to fit mathematical models from sets of observed data points which may contain outliers. The algorithm is non-deterministic in the sense that it produces a reasonable result only with a certain probability, with this probability increasing as more iterations are performed. It being a probabilistic method means that different results will be obtained for every time the algorithm is run. The RANSAC algorithm has found many applications in computer vision, including the simultaneous solving of the correspondence problem and the estimation of the fundamental matrix related to a pair of stereo cameras. The basic assumption of the method is that the data consists of "inliers", i.e., data whose distribution can be explained by some mathematical model, and "outliers" which are data that do not fit the model. Outliers are considered points which come from noise, erroneous measurements, or simply incorrect data. For the problem of homography estimation, RANSAC works by trying to fit several models using some of the point pairs and then checking if the models were able to relate most of the points. The best model – the homography, which produces the highest number of correct matches – is then chosen as the answer for the problem; thus, if the ratio of number of outliers to data points is very low, the RANSAC outputs a decent model fitting the data. === Calibration === Image calibration aims to minimize differences between an ideal lens models and the camera-lens combination that was used, optical defects such as distortions, exposure differences between images, vignetting, camera response and chromatic aberrations. If feature detection methods were used to register images and absolute positions of the features were recorded and saved, stitching software may use the data for geometric optimization of the images in addition to placing the images on the panosphere. Panotools and its various derivative programs use this method. ==== Alignment ==== Alignment may be necessary to transform an image to match the view point of the image it is being composited with. Alignment, in simple terms, is a change in the coordinates system so that it adopts a new coordinate system which outputs image matching the required viewpoint. The types of transformations an image may go through are pure translation, pure rotation, a similarity transform which includes translation, rotation and scaling of the image which needs to be transformed, Affine or projective transform. Projective transformation is the farthest an image can transform (in the set of two dimensional planar transformations), where only visible features that are preserved in the transformed image are straight lines whereas parallelism is maintained in an affine transform. Projective transformation can be mathematically described as x ′ = H ⋅ x , {\displaystyle x'=H\cdot x,} where x {\displaystyle x} is points in the old coordinate system, x ′ {\displaystyle x'} is the corresponding points in the transformed image and H {\displaystyle H} is the homography matrix. Expressing the points x {\displaystyle x} and x ′ {\displaystyle x'} using the camera intrinsics ( K {\displaystyle K} and K ′ {\displaystyle K'} ) and its rotation and translation [ R t ] {\displaystyle [R\,t]} to the real-world coordinates X {\displaystyle X} and < m a t h > x {\displaystyle , we get Using the abo
Social media background check
A social media background check is an investigative technique that involves scrutinizing the social media profiles and activities of individuals, primarily for pre-employment screening and other official verifications. These checks are performed to review people's online behavioral history on social media websites such as Facebook, Twitter, and LinkedIn. Social media background checks have become a common part of recruitment processes, among other verification procedures. == History == In the early 21st century, with the rapid expansion of social media platforms such as Facebook, Twitter, and LinkedIn, employers began to use these channels to gather additional information about prospective employees. Initially, social media background checks were an informal aspect of recruitment, but they have gradually gained formal recognition as a crucial element in candidate screening. Proponents of social media background checks argue that such reviews provide insight into a candidate's professional interests and networks, though the reliability of such assessments remains contested among researchers. == Rise in society == The practice of social media background checks has seen a significant surge in the last decade. This rise can be attributed to the exponential increase in social media users and the growing awareness among organizations regarding the importance of hiring individuals who align with their values and culture. Various platforms provide services explicitly designed to conduct social media background checks efficiently, simplifying the process for businesses. Companies providing social media background check services, such as Ferretly and Certn, have received venture capital funding, reflecting investor interest in the sector. The incorporation of artificial intelligence into conducting AI-powered social media background checks also illustrates its continued popularity and that businesses are looking to ramp up and even automate their use. High-profile cases in which individuals faced employment or admission consequences for past social media posts have raised awareness of social media background checking practices. For example, director James Gunn faced termination from Marvel Studios in 2018 over past offensive tweets, though he was later