AI Analysis Ui

AI Analysis Ui — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  Semantic analytics

  Semantic analytics, also termed semantic relatedness, is the use of ontologies to analyze content in web resources. This field of research combines text analytics and Semantic Web technologies like RDF. Semantic analytics measures the relatedness of different ontological concepts. Some academic research groups that have active project in this area include Kno.e.sis Center at Wright State University among others. == History == An important milestone in the beginning of semantic analytics occurred in 1996, although the historical progression of these algorithms is largely subjective. In his seminal study publication, Philip Resnik established that computers have the capacity to emulate human judgement. Spanning the publications of multiple journals, improvements to the accuracy of general semantic analytic computations all claimed to revolutionize the field. However, the lack of a standard terminology throughout the late 1990s was the cause of much miscommunication. This prompted Budanitsky & Hirst to standardize the subject in 2006 with a summary that also set a framework for modern spelling and grammar analysis. In the early days of semantic analytics, obtaining a large enough reliable knowledge bases was difficult. In 2006, Strube & Ponzetto demonstrated that Wikipedia could be used in semantic analytic calculations. The usage of a large knowledge base like Wikipedia allows for an increase in both the accuracy and applicability of semantic analytics. == Methods == Given the subjective nature of the field, different methods used in semantic analytics depend on the domain of application. No singular methods is considered correct, however one of the most generally effective and applicable method is explicit semantic analysis (ESA). ESA was developed by Evgeniy Gabrilovich and Shaul Markovitch in the late 2000s. It uses machine learning techniques to create a semantic interpreter, which extracts text fragments from articles into a sorted list. The fragments are sorted by how related they are to the surrounding text. Latent semantic analysis (LSA) is another common method that does not use ontologies, only considering the text in the input space. == Applications == Entity linking Ontology building / knowledge base population Search and query tasks Natural language processing Spoken dialog systems (e.g., Amazon Alexa, Google Assistant, Microsoft's Cortana) Artificial intelligence Knowledge management The application of semantic analysis methods generally streamlines organizational processes of any knowledge management system. Academic libraries often use a domain-specific application to create a more efficient organizational system. By classifying scientific publications using semantics and Wikipedia, researchers are helping people find resources faster. Search engines like Semantic Scholar provide organized access to millions of articles.

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  Data product

  In data management and product management, a data product is a reusable, active, and standardized data asset designed to deliver measurable value to its users, whether internal or external, by applying the rigorous principles of product thinking and management. It comprises one or more data artifacts (e.g., datasets, models, pipelines) and is enriched with metadata, including governance policies, data quality rules, data contracts, and, where applicable, a software bill of materials (SBOM) to document its dependencies and components. Ownership of a data product is aligned to a specific domain or use case, ensuring accountability, stewardship, and its continuous evolution throughout its lifecycle. Adhering to the FAIR principles – findable, accessible, interoperable, and reusable – a data product is designed to be discoverable, scalable, reusable, and aligned with both business and regulatory standards, driving innovation and efficiency in modern data ecosystems. == History == In 2012, DJ Patil proposed the first documented definition: a data product is a product that facilitates an end goal through the use of data. In 2019, Zhamak Dehghani introduced Data Mesh, with a strong focus on domain-oriented data products. Later, in 2020, she solidifies Data Mesh around four principles, one being Data as a Product, in which she defines Data Product as the node on the mesh that encapsulates three structural components required for its function, providing access to the domain's analytical data as a product. In 2024, Andrea Gioia published one of the first books specifically on data products post Data Mesh announcement. In his book, Gioia defines the concept of pure data product. In 2025, during the Data Day Texas conference, Jean-Georges Perrin and a collective of product managers and data engineers got together to craft the current definition and make it available to the public domain. In July 2025, Bitol, a project of The Linux Foundation, released and early version of the Open Data Product Standard (ODPS) aiming at normalizing data products

