AI For Business Reddit

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  Image-based modeling and rendering

  In computer graphics and computer vision, image-based modeling and rendering (IBMR) methods rely on a set of two-dimensional images of a scene to generate a three-dimensional model and then render some novel views of this scene. The traditional approach of computer graphics has been used to create a geometric model in 3D and try to reproject it onto a two-dimensional image. Computer vision, conversely, is mostly focused on detecting, grouping, and extracting features (edges, faces, etc.) present in a given picture and then trying to interpret them as three-dimensional clues. Image-based modeling and rendering allows the use of multiple two-dimensional images in order to generate directly novel two-dimensional images, skipping the manual modeling stage. == Light modeling == Instead of considering only the physical model of a solid, IBMR methods usually focus more on light modeling. The fundamental concept behind IBMR is the plenoptic illumination function which is a parametrisation of the light field. The plenoptic function describes the light rays contained in a given volume. It can be represented with seven dimensions: a ray is defined by its position ( x , y , z ) {\displaystyle (x,y,z)} , its orientation ( θ , ϕ ) {\displaystyle (\theta ,\phi )} , its wavelength ( λ ) {\displaystyle (\lambda )} and its time ( t ) {\displaystyle (t)} : P ( x , y , z , θ , ϕ , λ , t ) {\displaystyle P(x,y,z,\theta ,\phi ,\lambda ,t)} . IBMR methods try to approximate the plenoptic function to render a novel set of two-dimensional images from another. Given the high dimensionality of this function, practical methods place constraints on the parameters in order to reduce this number (typically to 2 to 4). == IBMR methods and algorithms == View morphing generates a transition between images Panoramic imaging renders panoramas using image mosaics of individual still images Lumigraph relies on a dense sampling of a scene Space carving generates a 3D model based on a photo-consistency check

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  AS2

  AS2 (Applicability Statement 2) is a specification on how to transport structured business-to-business data securely and reliably over the Internet. Security is achieved by using digital certificates and encryption. == Background == AS2 was created in 2002 by the IETF to replace AS1, which they created in the early 1990s. The adoption of AS2 grew rapidly throughout the early 2000s because major players in the retail and fast-moving consumer goods industries championed AS2. Walmart was the first major retailer to require its suppliers to use the AS2 protocol instead of relying on dial-up modems for ordering goods. Amazon, Target, Lowe's, Bed, Bath, & Beyond and thousands of others followed suit. Many other industries use the AS2 protocol, including healthcare, as AS2 meets legal HIPAA requirements. In some cases, AS2 is a way to bypass expensive value-added networks previously used for data interchange. == Technical overview == AS2 is specified in RFC 4130, and is based on HTTP and S/MIME. It was the second AS protocol developed and uses the same signing, encryption and MDN (as defined by RFC3798) conventions used in the original AS1 protocol introduced in the late 1990s by IETF. In other words: Files are encoded as "attachments" in a standardized S/MIME message (an AS2 message). AS2 does not specify the contents of the files. Usually, the file contents are in a standardized format that is separately agreed upon, such as XML or EDIFACT. AS2 messages are always sent using the HTTP or HTTPS protocol (Secure Sockets Layer — also known as SSL — is implied by HTTPS) and usually use the "POST" method (use of "GET" is rare). Messages can be signed, but do not have to be. Messages can be encrypted, but do not have to be. Messages may request a Message Disposition Notification (MDN) back if all went well, but do not have to request such a message. If the original AS2 message requested an MDN: Upon the receipt of the message and its successful decryption or signature validation (as necessary) a "success" MDN will be sent back to the original sender. This MDN is typically signed but never encrypted (unless temporarily encrypted in transit via HTTPS). Upon the receipt and successful verification of the signature on the MDN, the original sender will "know" that the recipient got their message (this provides the "Non-repudiation" element of AS2). If there are any problems receiving or interpreting the original AS2 message, a "failed" MDN may be sent back. However, part of the AS2 protocol states that the client must treat a lack of an MDN as a failure as well, so some AS2 receivers will not return an MDN in this case. Like any other AS file transfer, AS2 file transfers typically require both sides of the exchange to trade X.509 certificates and specific "trading partner" names before any transfers can take place. AS2 trading partner names can usually be any valid phrase. === MDN options === Unlike AS1 or AS3 file transfers, AS2 file transfers offer several "MDN return" options instead of the traditional options of "yes" or "no". Specifically, the choices are: ==== AS2 w/ "Sync" MDNs ==== Return Synchronous MDN via HTTP(S) ("AS2 Sync") - This popular option allows AS2 MDNs to be returned to AS2 message sender clients over the same HTTP connection they used to send the original message. This "MDN while you wait" capability makes "AS2 Sync" transfers the fastest of any type of AS file transfer, but it also keeps this flavor of MDN requests from being used with large files (which may time out in low-bandwidth situations). ==== AS2 w/ "ASync" MDNs ==== Return Asynchronous MDN via HTTP(S) (a.k.a. "AS2 Async") - This popular option allows AS2 MDNs to be returned to the AS2 message sender's server later over a different HTTP connection. This flavor of MDN request is usually used if large files are involved or if your trading partner's AS2 server has poor Internet service. ==== AS2 w/ "Email" MDNs ==== Return (Asynchronous) MDN via Email - This rarely used option allows AS2 MDNs to be returned to AS2 message senders via email rather than HTTP. Otherwise, it is similar to "AS2 Async (HTTP)". ==== AS2 w/ No MDNs ==== Do not return MDN - This option works like it does in any other AS protocol: the receiver of an AS2 message with this option set simply does not try to return an MDN to the AS2 message sender. ==== Filename preservation ==== AS2 filename preservation feature will be used to communicate the filename to the trading partner. The banking industry relies on filenames being communicated between trading partners. AS2 vendors are currently certifying that implementation of filename communication conforms to the standard and is interoperable. There are two profiles for filename preservation being optionally tested under AS2 testing: Filename preservation without MDN responses Filename preservation with an associated MDN response certification Walmart recommends contacting Drummond Group, LLC for more information on EDIINT AS2, or for a list of interoperable-testing AS2 software providers. == Benefits == For many businesses, the use of AS2 and electronic data interchange (EDI) is not a choice so much as it is a requirement of doing business with a large customer or partner. That said, AS2 is a universal protocol that has benefits, from both business and technology vantage points. === Business case === Cut costs by using the web for EDI file transfers, AS2 reduces the cost of transactions from expensive VANs. Extend EDI to more partners; with lower costs and universal web connectivity, AS2 allows organizations to implement EDI with partners worldwide that have little EDI infrastructure. Save time by eliminating the need to manually process orders. Eliminate errors by turning manual processes into automated processes. Universal solution — AS2 is established and tested, so no one has to re-invent the wheel. === Technological advantages === Leverage the web: if an organization can share data securely via the web, they already have much of the infrastructure for AS2. Unlimited EDI data — there are no practical limitations on transaction sizes via the web, and AS2 includes features for managing large transfers. Payload Agnostic — AS2 can be used to transport any type of document. While EDI X12, EDIFACT and XML are common, any mutually agreed-upon format may be transferred.