rehired. Additionally, multiple college admissions officers have acknowledged reviewing applicants' social media profiles, though such practices vary by institution. == Evolution of ethical considerations == Social media background checks are not without controversy, raising significant ethical considerations that have evolved in recent years. Privacy advocates argue that social media background checks raise concerns about data use and discrimination, particularly given the use of personal information that may not reflect job-relevant behavior. Legal scholars debate whether reviewing publicly posted information constitutes a privacy violation under U.S. law. Researchers and critics note that social media profiles often present curated representations of users' lives and may not reflect workplace behavior or professional competence. Moreover, the accuracy of social media background checks has been called into question, with critics pointing out that these checks may not always yield reliable or comprehensive results. Critics also warn about potential misuse of information obtained from social media, including cyberbullying and harassment. A 2023 study by found that approximately 90% of employers incorporate social media into hiring processes, with over half of those surveyed reporting they had rejected candidates based on social media content. This informal approach operates largely outside federal compliance frameworks. Critics argue that without regulation, candidates lack dispute mechanisms available under regulatory frameworks like the Fair Credit Reporting Act (FCRA), which requires compliance when background checks formally influence employment decisions. In a hiring environment where the practice is already performed often on an individual basis, the introduction of systematic, regulated screening practices that meet federal compliance standards can present a better, fairer alternative for both employers and candidates. == Business considerations == From a business perspective, social media background checks can be a valuable tool in protecting an organization's reputation and maintaining a safe and respectful workplace environment. A well-conducted social media background check can identify potential red flags, helping to prevent instances of workplace harassment or other negative behaviors. However, businesses also face potential legal repercussions if social media background checks are conducted improperly, such as non-compliance with the Fair Credit Reporting Act (FCRA) in the United States. Critics argue that over-reliance on social media data may exclude qualified candidates whose professional competence is not reflected in their online presence. The proliferation of social media screening services has prompted legal and industry experts to emphasize the importance of compliance with the Fair Credit Reporting Act and relevant state privacy laws when conducting such checks.
Social media surgery
A social media surgery is a gathering at which volunteer "surgeons" with expertise in using web tools, chiefly social media, offer free advice in using such tools, to representatives ("patients") of non-profit organisations, charities, community groups and activists, with "no boring speeches or jargon". The idea was conceived by Pete Ashton, with Nick Booth of Podnosh Ltd, who ran the first such surgery in Birmingham, England, on 15 October 2008. In July 2009, a spin-off surgery (dubbed the "Social media mob") started in Mosman, Australia, and in January 2010, the first spin-off surgery in Africa was held. On 16 February 2012, it was announced that the Social Media Surgery movement had won "the Prime Minister’s Big Society Award". Prime Minister David Cameron said: This is an excellent initiative - such a simple idea and yet so effective. The popularity of these surgeries and the fact that they have inspired so many others across the country to follow in their footsteps, is testament to its brilliance. Congratulations to Nick and all the volunteers who have shared their time and expertise to help so many local groups make the most of the internet to support their community. A great example of the Big Society in action. The scheme also won the 2013 Adult Learners' Week "BBC Learning Through Technology Award".
NRENum.net
The NRENum.net service is an end-user ENUM service run by TERENA and the participating national research and education networking organisations (NRENs), primarily for academia. NRENum.net is considered as a complementary service and a valid alternative to the Golden ENUM tree. The domain nrenum.net is being populated in order to provide the infrastructure in DNS for storage of E.164 numbers. The NRENum.net service includes the operation of the Tier-0 root Domain Name Server(s) and the delegation of county codes to NRENum.net Registries. NRENum.net is a registered community trademark of TERENA. == Service description == E.164 Telephone Number Mapping (ENUM) is a standard protocol that is the result of work of the Internet Engineering Task Force's Telephone Number Mapping working group. ENUM translates a telephone number into a domain name. This allows users to continue to use the existing phone number formats they are familiar with, while allowing the call to be routed using DNS. This makes ENUM a quick, stable and cheap link between telecommunications systems and the Internet. RFC 3761 discusses the use of the Domain Name System for storage of E.164 numbers. More specifically, how DNS can be used for identifying available services connected to one E.164 number. The RIPE NCC provides DNS operations for e164.arpa (known as Golden ENUM tree) in accordance with the instructions