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  Data analysis

  Data analysis is the process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social science domains. In today's business world, data analysis plays an important role in making decisions more scientific and helping businesses operate more effectively. It is widely used in fields such as business analytics, healthcare, and artificial intelligence to extract meaningful insights from data. Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. In statistical applications, data analysis can be divided into descriptive statistics, exploratory data analysis (EDA), and confirmatory data analysis (CDA). EDA focuses on discovering new features in the data, while CDA focuses on confirming or falsifying existing hypotheses. Predictive analytics focuses on the application of statistical models for predictive forecasting or classification, while text analytics applies statistical, linguistic, and structural techniques to extract and classify information from textual sources, a variety of unstructured data. All of the above are varieties of data analysis. == Data analysis process == Data analysis is a process for obtaining raw data, and subsequently converting it into information useful for decision-making by users. Statistician John Tukey, defined data analysis in 1961, as:"Procedures for analyzing data, techniques for interpreting the results of such procedures, ways of planning the gathering of data to make its analysis easier, more precise or more accurate, and all the machinery and results of (mathematical) statistics which apply to analyzing data." There are several phases, and they are iterative, in that feedback from later phases may result in additional work in earlier phases. === Data requirements === The data is necessary as inputs to the analysis, which is specified based upon the requirements of those directing the analytics (or customers, who will use the finished product of the analysis). The general type of entity upon which the data will be collected is referred to as an experimental unit (e.g., a person or population of people). Specific variables regarding a population (e.g., age and income) may be specified and obtained. Data may be numerical or categorical (i.e., a text label for numbers). === Data collection === Data may be collected from a variety of sources. A list of data sources are available for study & research. The requirements may be communicated by analysts to custodians of the data; such as, Information Technology personnel within an organization. Data collection or data gathering is the process of gathering and measuring information on targeted variables in an established system, which then enables one to answer relevant questions and evaluate outcomes. The data may also be collected from sensors in the environment, including traffic cameras, satellites, recording devices, etc. It may also be obtained through interviews, downloads from online sources, or reading documentation. === Data processing === Data integration is a precursor to data analysis: Data, when initially obtained, must be processed or organized for analysis. For instance, this may involve placing data into rows and columns in a table format (known as structured data) for further analysis, often through the use of spreadsheet (e.g. Excel) or statistical software. === Data cleaning === Once processed and organized, the data may be incomplete, contain duplicates, or contain errors. The need for data cleaning will arise from problems in the way that the data is entered and stored. Data cleaning is the process of preventing and correcting these errors. Common tasks include record matching, identifying inaccuracy of data, overall quality of existing data, deduplication, and column segmentation. Such data problems can also be identified through a variety of analytical techniques. For example; with financial information, the totals for particular variables may be compared against separately published numbers that are believed to be reliable. Unusual amounts, above or below predetermined thresholds, may also be reviewed. There are several types of data cleaning that are dependent upon the type of data in the set; this could be phone numbers, email addresses, employers, or other values. Quantitative data methods for outlier detection can be used to get rid of data that appears to have a higher likelihood of being input incorrectly. Text data spell checkers can be used to lessen the amount of mistyped words. However, it is harder to tell if the words are contextually (i.e., semantically and idiomatically) correct. === Exploratory data analysis === Once the datasets are cleaned, they can then begin to be analyzed using exploratory data analysis. The process of data exploration may result in additional data cleaning or additional requests for data; thus, the initialization of the iterative phases mentioned above. Descriptive statistics, such as the average, median, and standard deviation, are often used to broadly characterize the data. Data visualization is also used, in which the analyst is able to examine the data in a graphical format in order to obtain additional insights about messages within the data. === Modeling and algorithms === Mathematical formulas or mathematical models (supported by algorithms) may be applied to the data in order to identify relationships among the variables; for example, checking for correlation and by determining whether or not there is the presence of causality. In general terms, models may be developed to evaluate a specific variable based on other variable(s) contained within the dataset, with some residual error depending on the implemented model's accuracy (e.g., Data = Model + Error). Inferential statistics utilizes techniques that measure the relationships between particular variables. For example, regression analysis may be used to model whether a change in advertising (independent variable X), provides an explanation for the variation in sales (dependent variable Y), i.e. is Y a function of X? This can be described as (Y = aX + b + error), where the model is designed such that (a) and (b) minimize the error when the model predicts Y for a given range of values of X. === Data product === A data product is a computer application that takes data inputs and generates outputs, feeding them back into the environment. It may be based on a model or algorithm. For instance, an application that analyzes data about customer purchase history, and uses the results to recommend other purchases the customer might enjoy. === Communication === Once data is analyzed, it may be presented in many formats to the users of the analysis to support their requirements. The users may have feedback, which results in additional analysis. When determining how to communicate the results, the analyst may consider implementing a variety of data visualization techniques to help communicate the message more clearly and efficiently to the audience. Data visualization uses information displays (graphics such as, tables and charts) to help communicate key messages contained in the data. Tables are a valuable tool by enabling the ability of a user to query and focus on specific numbers; while charts (e.g., bar charts or line charts), may help explain the quantitative messages contained in the data. == Quantitative messages == Stephen Few described eight types of quantitative messages that users may attempt to communicate from a set of data, including the associated graphs. Time-series: A single variable is captured over a period of time, such as the unemployment rate over a 10-year period. A line chart may be used to demonstrate the trend. Ranking: Categorical subdivisions are ranked in ascending or descending order, such as a ranking of sales performance (the measure) by salespersons (the category, with each salesperson a categorical subdivision) during a single period. A bar chart may be used to show the comparison across the salespersons. Part-to-whole: Categorical subdivisions are measured as a ratio to the whole (i.e., a percentage out of 100%). A pie chart or bar chart can show the comparison of ratios, such as the market share represented by competitors in a market. Deviation: Categorical subdivisions are compared against a reference, such as a comparison of actual vs. budget expenses for several departments of a business for a given time period. A bar chart can show the comparison of the actual versus the reference amount. Frequency distribution:

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  Key-agreement protocol

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

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  Google Research

  Google Research (also known as Research at Google) is the research division of Google, a subsidiary of Alphabet Inc.. According to its official website, Google Research publishes findings, releases open-source software, and applies research results within Google products and services as well as within the wider scientific community. == Notable contributions == The 2017 landmark paper Attention Is All You Need, which introduced the Transformer architecture, which has subsequently been used to build modern large language models. Advances in neural machine translation powering Google Translate. Time series forecasting. Development of scalable learning systems and infrastructure for large-model training. Flood forecasting. Research into computational discovery via Google Accelerated Science including demonstrating the first below-threshold quantum calculations.

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  Microsoft Security Development Lifecycle

  The Microsoft Security Development Lifecycle (SDL) is the approach Microsoft uses to integrate security into DevOps processes (sometimes called a DevSecOps approach). You can use this SDL guidance and documentation to adapt this approach and practices to your organization. == Overview == The practices outlined in the SDL approach are applicable to all types of software development and across all platforms, ranging from traditional waterfall methodologies to modern DevOps approaches. They can generally be applied to the following: Software – whether you are developing software code for firmware, AI applications, operating systems, drivers, IoT Devices, mobile device apps, web services, plug-ins or applets, hardware microcode, low-code/no-code apps, or other software formats. Note that most practices in the SDL are applicable to secure computer hardware development as well. Platforms – whether the software is running on a ‘serverless’ platform approach, on an on-premises server, a mobile device, a cloud hosted VM, a user endpoint, as part of a Software as a Service (SaaS) application, a cloud edge device, an IoT device, or anywhere else. == Practices == The SDL recommends 10 security practices to incorporate into your development workflows. Applying the 10 security practices of SDL is an ongoing process of improvement so a key recommendation is to begin from some point and keep enhancing as you proceed. This continuous process involves changes to culture, strategy, processes, and technical controls as you embed security skills and practices into DevOps workflows. The 10 SDL practices are: Establish security standards, metrics, and governance Require use of proven security features, languages, and frameworks Perform security design review and threat modeling Define and use cryptography standards Secure the software supply chain Secure the engineering environment Perform security testing Ensure operational platform security Implement security monitoring and response Provide security training == Versions ==