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  Social media reach

  Social media reach is a media analytics metric that refers to the number of users who have come across a particular content on a particular social media platform. Social media platforms have their own individual ways of tracking, analyzing and reporting the traffic on each of the individual platforms. As these platforms are a main source of communication between companies and their target audiences, by conducting research, companies are able to utilize analytical information, such as the reach of their posts, to better understand the interactions between the users and their content. There are multiple underlying factors that will determine what shows up on a newsfeed or timeline. Algorithms, for example, are a type of factor that can alter the reach of a post due to the way the algorithm is coded, which can affect who sees a post and when. Other examples of factors that can impede the reach can include the time at which posts are made, as well as how frequent the posts are between one another. In comparison, an impression is the total number of circumstances where content has been shown on a social timeline, meanwhile, engagement looks at how people interact with the content that they see on a social platform such as like, share or retweet. == Reach on Facebook == Facebook has their own analytic platform which allows the user to see how other users are interacting with their posts, with the use of multiple metrics. This is not something the average user uses, but rather a tool that is used by pages or public figures. For example, Facebook pages that represent a business often look at the activity their posts have generated. There are three types of reach that can be looked at on the Facebook analytic platform. === Types of reach === ==== Organic Reach ==== This type of reach regards the number of distinct users that have seen a specific post on their feed. Organic reach, in other words is the number of people who have seen the post being analyzed on their Facebook newsfeed. Data gathered from this type of reach can give intel to those doing the analysis, such as the demographics of those who have seen the post. ==== Paid Reach ==== This type of reach regards the number of times that distinct users have come across sponsored posts, ads or content. In other words, paid reach is the number of times Facebook users have seen a post that has been paid for by a company. Data collected can give insight, to advertisers or marketers for example, on the activity based around the reach of their post. ==== Viral Reach ==== This type of reach regards the number of views by distinct users on posts that have been commented on or shared by their friends on Facebook. In other words, viral reach looks at the number of people who have seen a post after a friend of theirs commented or shared the original post, therefore it showed on their timeline. Viral reach can be looked at in terms of a collective number of times that the post has been on individual user's timelines. Data collected from viral reach can be used in multiple ways, for example, it can be used to analyze the type of content that gets shared or commented on and can be further used to compare to other posts. === Engaged users === This refers to the number of individual users who have clicked and interacted with a post on Facebook. == Reach on Twitter == Twitter gives access to any of their users to analytics of their tweets as well as their followers. Their dashboard is user friendly, which allows anyone to take a look at the analytics behind their Twitter account. This open access is useful for both the average user and companies as it can provide a quick glance or general outlook of who has seen their tweets. The way that Twitter works is slightly different than the way of Facebook in terms of the reach. On Twitter, especially for users with a higher profile, they are not only engaging with the people who follow them, but also with the followers of their own followers. The reach metric on Twitter looks at the quantity of Twitter users who have been engaged, but also the number of users that follow them as well. This metric is useful to see the if the tweets/content being shared on Twitter are contributing to the growth of audience on this platform. == Reach on Instagram == Instagram gives their users access to their reach, in the Instagram Insights section. Instagram insights can be used to learn more about an account's followers and performance. Reach indicates the total number of unique Instagram accounts that have seen your Instagram post or story. You can find this data by looking at each individual post insights. == Uses of reach == The reach can be a useful metric to analyze for marketers and advertisers. Social media is a platform that is used by marketers to directly target their intended audience with ease. These platforms not only allow marketers to get a better understanding of their audience, but also allow advertisers to insert their ads onto the timelines of specific users to later be able to conduct research to see the reach of their posts/content. The basic goal of marketers is to increase their reach as much as possible to impact bigger audiences of their dream customers and, in the end, make more sales. When doing organic social media marketing, using paid methods like ads or doing influencer marketing whether it is paid or free, it allows marketers to track the performance of their strategy and tweak it based on what works and what does not. == Analytics and reach == Social analytics looks at the data collected based on the interactions of users on social media platforms. A lot of information can be gathered which can provide intel based on user activities on social media. When looking into analytics in regard to social media, each company or group has a different goal in mind to engage their audience. At a glance, the three might seem as if they are very similar, however the differences between them are significant. There are many aspects that can be analyzed from the data gathered from social media platforms, depending on what is being observed, the correct metric would then be selected to further analyze. One example of the many metrics that can be used through social analytics is the reach. == Reach formula == To calculate social media reach one can use the following formula: R = I f ¯ {\displaystyle R={\frac {I}{\bar {f}}}} where R {\displaystyle R} — is social media reach, I {\displaystyle I} stands for the number of impressions, f ¯ {\displaystyle {\bar {f}}} is the average frequency of impressions per user. f ¯ {\displaystyle {\bar {f}}} represents the number of events when the ad is shown to a particular user. The average value should be calculated over the time period with stable settings of advertisement campaign. == Commenting For Better Reach == Commenting For Better Reach also known as "CFBR" is a widely used strategy for organically boosting post reach on social media platforms. Algorithms tend to favor posts with substantial likes and comments, granting them broader exposure compared to less engaging content. Primarily seen on LinkedIn, a platform geared toward professional networking and business connections, the use of CFBR signals active engagement aimed at enhancing post visibility. It is important to note that genuine and meaningful comments are key to effective engagement. Spammy or irrelevant comments not only detract from the conversation but may also limit a post's potential reach and impact.