from the Internet Architecture Board. The NRENum.net service is an end-user ENUM service run by TERENA and the participating NRENs primarily for academia. NRENum.net is considered as a complementary service and a valid alternative to the Golden ENUM tree. The domain nrenum.net is being populated in order to provide the infrastructure in DNS for storage of E.164 numbers. The NRENum.net service includes the operation of the Tier-0 root Domain Name Servers and the delegation of county codes to NRENum.net Registries. NRENum.net is a registered community trademark of TERENA. NRENum.net facilitates services such as Voice over IP and videoconferencing. NRENum.net tree refers to the tree structure where: Tier-0 root Domain Name Servers (technically one master and several secondary servers ensuring resilience) are run by the hosting organisations and coordinated by the NRENum.net Operations Team. Tier-1 Domain Name Servers are run by the NRENum.net (national or regional) Registries responsible for the country code(s) delegated. Tier-2 and lower DNS sub-delegations may be implemented, regulated by the national service policies. An NRENum.net Registry is an entity that is authorised by the NRENum.net Operations Team to operate the national or regional Tier-1 Domain Name Server and be responsible for the county code(s) delegated. In many countries there is a National Research and Education Networking organisation (NREN) that acts as the Registry of the country. An NRENum.net Registrar is responsible for the number/block registration in the Tier-1 DNS and a Number Validation Entity is responsible for the validation of the E.164 telephone numbers to be registered. The NREN may at the same time have the role of the NRENum.net Registry, Registrar and Validation Entity for the country code(s) delegated. A Registrant (end user) is an E.164 telephone number holder. Holders of E.164 numbers who want to be listed in the service must contact the appropriate NRENum.net Registrar. Number (block) delegation is the technical process of assigning country codes to national registries, or number blocks under country codes to end users. Number (block) registration is the technical process of configuring DNS and populating it with the appropriate ENUM records (i.e., adding NAPTR records to DNS) via registrars. The ITU-T strictly regulates the number structure of valid E.164 telephone numbers and assigns number blocks to national authorities (telecom regulators) or recently to global entities directly. The national authorities can further delegate the number ranges to local operators within the country or region. A virtual number has either a non-valid E.164 number structure (e.g., longer than 15 digits) or has a valid structure but is not assigned to any national authorities or operators. The number Validation Entity is responsible for checking the numbers to be registered to NRENum.net. == History == The idea for the NRENum.net service was conceived in 2006. NRENum.net became operational in August 2006, and was run by Bernie Höneisen, a staff member of SWITCH, and Kewin Stöckigt, a staff member of AARNet, as a private service, with technical support from SWITCH and the participants in the TERENA Task Force on Enhanced Communication Services (TF-ECS). When that task force completed its activities in 2008, TERENA agreed to take over the coordination of the NRENum.net service. By that time, nine NRENs had joined NRENum.net. The service continued to grow during the next years, and in March 2012 NRENum.net went global when RNP from Brazil joined the service as its 14th partificpant and the first outside Europe. In 2011, the participants decided to migrate the operation of the service's master Domain Name Server to NIIF and the operation of the two secondary DNSs to CARNET and SWITCH. In 2013, Internet2, AARNet and NORDUnet set up additional secondary Domain Name Servers for their regions, thereby completing the global distribution of DNS slaves and bringing the resilience of the NRENum.net infrastructure to a high level. == Governance == TERENA has established a lightweight global governance structure. The Global NRENum.net Governance Committee (GNGC) is the highest-level strategic body responsible for overall NRENum.net service definition, sustainability and long-term strategy. This includes formulating and recommending service governance principles and policies. Its members are nominated by the NRENum.net Registries in the various world regions, and are appointed by TERENA. The GNGC is composed of two members representing Europe, two representing the Asia-Pacific region, and two representing the Americas. The NRENum.net Operations Team is responsible for the day-to-day operations of the Tier-0 root DNSs and the handling of country code delegation requests. It may escalate technical or policy issues to the GNGC for discussion. TERENA is responsible for ensuring the correct and secure operations of the NRENum.net service performed by the NRENum.net Operations Team and governance by the GNGC. TERENA also supports the development of technical improvements to the NRENum.net service and promotes the deployment of NRENum.net worldwide. == Geographical deployment == Thirty-two county codes are delegated in the NRENum.net service. Below these are listed per world region. === Europe === === Asia-Pacific === === North America === +1 United States (Internet2) === Latin America === === Caribbean === === Africa === +262 Réunion, Mayotte (RENATER)
Harmony (software)