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  Cryptosystem

  In cryptography, a cryptosystem is a suite of cryptographic algorithms needed to implement a particular security service, such as confidentiality (encryption). Typically, a cryptosystem consists of three algorithms: one for key generation, one for encryption, and one for decryption. The term cipher (sometimes cypher) is often used to refer to a pair of algorithms, one for encryption and one for decryption. Therefore, the term cryptosystem is most often used when the key generation algorithm is important. For this reason, the term cryptosystem is commonly used to refer to public key techniques; however both "cipher" and "cryptosystem" are used for symmetric key techniques. == Formal definition == Mathematically, a cryptosystem or encryption scheme can be defined as a tuple ( P , C , K , E , D ) {\displaystyle ({\mathcal {P}},{\mathcal {C}},{\mathcal {K}},{\mathcal {E}},{\mathcal {D}})} with the following properties. P {\displaystyle {\mathcal {P}}} is a set called the "plaintext space". Its elements are called plaintexts. C {\displaystyle {\mathcal {C}}} is a set called the "ciphertext space". Its elements are called ciphertexts. K {\displaystyle {\mathcal {K}}} is a set called the "key space". Its elements are called keys. E = { E k : k ∈ K } {\displaystyle {\mathcal {E}}=\{E_{k}:k\in {\mathcal {K}}\}} is a set of functions E k : P → C {\displaystyle E_{k}:{\mathcal {P}}\rightarrow {\mathcal {C}}} . Its elements are called "encryption functions". D = { D k : k ∈ K } {\displaystyle {\mathcal {D}}=\{D_{k}:k\in {\mathcal {K}}\}} is a set of functions D k : C → P {\displaystyle D_{k}:{\mathcal {C}}\rightarrow {\mathcal {P}}} . Its elements are called "decryption functions". For each e ∈ K {\displaystyle e\in {\mathcal {K}}} , there is d ∈ K {\displaystyle d\in {\mathcal {K}}} such that D d ( E e ( p ) ) = p {\displaystyle D_{d}(E_{e}(p))=p} for all p ∈ P {\displaystyle p\in {\mathcal {P}}} . Note; typically this definition is modified in order to distinguish an encryption scheme as being either a symmetric-key or public-key type of cryptosystem. == Examples == A classical example of a cryptosystem is the Caesar cipher. A more contemporary example is the RSA cryptosystem. Another example of a cryptosystem is the Advanced Encryption Standard (AES). AES is a widely used symmetric encryption algorithm that has become the standard for securing data in various applications. Paillier cryptosystem is another example used to preserve and maintain privacy and sensitive information. It is featured in electronic voting, electronic lotteries and electronic auctions.

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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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  Stixel

  In computer vision, a stixel (portmanteau of "stick" and "pixel") is a superpixel representation of depth information in an image, in the form of a vertical stick that approximates the closest obstacles within a certain vertical slice of the scene. Introduced in 2009, stixels have applications in robotic navigation and advanced driver-assistance systems, where they can be used to define a representation of robotic environments and traffic scenes with a medium level of abstraction. == Definition == One of the problems of scene understanding in computer vision is to determine horizontal freespace around the camera, where the agent can move, and the vertical obstacles delimiting it. An image can be paired with depth information (produced e.g. from stereo disparity, lidar, or monocular depth estimation), allowing a dense tridimensional reconstruction of the observed scene. One drawback of dense reconstruction is the large amount of data involved, since each pixel in the image is mapped to an element of a point cloud. Vision problems characterised by planar freespace delimited by mostly vertical obstacles, such as traffic scenes or robotic navigation, can benefit from a condensed representation that allows to save memory and processing time. Stixels are thin vertical rectangles representing a slice of a vertical surface belonging to the closest obstacle in the observed scene. They allow to dramatically reduce the amount of information needed to represent a scene in such problems. A stixel is characterised by three parameters: vertical coordinate of the bottom, height of the stick, and depth. Stixels have fixed width, with each stixel spanning over a certain number of image columns, allowing downsampling of the horizontal image resolution. In the original formulation, each column of the image would contain at most one stixel, and later extensions were developed to allow multiple stixels on each column, allowing to represent multiple objects at different distances. == Stixel estimation == The input to stixel estimation is a dense depth map, that can be computed from stereo disparity or other means. The original approach computes an occupancy grid that can be segmented to estimate the freespace, with dynamic programming providing an efficient method to find an optimal segmentation. Alternative approaches can be used instead of occupancy grid mapping, such as manifold-based methods. The freespace boundary provides the base points of the obstacles at closest longitudinal distance, however multiple objects at different distances might appear in each column of the image. To fully define the obstacles, their height should be estimated, and this is accomplished by segmenting the depth of the object from the depth of the background. A membership function over the pixels can be defined based on the depth value, where the membership represents the confidence of a pixel belonging to the closest vertical obstacle or to the background, and a cut separating the obstacles from the background can again be computed effectively with dynamic programming. Once both the freespace and the obstacle height are known, the stixels can be estimated by fusing the information over the columns spanned by each stixel, and finally a refined depth of the stixel can be estimated via model fitting over the depth of the pixels covered by the stixel, possibly paired with confidence information (e.g. disparity confidence produced by methods such as semi-global matching).