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  ACTS Gigabit Satellite Network

  The ACTS Gigabit Satellite Network was a pioneering, high-speed communications satellite network in the years 1993-2004, created as a prototype system to explore high-speed networking of digital endpoints. The system was jointly sponsored by NASA and ARPA, implemented by BBN Technologies and Motorola, and was inducted into the Space Technology Hall of Fame in April 1997. The Advanced Communications Technology Satellite (ACTS) network was designed to provide fiber-compatible SONET service to remote nodes and networks through a wideband satellite system, and provided long-haul, point-to-point and point-to-multipoint full-duplex SONET services, at rates up to 622 Mbit/s, over NASA's Advanced Communication Technology Satellite (ACTS). The Advanced Communications Technology Satellite itself, built and operated by Lockheed Martin, was launched on STS-51 on September 12, 1993, by the Space Shuttle Discovery, and occupied a geostationary orbit at 100° west longitude. It was the first communication satellite to operate in the 20–30 GHz frequency band (Ka band), with 30 GHz uplink and 20 GHz downlink signals. The satellite incorporated advanced on-board switching and multiple dynamically-hopping spot-beam antennas for selected areas of the United States including Hawaii. Up to 3 uplink and 3 downlink antenna beams could be active simultaneously. The ACTS network ground terminals were transportable Gigabit Earth Stations (GES) with fiber-optic SONET interfaces (OC-3 and OC-12), which also supported the Asynchronous Transfer Mode (ATM) protocol suite. The network control and management functions are distributed in the various Gigabit Earth Stations, with the operator's interface being centralized in a Network Management Terminal (NMT), which could be collocated at a GES, or anywhere in the Internet. The system was operational and used for experiments for 127 months, instead of the originally planned 24–48 months. In all, 53 terminals were built and used by more than 100 experimenters to test ACTS abilities. In Nov. 1997 a record data rate of 520 Mbit/s TCP/IP throughput was achieved using ATM between several ground stations via ACTS. On May 31, 2000 the ACTS experiments program officially came to a close, but the system continued to support experiments until it was deactivated on April 28, 2004.

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  Poop Map

  Poop Map is a social app where users can track on a map where and when they defecate. In addition to logging location and time of each bowel movement, users can also add a photo, "like" other users' logs, and rate each account. The social elements of the app allow for groups of users to create a competitive league. Certain behaviors unlock achievements in-app. == Development == The app was created by app developer Nino Uzelac. It was launched in July 2013. == Popularity == The app charted at number one on the Apple App Store charts in 2021 after going viral on TikTok. As of September 2024, the app has a 4.8 rating on the App Store and more than 58,000 ratings. It also has more than one million downloads on the Google Play Store. Poop Map is notably popular among hikers, and has been written about in the outdoors magazine Outside.

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  Torus interconnect

  A torus interconnect is a switch-less network topology for connecting processing nodes in a parallel computer system. == Introduction == In geometry, a torus is created by revolving a circle about an axis coplanar to the circle. While this is a general definition in geometry, the topological properties of this type of shape describes the network topology in its essence. === Geometry illustration === In the representations below, the first is a one dimension torus, a simple circle. The second is a two dimension torus, in the shape of a 'doughnut'. The animation illustrates how a two dimension torus is generated from a rectangle by connecting its two pairs of opposite edges. At one dimension, a torus topology is equivalent to a ring interconnect network, in the shape of a circle. At two dimensions, it becomes equivalent to a two dimension mesh, but with extra connection at the edge nodes. === Torus network topology === A torus interconnect is a switch-less topology that can be seen as a mesh interconnect with nodes arranged in a rectilinear array of N = 2, 3, or more dimensions, with processors connected to their nearest neighbors, and corresponding processors on opposite edges of the array connected.[1] In this lattice, each node has 2N connections. This topology is named for the lattice formed in this way, which is topologically homogeneous to an N-dimensional torus. == Visualization == The first 3 dimensions of torus network topology are easier to visualize and are described below: 1D Torus: one dimension, n nodes are connected in closed loop with each node connected to its two nearest neighbors. Communication can take place in two directions, +x and −x. A 1D Torus is the same as ring interconnection. 2D Torus: two dimensions with degree of four, the nodes are imagined laid out in a two-dimensional rectangular lattice of n rows and n columns, with each node connected to its four nearest neighbors, and corresponding nodes on opposite edges connected. Communication can take place in four directions, +x, −x, +y, and −y. The total nodes of a 2D Torus is n2. 3D Torus: three dimensions, the nodes are imagined in a three-dimensional lattice in the shape of a rectangular prism, with each node connected with its six neighbors, with corresponding nodes on opposing faces of the array connected. Each edge consists of n nodes. communication can take place in six directions, +x, −x, +y, −y, +z, −z. Each edge of a 3D Torus consist of n nodes. The total nodes of 3D Torus is n3. ND Torus: N dimensions, each node of an N dimension torus has 2N neighbors, Communication can take place in 2N directions. Each edge consists of n nodes. Total nodes of this torus is nN. The main motivation of having higher dimension of torus is to achieve higher bandwidth, lower latency, and higher scalability. Higher-dimensional arrays are difficult to visualize. The above ruleset shows that each higher dimension adds another pair of nearest neighbor connections to each node. == Performance == A number of supercomputers on the TOP500 list use three-dimensional torus networks, e.g. IBM's Blue Gene/L and Blue Gene/P, and the Cray XT3. IBM's Blue Gene/Q uses a five-dimensional torus network. Fujitsu's K computer and the PRIMEHPC FX10 use a proprietary three-dimensional torus 3D mesh interconnect called Tofu. === 3D Torus performance simulation === Sandeep Palur and Dr. Ioan Raicu from Illinois Institute of Technology conducted experiments to simulate 3D torus performance. Their experiments ran on a computer with 250GB RAM, 48 cores and x86_64 architecture. The simulator they used was ROSS (Rensselaer’s Optimistic Simulation System). They mainly focused on three aspects: Varying network size Varying number of servers Varying message size They concluded that throughput decreases with the increase of servers and network size. Otherwise, throughput increases with the increase of message size. === 6D Torus product performance === Fujitsu Limited developed a 6D torus computer model called "Tofu". In their model, a 6D torus can achieve 100 GB/s off-chip bandwidth, 12 times higher scalability than a 3D torus, and high fault tolerance. The model is used in the K computer and Fugaku. === Cost === While long wrap-around links may be the easiest way to visualize the connection topology, in practice, restrictions on cable lengths often make long wrap-around links impractical. Instead, directly connected nodes—including nodes that the above visualization places on opposite edges of a grid, connected by a long wrap-around link—are physically placed nearly adjacent to each other in a folded torus network. Every link in the folded torus network is very short—almost as short as the nearest-neighbor links in a simple grid interconnect—and therefore low-latency.