Harmony is a Java-based software for creating high-definition music videos with 2D and 3D animations. The application was developed by Digital Chaotics, a company based in San Jose, California and established in 2010 by Ken and Leanna Scott. == History == During a March 1, 2011 interview published by The LIST magazine, Ken explained how he initially got into music and digital entertainment. According to Scott: “I came at it from both the art and the technology side. … I built one of the first digital audio synthesizers as an undergrad project back in 1979. It was a short jump from there to creating visuals with computers, too.” Taking inspiration from Fantasia – which Scott calls, “The greatest music video of all time” – he began writing software code for Harmony in late 2009, finishing the project in mid-2010. However, Scott has also said that the idea for Harmony began much earlier: I read a book in 1978 called Digital Harmony, by John H Whitney, Sr. (Interestingly, he was the father of the president of Digital Productions.) He said that there was a kind of visual art based on motion, and proposed theories about the underlying mathematical structure of visual harmony. So there's the book, combined with my desire to create art with computers-add a taste or two of things commonly used by college students during the 70's - and lots of Pink Floyd. Add it all up, and the seeds for Harmony were planted. My friends in school and at Floating Point Systems listened to me ranting about "making music videos with computers" incessantly. I'm sure it was both maddening and fascinating to see. == Features == Harmony runs on Windows 7 and Windows Vista. Currently, Digital Chaotics does not offer a macOS or Linux platform for the software. However, Harmony can be run on these platforms by running it on Windows in a virtual machine. == Harmony 2 == On November 1, 2011, Digital Chaotics released the 2.0 version of the Harmony software. Unlike the original version, the second release featured three product levels: Harmony 2 Express, Harmony 2 Pro, and Harmony 2 Extreme. The "Express" version was positioned as an entry-level, free release to allow users a chance to "test-drive" the software. The "Pro" version currently retails at $197, while the "Extreme" is priced at $397. These two versions, aimed more towards VJ and Fulldome theater usage, featured additional software capability and features such as higher resolution, more video formatting options, and more camera angles.
Social media use in education
Social media in education is the use of social media to enhance education. Social media are "a group of Internet-based applications...that allow the creation and exchange of user-generated content". It is also known as the read/write web. As time went on and technology evolved, social media has been an integral part of people's lives, including students, scholars, and teachers. However, social media are controversial because, in addition to providing new means of connection, critics claim that they damage self-esteem, shorten attention spans, and increase mental health issues. A 2016 dissertation presented surveys that focused on the impact of social media. It reported that 54.6% of students believed that social media affected their studies positively (38% agree, 16.6% strongly agree). About 40% disagreed, and 4.7% of students strongly disagreed. 53% of female students reported that social media negatively impacted their studies. Among male students, 40% agreed that social media had a negative impact on studies, while 59% disagreed. A 2023 article dives deep into the rewards system of the brain in response to social media. This study compares the social rewards system in our brain to those from social media. From ages 10-12, most are receiving a cell phone, social rewards in the brain start to feel more satisfying. Leading to adulthood, the effects of social rewards are less likely to feel reliant on feedback from peers. Equivalent to a more mature prefrontal cortex, this enables a better management of their emotional reaction to these social rewards, meaning a more balanced and controlled reaction. == History == A survey from Cambridge International of nearly 20,000 teachers and students (ages 12–19) from 100 countries found that 48% of students use a desktop computer in class, 42% uses phones, 33% use interactive whiteboards and 20% use tablets. Desktop computers are more used than tablets. Teachers were abandoning the "no phones at school" rule. A 2024 research survey through Common Sense Education reported 54% of age 8-12 and 69% of ages 13-18 social media is an extensive distraction from homework. === United States === The long-running technology boom accelerated after the millennium. As of 2018, 95% of US teenage students had access to a smartphone and 45% said they were online almost constantly. In the early days of social media, access to technology was a significant issue as many students did not own not compatible devices and school budgets were often insufficient to purchase devices for student use. Despite backlash, Missouri passed a law that prohibited teachers from communicating privately with students over social media in 2011. Supporters were concerned that online communication between underage students and faculty could lead to inappropriate relationships. Some schools adopted a "Bring Your Own Device" (BYOD) policy, allowing students to bring Internet-accessing devices, such as phones or tablets to class. During the pandemic, the federal government offered funds that allowed more schools to purchase devices. Over time, more students acquired phones with social media access. Personal devices increased student satisfaction, but reduced teachers' ability to control device use in their classrooms. A 2018 Pew Research study reported that 