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  PitchYaGame

  PitchYaGame or #PitchYaGame (sometimes abbreviated to PYG) is a volunteer movement hosted on the social media platform Twitter to showcase, and present awards for, independent video games from around the world. == Description == PitchYaGame is hosted on the social media platform Twitter to showcase independent video games from around the world. Video pitches are presented by developers in June and November each year, and use the hashtag #PitchYaGame to identify and reference news about the showcase and the individual pitches, and the presentation of awards. The showcase was founded in May 2020 by Liam Twose, with the mission of recognising independent video games, and "focused on empowering indie game developers to strengthen their position in the industry." Twose has made clear that PitchYaGame is a showcase and not a hardcore competition, with "[j]ust enough of a push to make sure people put their best pitch forward." The team now comprises Twose (@LiamTwose at Twitter), operations manager "Indie Game Lover" (@IndieGameLover), and host Sarah Clancy (@ImSarahNow). The pitches were originally made monthly, with entries split into a number of categories, but this proved unmanageable. PitchYaGame collaborator, Sarah Clancy reported that judging the many entries on a monthly basis was "difficult and unwieldy." Therefore, pitches were later switched to six monthly, "feature creep" was reduced, and awards streamlined into gold, silver, bronze, runners-up, and most viral. == Sponsorship == In June 2021, PitchYaGame prizes were sponsored by Xsolla, and in November 2021 by Aurora Punks and Cold Pixel. No cash prizes were available in 2022, as the organisers moved PitchYaGame into a less-competitive, "more showcase centric format". == Reception == In October 2020, Elijah Beahm at The Escapist wrote that "One of the greatest challenges for any game is landing a solid pitch. You have to sell people, maybe even a publisher, to take your idea seriously. Most of the time, it's an obfuscated process that leaves the average developer scratching their heads, but Liam Twose and his team behind #PitchYaGame, 'PYG' for short, are looking to change all that with some clever social engineering." In March 2021, Cameron Koch at GameSpot wrote that "Using the #PitchYaGame, thousands of indie developers tweeted out pitches for their games on November 2 as part of a social media contest, and the results are astounding." He went on to say that "There is no arguing with the results. According to Twose, around 1100-1300 games were shared with the hashtag, and some real gems look to have shined through." In November 2021, Stafano "Stef" Castelli at IGN Italia wrote that "I myself enjoyed 'browsing through' the competitors, discovering a handful of intriguing video games in development." (translated from Italian). In November 2022, Eric Bartelson at Premortem Games wrote that "It's a great way to get games noticed by fellow developers, but also publishers, investors and press." In June 2023, Mark Plunkett in Kotaku wrote about the impossibility of keeping up with all the video game releases, and described PitchYaGame, which has attracted over 10,000 pitches since 2020, as an "astoundingly simple idea" that has "become an increasingly useful spot to catch up on some excellent-looking games that we may have otherwise completely slept on."

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  Virtual collective consciousness