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  Polygraphic substitution

  Polygraphic substitution is a substitution cipher in which a uniform substitution is performed on blocks of letters. When the length of the block is specifically known, more precise terms are used: for instance, a cipher in which pairs of letters are substituted is bigraphic. As a concept, polygraphic substitution contrasts with monoalphabetic (or simple) substitutions in which individual letters are uniformly substituted, or polyalphabetic substitutions in which individual letters are substituted in different ways depending on their position in the text. In theory, there is some overlap in these definitions; one could conceivably consider a Vigenère cipher with an eight-letter key to be an octographic substitution. In practice, this is not a useful observation since it is far more fruitful to consider it to be a polyalphabetic substitution cipher. == Specific ciphers == In 1563, Giambattista della Porta devised the first bigraphic substitution. However, it was nothing more than a matrix of symbols. In practice, it would have been all but impossible to memorize, and carrying around the table would lead to risks of falling into enemy hands. In 1854, Charles Wheatstone came up with the Playfair cipher, a keyword-based system that could be performed on paper in the field. This was followed up over the next fifty years with the closely related four-square and two-square ciphers, which are slightly more cumbersome but offer slightly better security. In 1929, Lester S. Hill developed the Hill cipher, which uses matrix algebra to encrypt blocks of any desired length. However, encryption is very difficult to perform by hand for any sufficiently large block size, although it has been implemented by machine or computer. This is therefore on the frontier between classical and modern cryptography. == Cryptanalysis of general polygraphic substitutions == Polygraphic systems do provide a significant improvement in security over monoalphabetic substitutions. Given an individual letter 'E' in a message, it could be encrypted using any of 52 instructions depending on its location and neighbors, which can be used to great advantage to mask the frequency of individual letters. However, the security boost is limited; while it generally requires a larger sample of text to crack, it can still be done by hand. One can identify a polygraphically-encrypted text by performing a frequency chart of polygrams and not merely of individual letters. These can be compared to the frequency of plaintext English. The distribution of digrams is even more stark than individual letters. For example, the six most common letters in English (23%) represent approximately half of English plaintext, but it takes only the most frequent 8% of the 676 digrams to achieve the same potency. In addition, even in a plaintext many thousands of characters long, one would expect that nearly half of the digrams would not occur, or only barely. In addition, looking over the text one would expect to see a fairly regular scattering of repeated text in multiples of the block length and relatively few that are not multiples. Cracking a code identified as polygraphic is similar to cracking a general monoalphabetic substitution except with a larger 'alphabet'. One identifies the most frequent polygrams, experiments with replacing them with common plaintext polygrams, and attempts to build up common words, phrases, and finally meaning. Naturally, if the investigation led the cryptanalyst to suspect that a code was of a specific type, like a Playfair or order-2 Hill cipher, then they could use a more specific attack.

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  Customer data management

  Customer data management (CDM) is the ways in which businesses keep track of their customer information and survey their customer base in order to obtain feedback. CDM includes a range of software or cloud computing applications designed to give large organizations rapid and efficient access to customer data. Surveys and data can be centrally located and widely accessible within a company, as opposed to being warehoused in separate departments. CDM encompasses the collection, analysis, organizing, reporting and sharing of customer information throughout an organization. Businesses need a thorough understanding of their customers’ needs if they are to retain and increase their customer base. Efficient CDM solutions provide companies with the ability to deal instantly with customer issues and obtain immediate feedback. As a result, customer retention and customer satisfaction can show marked improvement. According to a study by Aberdeen Group, "above-average and best-in-class companies... attain greater than 20% annual improvement in retention rates, revenues, data accuracy and partner/customer satisfaction rates." == Customer data management and cloud computing == Cloud computing offers an attractive choice for CDM in many companies due to its accessibility and cost-effectiveness. Businesses can decide who, within their company, should have the ability to create, adjust, analyze or share customer information. In December 2010, 52% of Information Technology (IT) professionals worldwide were deploying, or planning to deploy, cloud computing; this percentage is far higher in many countries. == Background == Customer data management, as a term, was coined in the 1990s, pre-dating the alternative term enterprise feedback management (EFM). CDM was introduced as a software solution that would replace earlier disc-based or paper-based surveys and spreadsheet data. Initially, CDM solutions were marketed to businesses as software, which were specific to one company, and often to one department within that company. This was superseded by application service providers (ASPs) where software was hosted for end user organizations, thus avoiding the necessity for IT professionals to deploy and support software. However, ASPs with their single-tenancy architecture were, in turn, superseded by software as a service (SaaS), engineered for multi-tenancy. By 2007 SaaS applications, giving businesses on-demand access to their customer information, were rapidly gaining popularity compared with ASPs. Cloud computing now includes SaaS and many prominent CDM providers offer cloud-based applications to their clients. In recent years, there has been a push away from the term EFM, with many of those working in this area advocating the slightly updated use of CDM. The return to the term CDM is largely based on the greater need for clarity around the solutions offered by companies, and on the desire to retire terminology veering on techno-jargon that customers may have a hard time understanding.

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  Situated

  In artificial intelligence and cognitive science, the term situated refers to an agent which is embedded in an environment. The term situated is commonly used to refer to robots, but some researchers argue that software agents can also be situated if: they exist in a dynamic (rapidly changing) environment, which they can manipulate or change through their actions, and which they can sense or perceive. Examples might include web-based agents, which can alter data or trigger processes (such as purchases) over the internet, or virtual-reality bots which inhabit and change virtual worlds, such as Second Life. Being situated is generally considered to be part of being embodied, but it is useful to consider each perspective individually. The situated perspective emphasizes that intelligent behaviour derives from the environment and the agent's interactions with it. The nature of these interactions are defined by an agent's embodiment.