95% of teenagers had a phone and used social media consistently. === Canada === The Peel District School Board (PDSB) in Ontario accepted the use of social media in the classroom. In 2013, the PDSB introduced BYOD and unblocked many social media sites. That was later replaced by a policy that dealt specifically with social media. == Uses == === Classroom === In the classroom, social media offers a way to systematically distribute and gather information from students. Teachers can supply documents, and audio/video media to students for immediate or later use. One study on higher education reported that devices and social media: created opportunities for interaction provided occasions for collaboration sped up information access offered more ways to learn situated learning. Frustrations included anti-technology instructors, device challenges, and devices as a distraction. Social media in classrooms can have a negative effect. A Yale University publication reported that students who used laptops in class for non-academic reasons had poorer performance. Students spent most of their time on social media, shopping, and other personal activities. Social media has helped many educators mentor their students more effectively. === Outside of class === Social media offer a venue for video calls, stories, feeds, and game playing that can enhance the learning process. Teachers can utilize social media to communicate with their students. Social media can provide students with resources that they can utilize in essays, projects, and presentations. Students can easily access comments made by teachers and peers and offer feedback to teachers. Social media can offer students the opportunity to collaborate by sharing information without requiring face to face meetings. Social media can allow students to more easily connect with experts, to go beyond course materials. Instructors in a 2010 study reported that online technologies (social media) can help students become comfortable having discussions outside the classroom better than traditional means. Teachers may face some risk when using social media outside the classroom, without appropriate work rules. Studies explores how college students' engagement with social media platforms influences their communication preferences and habits, particularly in relation to using school email for academic purposes. === Professional development === Social media can aid professional development, as teachers become students, enhancing knowledge transfer, skill master, and collaboration. === Non-academic uses === Schools can use social media to make public announcements. Teachers and administrators can communicate other important information to parents and students and to receive feedback from them. Families can keep up with school events and policies. === Ecology education === The potential of using social media in ecological, nature and forest education include: virtual nature groups can help promote good habits in forest tourism and recreation (nature ethics), by entering general rules in the regulations by administrators, e.g. "DO NOT PICK UP PLANTS UNKNOWN TO US", which is to protects rare species from pointless picking. social media activity motivates people to learn about nature in the field, allows them to gain knowledge, dispels popular myths, enables contact with scientists and practitioners, promotes valuable literature, websites, and at the same time reveals distortions and substantive errors in popular news services. contact is not only virtual. Despite financial barriers and distance, Internet users organize nature conventions. Such meetings are an opportunity not only to make friends, but also to learn about nature together and have fun. the possibility of contact between scientists and nature lovers via Facebook has become a source of cooperation in species inventory, e.g. the online campaign of the NATRIX Herpetological Society, which consists not only of collecting reports of observations of the smooth snake by Internet users, but also of drawing attention to the biology and threats to this species. Social media has become a place where ecology education quickly reaches people of different ages and social statuses. The nature groups that have been created, in which nature lovers, biologists, foresters and scientists participate, can have a real impact on the state of knowledge and data collection through citizen science. == Apps and services == Social media can allow students to participate in their field by working with organizations outside the classroom. By offering easier access to peers outside the classroom, students can broaden their perspectives and find support resources. Social media aided learning outside of the classroom through collaboration and innovation. One specific study, "Exploring education-related use of social media," called this "audience connectors". Audience connectors bring students together while studying with WhatsApp and Facebook. This study reported that "60 percent [of students in the study] agreed that technology changes education for the better." While social media can promote a beneficial education platform, downsides exist. Students may become skilled at "lifting material from the internet" rather than enhancing their personal understanding. Another downside is student attention spans decline. A concern raised by the students of this study showed how many use spell-check as a crutch and will see a trend of points taken off when spell-check is not an option. Apps like X allowed teachers to make classroom accounts where students can learn about social media in a controlled context. Teachers can post assignments on th