  Virtual collective consciousness (VCC) is a term rebooted and promoted by two behavioral scientists, Yousri Marzouki and Olivier Oullier in their 2012 Huffington Post article titled: "Revolutionizing Revolutions: Virtual Collective Consciousness and the Arab Spring", after its first appearance in 1999-2000. VCC is now defined as an internal knowledge catalyzed by social media platforms and shared by a plurality of individuals driven by the spontaneity, the homogeneity, and the synchronicity of their online actions. VCC occurs when a large group of persons, brought together by a social media platform think and act with one mind and share collective emotions. Thus, they are able to coordinate their efforts efficiently, and could rapidly spread their word to a worldwide audience. When interviewed about the concept of VCC that appeared in the book - Hyperconnectivity and the Future of Internet Communication - he edited, Professor of Pervasive Computing, Adrian David Cheok mentioned the following: "The idea of a global (collective) virtual consciousness is a bottom-up process and a rather emergent property resulting from a momentum of complex interactions taking place in social networks. This kind of collective behaviour (or intelligence) results from a collision between a physical world and a virtual world and can have a real impact in our life by driving collective action." == Etymology == In 1999-2000, Richard Glen Boire provided a cursory mention and the only occurrence of the term "Virtual collective consciousness" in his text as follows: The trend of technology is to overcome the limitations of the human body. And, the Web has been characterized as a virtual collective consciousness and unconsciousness The recent definition of VCC evolved from the first empirical study that provided a cyberpsychological insight into the contribution of Facebook to the 2011 Tunisian revolution. In this study, the concept was originally called "collective cyberconsciousness". The latter is an extension of the idea of "collective consciousness" coupled with "citizen media" usage. The authors of this study also made a parallel between this original definition of VCC and other comparable concepts such as Durkheim's collective representation, Žižek's "collective mind" or Boguta's "new collective consciousness" that he used to describe the computational history of the Internet shutdown during the Egyptian revolution. Since VCC is the byproduct of the network's successful actions, then these actions must be timely, acute, rapid, domain-specific, and purpose-oriented to successfully achieve their goal. Before reaching a momentum of complexity, each collective behavior starts by a spark that triggers a chain of events leading to a crystallized stance of a tremendous amount of interactions. Thus, VCC is an emergent global pattern from these individual actions. In 2012, the term virtual collective consciousness resurfaced and was brought to light after extending its applications to the Egyptian case and the whole social networking major impact on the success of the so-called Arab Spring. Moreover, the acronym VCC was suggested to identify the theoretical framework covering on-line behaviors leading to a virtual collective consciousness. Hence, online social networks have provided a new and faster way of establishing or modifying "collective consciousness" that was paramount to the 2011 uprisings in the Arab world. == Theoretical underpinnings of VCC == Various theoretical references in fields ranging from sociology to computer science were mentioned in order to account for the key features that render the framework for a virtual collective consciousness. The following list is not exhaustive, but the references it contains are often highlighted: Émile Durkheim's collective representations are at the heart of VCC since collectivity taken decisions according to Durkheim's assumptions will approve or disapprove individuals' actions and help them eventually reach their final goal. Marshall McLuhan's global village: The shrinking of our big world to a small place called cyberspace is made possible by technological extensions of human consciousness. Carl Jung's collective unconscious: When a society witnesses significant changes, the anchoring of archetypal images (e.g., political leaders) seems to be deeply rooted in individuals' collective unconscious that is likely to bias their political choices. Individual memories of public events were also supposed to convey a "collective awareness" that can be subconsciously altered by the instantaneous spread of information through social networking around the world. Daniel Wegner's transactive memory (TM): social-networking platforms such as Facebook during the Tunisian revolution or Twitter during the Egyptian revolution served as placeholders of a VCC where information can be harnessed and steered to the highly specific revolutionary purpose. Although research on TM was originally limited to couples, small groups, and organizations, recent studies strongly suggest that an effective TM can operate on a very large scale too. James Surowiecki's wisdom of crowds Collective influence algorithm: The CI (Collective influence) algorithm is effective in finding influential nodes in a variety of networks, including social networks, communication networks, and biological networks. It has been used to identify influencers on social-media platforms, to identify key nodes in transportation networks, and to identify potential drug-targets in biological networks. == Some illustrations of VCC == Besides the studied effect of social networking on the Tunisian and Egyptian revolutions, the former via Facebook and the latter via Twitter other applications were studied under the prism of VCC framework: The Whitacre's virtual choir: A compelling example of the degree of autonomy and self-identity members of a spontaneously created network through a VCC is Eric Whitacre's unique musical project that involved a collection of singers performing remotely to create a virtual Choir. The effect of all the voices illustrated a genuine virtual collective empathy merging the artist's mind with all the singers through his silent conducting gestures. The Harlem Shake dance: The Bitcoin protocol: It was questioned whether or not the Bitcoin protocol can morph into virtual collective consciousness. The Byzantine generals problem was used as an analogy to understand the behavioral complexity of the community of Bitcoin's users. Artificial Social Networking Intelligence (ASNI): refers to the application of artificial intelligence within social networking services and social media platforms. It encompasses various technologies and techniques used to automate, personalize, enhance, improve, and synchronize users' interactions and experiences within social networks. ASNI is expected to evolve rapidly, influencing how we interact online and shaping our digital experiences. Transparency, ethical considerations, media influence bias, and user control over data will be crucial to ensure responsible development and positive impact.