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  Data philanthropy

  Data philanthropy refers to the practice of private companies donating corporate data. This data is usually donated to nonprofits or donation-run organizations that have difficulty keeping up with expensive data collection technology. The concept was introduced through the United Nations Global Pulse initiative in 2011 to explore corporate data assets for humanitarian, academic, and societal causes. For example, anonymized mobile data could be used to track disease outbreaks, or data on consumer actions may be shared with researchers to study public health and economic trends. == Definition == A large portion of data collected from the internet consists of user-generated content, such as blogs, social media posts, and information submitted through lead generation and data forms. Additionally, corporations gather and analyze consumer data to gain insight into customer behavior, identify potential markets, and inform investment decisions. United Nations Global Pulse director Robert Kirkpatrick has referred to this type of data as "massive passive data" or "data exhaust." == Challenges == While data philanthropy can enhance development policies, making users' private data available to various organizations raises concerns regarding privacy, ownership, and the equitable use of data. Different techniques, such as differential privacy and alphanumeric strings of information, can allow access to personal data while ensuring user anonymity. However, even if these algorithms work, re-identification may still be possible. Another challenge is convincing corporations to share their data. The data collected by corporations provides them with market competitiveness and insight regarding consumer behavior. Corporations may fear losing their competitive edge if they share the information they have collected with the public. Numerous moral challenges are also encountered. In 2016, Mariarosaria Taddeo, a digital ethics professor at the University of Oxford, proposed an ethical framework to address them. == Sharing strategies == The goal of data philanthropy is to create a global data commons where companies, governments, and individuals can contribute anonymous, aggregated datasets. The United Nations Global Pulse offers four different tactics that companies can use to share their data that preserve consumer anonymity: Share aggregated and derived data sets for analysis under nondisclosure agreements (NDA) Allow researchers to analyze data within the private company's own network under NDAs Real-Time Data Commons: data pooled and aggregated among multiple companies of the same industry to protect competitiveness Public/Private Alerting Network: companies mine data behind their own firewalls and share indicators == Application in various fields == Many corporations take part in data philanthropy, including social networking platforms (e.g., Facebook, Twitter), telecommunications providers (e.g., Verizon, AT&T), and search engines (e.g., Google, Bing). Collecting and sharing anonymized, aggregated user-generated data is made available through data-sharing systems to support research, policy development, and social impact initiatives. By participating in such efforts, these organizations contribute to causes regarded as beneficial to society, allowing institutions to give back meaningfully. With the onset of technological advancements, the sharing of data on a global scale and an in-depth analysis of these data structures could mitigate the effects of global issues such as natural disasters and epidemics. Robert Kirkpatrick, the Director of the United Nations Global Pulse, has argued that this aggregated information is beneficial for the common good and can lead to developments in research and data production in a range of varied fields. === Digital disease detection === Health researchers use digital disease detection by collecting data from various sources—such as social media platforms (e.g., Twitter, Facebook), mobile devices (e.g., cell phones, smartphones), online search queries, mobile apps, and sensor data from wearables and environmental sensors—to monitor and predict the spread of infectious diseases. This approach allows them to track and anticipate outbreaks of epidemics (e.g., COVID-19, Ebola), pandemics, vector-borne diseases (e.g., malaria, dengue fever), and respiratory illnesses (e.g., influenza, SARS), improving response and intervention strategies for the spread of diseases. In 2008, Centers for Disease Control and Prevention collaborated with Google and launched Google Flu Trends, a website that tracked flu-related searches and user locations to track the spread of the flu. Users could visit Google Flu Trends to compare the amount of flu-related search activity versus the reported numbers of flu outbreaks on a graphical map. One drawback of this method of tracking was that Google searches are sometimes performed due to curiosity rather than when an individual is suffering from the flu. According to Ashley Fowlkes, an epidemiologist in the CDC Influenza division, "The Google Flu Trends system tries to account for that type of media bias by modeling search terms over time to see which ones remain stable." Google Flu Trends is no longer publishing current flu estimates on the public website; however, visitors to the site can still view and download previous estimates. Current data can be shared with verified researchers. A study from the Harvard School of Public Health (HSPH), published in the October 12, 2012 issue of Science, discussed how phone data helped curb the spread of malaria in Kenya. The researchers mapped phone calls and texts made by 14,816,521 Kenyan mobile phone subscribers. When individuals left their primary living location, the destination and length of journey were calculated. This data was then compared to a 2009 malaria prevalence map to estimate the disease's commonality in each location. Combining all this information, the researchers could estimate the probability of an individual carrying malaria and map the movement of the disease. This research can be used to track the spread of similar diseases. === Humanitarian aid === Calling patterns of mobile phone users can determine the socioeconomic standings of the populace, which can be used to deduce "its access to housing, education, healthcare, and basic services such as water and electricity." Researchers from Columbia University and Karolinska Institute used daily SIM card location data from both before and after the 2010 Haiti earthquake to estimate the movement of people both in response to the earthquake and during the related 2010 Haiti cholera outbreak. Their research suggests that mobile phone data can provide rapid and accurate estimates of population movements during disasters and outbreaks of infectious disease. Big data can also provide information on looming disasters and can assist relief organizations in rapid-response and locating displaced individuals. By analyzing specific patterns within this 'big data', governments and NGOs can enhance responses to disruptive events such as natural disasters, disease outbreaks, and global economic crises. Leveraging real-time information enables a deeper understanding of individual well-being, allowing for more effective interventions. Corporations utilize digital services, such as human sensor systems, to detect and solve impending problems within communities. This is a strategy used by the private sector to anonymously share customer information for public benefit, while preserving user privacy. === Impoverished areas === Poverty still remains a worldwide issue, with over 2.5 billion people currently impoverished. Statistics indicate the widespread use of mobile phones, even within impoverished communities. Additional data can be collected through Internet access, social media, utility payments and governmental statistics. Data-driven activities can lead to the accumulation of 'big data', which in turn can assist international non-governmental organizations in documenting and evaluating the needs of underprivileged populations. Through data philanthropy, NGOs can distribute information while cooperating with governments and private companies. === Corporate === Data philanthropy incorporates aspects of social philanthropy by allowing corporations to create profound impacts through the act of giving back by dispersing proprietary datasets. The public sector collects and preserves information, considered an essential asset. Companies track and analyze users' online activities to gain insight into their needs related to new products and services. These companies view the welfare of the population as key to business expansion and progression by using their data to highlight global citizens' issues. Experts in the private sector emphasize the importance of integrating diverse data sources—such as retail, mobile, and social media data—to develop essential solutions for global challenges. In Data Philanthropy:

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  Bitcoin Satoshi Vision

  Bitcoin Satoshi Vision (BSV) is a cryptocurrency that is a hard fork of Bitcoin Cash. Bitcoin Satoshi Vision was created in November 2018 by a group of individuals led by Craig Steven Wright, who has claimed since 2015 to be Satoshi Nakamoto, the creator of the original bitcoin. == History == === 2018 split from Bitcoin Cash === On 15 November 2018, a hard fork chain split of Bitcoin Cash occurred between two rival factions called Bitcoin Cash and Bitcoin SV. On 15 November 2018 Bitcoin Cash traded at about $289, and Bitcoin SV traded at about $96.50, down from $425.01 on 14 November for the un-split Bitcoin Cash. The split originated from what was described as a "civil war" in two competing Bitcoin Cash camps. The first camp, supported by entrepreneur Roger Ver and Jihan Wu of Bitmain, promoted the software entitled Bitcoin ABC (short for Adjustable Blocksize Cap), which would maintain the block size at 32 MB. The second camp led by Craig Steven Wright and billionaire Calvin Ayre put forth a competing software version Bitcoin SV, short for "Bitcoin Satoshi Vision", which would increase the block size limit to 128 MB. === 2019 de-listing from Binance === In April 2019, an online feud broke out between those who supported the claims of Bitcoin SV supporter Craig Wright that he was Satoshi Nakamoto, and those who did not. The feud resulted in cryptocurrency exchange Binance de-listing Bitcoin SV from their platform, stating that: At Binance, we periodically review each digital asset we list to ensure that it continues to meet the high level of standard we expect. When a coin or token no longer meets this standard, or the industry changes, we conduct a more in-depth review and potentially delist it. We believe this best protects all of our users. When we conduct these reviews, we consider a variety of factors. Here are some that drive whether we decide to delist a digital asset: Commitment of team to project Level and quality of development activity Network / smart contract stability Level of public communication Responsiveness to our periodic due diligence requests Evidence of unethical / fraudulent conduct Contribution to a healthy and sustainable crypto ecosystem === 2021 network attack === In August 2021, Bitcoin SV suffered a 51% attack, after previously suffering attacks in June and July of the same year. Such an attack involves cryptocurrency miners gaining control of more than half of a network's computing power; these kinds of network attacks have the goal of preventing new transactions from gaining confirmations, allowing the attackers to double-spend coins. Adam James, senior editor at OKEx Insights claimed that "In the intermediate term, the attack has seemingly somewhat-negligible impact on its current price action," however "Faith in [Bitcoin SV] will likely be reduced following the incident." === 2024 high court ruling === In March 2024, Mr Justice James Mellor in the British High Court ruled that Wright is not Satoshi Nakamoto.

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  Data security

  Data security or data protection is the process of securing digital information to protect it from online threats. Data security or protection means protecting digital data, such as those in a database, from destructive forces and from the unwanted actions of unauthorized users, such as a cyberattack or a data breach. Data security protects computer hardware, software, storage devices, and the data of user devices. Data security also protects the data of organizations, companies and administrative controls. Data security guarantees the protection of individual data, such as identity documents and bank data, and protects against unauthorized access, theft and loss of individual data. Data security also protects data breaches that occurs in companies and industries. Good security measures in industries reduce the probability of data breaches, and employees can rely on the company with their data and private information to be kept secured while companies can continue to maintain a stable reputation. The CIA Triad (Confidentiality, Integrity, and Availability) is what is used to practice what an information security is required to follow. Confidentiality, protects information from being accessed by unauthorized persons. Integrity, makes sure data is trustworthy; and Availability, meaning that data can be accessed by approved users when it is needed; are three goals for data security. Non-repudiation in data security definition, is a device/service that shows where the data originated from and the proof of integrity. == Technologies == === Disk encryption === Disk encryption refers to encryption technology that encrypts data on a hard disk drive. It takes data from a storage device and coverts it into an unreadable format. Disk encryption typically takes form in either software (see disk encryption software) or hardware (see disk encryption hardware) which can be used together. Disk encryption is often referred to as on-the-fly encryption (OTFE) or transparent encryption. Full disk encryption encrypts each individual sector of a disk volume. Files and user data are encrypted to hinder unauthorized users from accessing without a decryption key. A diversifier permits a plaintext of a specific disk sector to be encrypted into different ciphertexts, which does not require additional storage, such as an initialization vector (IV) or message authentication code (MAC). === Software versus hardware-based mechanisms for protecting data === Software-based security solutions encrypt the data to protect it from theft. However, a malicious program or a hacker could corrupt the data to make it unrecoverable, making the system unusable. Hardware-based security solutions prevent read and write access to data, which provides very strong protection against tampering and unauthorized access. Hardware-based security or assisted computer security offers an alternative to software-only computer security. Security tokens such as those using PKCS#11 or a mobile phone may be more secure due to the physical access required in order to be compromised. Access is enabled only when the token is connected and the correct PIN is entered (see two-factor authentication). However, dongles can be used by anyone who can gain physical access to it. Newer technologies in hardware-based security solve this problem by offering full proof of security for data. Working off hardware-based security: A hardware device allows a user to log in, log out and set different levels through manual actions. Many devices use biometric technology to prevent malicious users from logging in, logging out, and changing privilege levels. The current state of a user of the device is read by controllers in peripheral devices such as hard disks. Illegal access by a malicious user or a malicious program is interrupted based on the current state of a user by hard disk and DVD controllers making illegal access to data impossible. Hardware-based access control is more secure than the protection provided by the operating systems as operating systems are vulnerable to malicious attacks by viruses and hackers. The data on hard disks can be corrupted after malicious access is obtained. With hardware-based protection, the software cannot manipulate the user privilege levels. A hacker or a malicious program cannot gain access to secure data protected by hardware or perform unauthorized privileged operations. This assumption is broken only if the hardware itself is malicious or contains a backdoor. The hardware protects the operating system image and file system privileges from being tampered with. Therefore, a completely secure system can be created using a combination of hardware-based security and secure system administration policies. === Backups === Backup is the process of reproducing copies of essential data and storing in a separate, secured place. It is used to ensure data that is lost can be recovered from another source. Backups contains a minimum of one copy of the data that requires preservation. It is considered essential to keep a backup of any data in most industries and the process is recommended for any files of importance to a user. There are 3 types of backups; full backups, incremental backups, and differential backups. Full backups secure all data from a production system, such as a server, database, or other connected data source. It is impossible to lose all data in a full backup if a breach or corruption were to occur. Full backups require a significantly large amount of time to back up and may be time-consuming taking hours to days to complete. Incremental backups only secures changed data since last backup. While all backups are done in full backups, incremental backups only save data that is recently or frequently changed. Incremental backups require lower storage costs making it a prominent solution for growing datasets. === Data Privacy === Data privacy (or information privacy) is the right for individual's data to be secured to obstruct the use of unauthorized access. It gives individuals control over their data and how it can be shared to third parties. The U.S Privacy Protection Law (see Privacy laws of the United States) requires organizations to inform individuals of how their data is collected and when a data breach occurs. By implementing an encryption, it ensures that private data is unreadable to cybercriminals. === Data masking === Data masking of structured data is the process of obscuring (masking) specific data within a database table or cell to ensure that data security is maintained and sensitive information is not exposed to unauthorized personnel. This may include masking the data from users (for example so banking customer representatives can only see the last four digits of a customer's national identity number), developers (who need real production data to test new software releases but should not be able to see sensitive financial data), outsourcing vendors, etc. Data masking is a form of encryption, as it obscures data by modifying particular letters and numbers to keep data concealed and protected from potential hackers. The individual that has access to the code that decrypts the replaced characters are the only ones that can uncover the data. === Data erasure === Data erasure (or data deletion, data destruction) is a method of software-based overwriting that permanently clears all electronic data residing on a hard drive or other digital media to ensure that no sensitive data is lost when an asset is retired or reused. Article 17: Right to be Forgotten states that users have the right to permanently remove all of their private information from their old devices/services to give people more control over their data. Users are able to switch between devices efficiently. == Threats == === Malware === Malware (or malicious software) is designed to destroy, corrupt or gain unauthorized access to a computer for the purpose of stealing, or destroying data. Hackers who use malware typically utilize many types of malware, which includes computer virus, computer worms, ransomware, spyware and Trojan horse to create a vast system of disruption and cause easy data theft. One of the victims of the vast system of disruption includes healthcare workers, who are targeted by compromised systems by infections and then having their data attacked. === Phishing === Phishing is a type of scam that allows hackers to hoax people using psychological and social engineering (using human emotions such as their trust and fear) tactics into giving personal data through emails and messages, and install computer viruses if the individual were to click on a malicious link unknowingly. Attackers are able to create websites that are very similar to original websites, which makes it difficult to detect a fake website, causing individuals to fall for giving in information. Phishing attackers use human emotion to exploit them, such as making them feel fear, urgency, sympathy with the message

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  Apache Kudu

  Apache Kudu is a free and open source column-oriented data store of the Apache Hadoop ecosystem. It is compatible with most of the data processing frameworks in the Hadoop environment. It provides completeness to Hadoop's storage layer to enable fast analytics on fast data. The open source project to build Apache Kudu began as internal project at Cloudera. The first version Apache Kudu 1.0 was released 19 September 2016. == Comparison with other storage engines == Kudu was designed and optimized for OLAP workloads. Like HBase, it is a real-time store that supports key-indexed record lookup and mutation. Kudu differs from HBase since Kudu's datamodel is a more traditional relational model, while HBase is schemaless. Kudu's "on-disk representation is truly columnar and follows an entirely different storage design than HBase/Bigtable".

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  Forward anonymity

  Forward anonymity is a property of a cryptographic system which prevents an attacker who has recorded past encrypted communications from discovering its contents and participants in the future. This property is analogous to forward secrecy. An example of a system which uses forward anonymity is a public key cryptography system, where the public key is well-known and used to encrypt a message, and an unknown private key is used to decrypt it. In this system, one of the keys is always said to be compromised, but messages and their participants are still unknown by anyone without the corresponding private key. In contrast, an example of a system which satisfies the perfect forward secrecy property is one in which a compromise of one key by an attacker (and consequent decryption of messages encrypted with that key) does not undermine the security of previously used keys. Forward secrecy does not refer to protecting the content of the message, but rather to the protection of keys used to decrypt messages. == History == Originally introduced by Whitfield Diffie, Paul van Oorschot, and Michael James Wiener to describe a property of STS (station-to-station protocol) involving a long term secret, either a private key or a shared password. == Public Key Cryptography == Public Key Cryptography is a common form of a forward anonymous system. It is used to pass encrypted messages, preventing any information about the message from being discovered if the message is intercepted by an attacker. It uses two keys, a public key and a private key. The public key is published, and is used by anyone to encrypt a plaintext message. The Private key is not well known, and is used to decrypt cyphertext. Public key cryptography is known as an asymmetric decryption algorithm because of different keys being used to perform opposing functions. Public key cryptography is popular because, while it is computationally easy to create a pair of keys, it is extremely difficult to determine the private key knowing only the public key. Therefore, the public key being well known does not allow messages which are intercepted to be decrypted. This is a forward anonymous system because one compromised key (the public key) does not compromise the anonymity of the system. == Web of Trust == A variation of the public key cryptography system is a Web of trust, where each user has both a public and private key. Messages sent are encrypted using the intended recipient's public key, and only this recipient's private key will decrypt the message. They are also signed with the senders private key. This creates added security where it becomes more difficult for an attacker to pretend to be a user, as the lack of a private key signature indicates a non-trusted user. == Limitations == A forward anonymous system does not necessarily mean a wholly secure system. A successful cryptanalysis of a message or sequence of messages can still decode the information without the use of a private key or long term secret. == News == Forward anonymity, along with other privacy-protecting measures, received a burst of media attention after the leak of classified information by Edward Snowden, beginning in June, 2013, which indicated that the NSA and FBI, through specially crafted backdoors in software and computer systems, were conducting mass surveillance over large parts of the population of both the United States (see Mass surveillance in the United States), Europe, Asia, and other parts of the world. They justified this practice as an aid to catch predatory pedophiles. Opponents to this practice argue that leaving in a back door to law enforcement increases the risk of attackers being able to decrypt information, as well as questioning its legality under the US Constitution, specifically being a form of illegal Search and Seizure.

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  Critical data studies

  Critical data studies is the exploration of and engagement with social, cultural, and ethical challenges that arise when working with big data. It is through various unique perspectives and taking a critical approach that this form of study can be practiced. As its name implies, critical data studies draws heavily on the influence of critical theory, which has a strong focus on addressing the organization of power structures. This idea is then applied to the study of data. Interest in this unique field of critical data studies began in 2011 with scholars danah boyd and Kate Crawford posing various questions for the critical study of big data and recognizing its potential threatening impacts on society and culture. It was not until 2014, and more exploration and conversations, that critical data studies was officially coined by scholars Craig Dalton and Jim Thatcher. They put a large emphasis on understanding the context of big data in order to approach it more critically. Researchers such as David Ribes, Robert Soden, Seyram Avle, Sarah E. Fox, and Phoebe Sengers focus on understanding data as a historical artifact and taking an interdisciplinary approach towards critical data studies. Other key scholars in this discipline include Rob Kitchin and Tracey P. Lauriault who focus on reevaluating data through different spheres. Various critical frameworks that can be applied to analyze big data include Feminist, Anti-Racist, Queer, Indigenous, Decolonial, Anti-Ableist, as well as Symbolic and Synthetic data science. These frameworks help to make sense of the data by addressing power, biases, privacy, consent, and underrepresentation or misrepresentation concerns that exist in data as well as how to approach and analyze this data with a more equitable mindset. == Motivation == In their article in which they coin the term 'critical data studies,' Dalton and Thatcher also provide several justifications as to why data studies is a discipline worthy of a critical approach. First, 'big data' is an important aspect of twenty-first century society, and the analysis of 'big data' allows for a deeper understanding of what is happening and for what reasons. Big data is important to critical data studies because it is the type of data used within this field. Big data does not necessarily refer to a large data set, it can have a data set with millions of rows, but also a data set that just has a wide variety and expansive scope of data with a smaller type of dataset. As well as having whole populations in the data set and not just sample sizes. Furthermore, big data as a technological tool and the information that it yields are not neutral, according to Dalton and Thatcher, making it worthy of critical analysis in order to identify and address its biases. Building off this idea, another justification for a critical approach is that the relationship between big data and society is an important one, and therefore worthy of study. Ribes et. al. argue there is a need for an interdisciplinary understanding of data as a historical artifact as a motivating aspect of critical data studies.The overarching consensus in the Computer-Supported Cooperative Work (CSCW) field, is that people should speak for the data, and not let the data speak for itself. The sources of big data and it’s relationship to varied metadata can be a complicated one, which leads to data disorder and a need for an ethical analysis. Additionally, Iliadis and Russo (2016) have called for studying data assemblages. This is to say, data has innate technological, political, social, and economic histories that should be taken into consideration. Kitchin argues data is almost never raw, and it is almost always cooked, meaning that it is always spoken for by the data scientists utilizing it. Thus, Big Data should be open to a variety of perspectives, especially those of cultural and philosophical nature. Further, data contains hidden histories, ideologies, and philosophies. Big data technology can cause significant changes in society's structure and in the everyday lives of people, and, being a product of society, big data technology is worthy of sociological investigation. Moreover, data sets are almost never completely without any influence. Rather, data are shaped by the vision or goals of those gathering the data, and during the data collection process, certain things are quantified, stored, sorted and even discarded by the research team. A critical approach is thus necessary in order to understand and reveal the intent behind the information being presented.One of these critical approaches has been through feminist data studies. This method applies feminist principles to critical studies and data collecting and analysis. The goal of this is to address the power imbalance in data science and society. According to Catherine D’Ignazio and Lauren F. Klein, a power analysis can be performed by examining power, challenging power, evaluating emotion and embodiment, rethinking binaries and hierarchies, embracing pluralism, considering context, and making labor visible. Feminist data studies is part of the movement towards making data to benefit everyone and not to increase existing inequalities. Moreover, data alone cannot speak for themselves; in order to possess any concrete meaning, data must be accompanied by theoretical insight or alternative quantitative or qualitative research measures. Based on different social topics such as anti-racist data studies, critical data studies give a focus on those social issues concerning data. Specifically in anti-racist data studies they use a classification approach to get representation for those within that community. Desmond Upton Patton and others used their own classification system in the communities of Chicago to help target and reduce violence with young teens on twitter. They had students in those communities help them to decipher the terminology and emojis of these teens to target the language used in tweets that followed with violence outside of the computer screens. This is just one real world example of critical data studies and its application. Dalton and Thatcher argue that if one were to only think of data in terms of its exploitative power, there is no possibility of using data for revolutionary, liberatory purposes. Finally, Dalton and Thatcher propose that a critical approach in studying data allows for 'big data' to be combined with older, 'small data,' and thus create more thorough research, opening up more opportunities, questions and topics to be explored. == Issues and concerns for critical data scholars == Data plays a pivotal role in the emerging knowledge economy, driving productivity, competitiveness, efficiency, sustainability, and capital accumulation. The ethical, political, and economic dimensions of data dynamically evolve across space and time, influenced by changing regimes, technologies, and priorities. Technically, the focus lies on handling, storing, and analyzing vast data sets, utilizing machine learning-based data mining and analytics. This technological advancement raises concerns about data quality, encompassing validity, reliability, authenticity, usability, and lineage. The use of data in modern society brings about new ways of understanding and measuring the world, but also brings with it certain concerns or issues. Data scholars attempt to bring some of these issues to light in their quest to be critical of data. Technical and organizational issues could include the scope of the data set, meaning there is too little or too much data to work with, leading to inaccurate results. It becomes crucial for critical data scholars to carefully consider the adequacy of data volume for their analyses. The quality of the data itself is another facet of concern. The data itself could be of poor quality, such as an incomplete or messy data set with missing or inaccurate data values. This would lead researchers to have to make edits and assumptions about the data itself. Addressing these issues often requires scholars to make edits and assumptions about the data to ensure its reliability and relevance. Data scientists could have improper access to the actual data set, limiting their abilities to analyze it. Linnet Taylor explains how gaps in data can arise when people of varying levels of power have certain rights to their data sources. These people in power can control what data is collected, how it is displayed and how it is analyzed. The capabilities of the research team also play a crucial role in the quality of data analytics. The research team may have inadequate skills or organizational capabilities which leads to the actual analytics performed on the dataset to be biased. This can also lead to ecological fallacies, meaning an assumption is made about an individual based on data or results from a larger group of people. These technical and organizational challenges highlight the complexity of working with data and

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