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  Payment tokenization

  Payment tokenization is a data security process that replaces sensitive payment information, such as credit card numbers, with a unique identifier or "token." This token can be used in place of actual data during transactions but has no exploitable value if breached, thereby reducing the risk of data theft and fraud. == Overview == Payment tokenization is generally categorized into two types: security tokens and payment tokens. Security tokens, also known as post-authorization tokens, are used to replace sensitive information like Primary Account Numbers (PANs), such as credit card numbers either after a payment is authorized or for storing data securely (data-at-rest), such as in merchant databases. These models have been in use since the mid-2000s, following the introduction of the Payment Card Industry Data Security Standard in 2004, which established standards for safeguarding cardholder data. The Payment Card Industry Security Standards Council's 2011 Tokenization Guidelines and the proposed American National Standards Institute X9 standards emphasize using tokens primarily to secure sensitive information, not as replacements for payment credentials processed over financial networks. Traditionally, merchants stored PANs to support backend operations such as settlements, reconciliations, chargebacks, loyalty programs, and customer service. However, with the adoption of security tokenization, merchants can substitute PANs with tokens in their systems. This not only reduces their exposure to fraud but also helps minimize the scope and cost of PCI-DSS compliance, offering a more secure and efficient way to manage cardholder data. == Applications == Payment tokenization is widely used by mobile wallets such as Apple Pay, Google Pay, and Samsung Pay use tokenization to safely store card data on devices. E-commerce platforms rely on it to securely retain customer payment details for recurring purchases. At the physical point of sale, EMV-enabled systems use tokenization to protect card information during in-store transactions. Also, subscription billing services implement tokenization to manage and safeguard payment credentials for ongoing charges.

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  Shadow and highlight enhancement

  Shadow and highlight enhancement refers to an image processing technique used to correct exposure. The use of this technique has been gaining popularity, making its way onto magazine covers, digital media, and photos. It is, however, considered by some to be akin to other destructive Photoshop filters, such as the Watercolor filter, or the Mosaic filter. == Shadow recovery == A conservative application of the shadow/highlight tool can be very useful in recovering shadows, though it tends to leave a telltale halo around the boundary between highlight and shadow if used incorrectly. A way to avoid this is to use the bracketing technique, although this usually requires a tripod. == Highlight recovery == Recovering highlights with this tool, however, has mixed results, especially when using it on images with skin in them, and often makes people look like they have been "sprayed with fake tan". == Shadow brightening - manual == One way to brighten shadows in image editing software such as GIMP or Adobe Photoshop is to duplicate the background layer, invert the copy and set the blend modes of that top layer to "Soft Light". You can also use an inverted black and white copy of the image as a mask on a brightening layer, such as Curves or Levels. == Shadow brightening - automatic == Several automatic computer image processing-based shadow recovery and dynamic range compression methods can yield a similar effect. Some of these methods include the retinex method and homomorphic range compression. The retinex method is based on work from 1963 by Edwin Land, the founder of Polaroid. Shadow enhancement can also be accomplished using adaptive image processing algorithms such as adaptive histogram equalization or contrast limiting adaptive histogram equalization (CLAHE).

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  Social media use in the financial services sector

  Social media in the financial services sector refers to the use of social media by the financial services sector to promote and distribute financial services. Social media is used in various aspects of the financial industry including customer service, marketing, and product development. It has enabled financial institutions to extend their reach through direct and real-time communication with customers, fostering more personal connections. It also allows individuals to talk to other individuals creating lending and trading via social groups as well as developing new financial services by fintech startup companies. In terms of marketing, social media is utilized by both traditional financial companies as well as disruptive fintech companies such as peer-to-peer lending (P2P) companies. The financial industry has used information technology since its inception in the 1960s and social media fits in with this ongoing development. Larger, traditional financial firms have integrated social media into their marketing strategies. Companies in the financial sector are subject to strict regulations that include how they use social media. In the United States, the Financial Industry Regulatory Authority (FINRA) is a key regulator that sets rules how financial firms can interact with consumers. This includes ensuring that social media posts follow financial advertising rules, such as being fair and balanced and not providing misleading information, and that financial advice is not provided by unqualified personnel, such as influencers. == History == In 2003, at the beginning of social media development, MySpace was founded as a "social networking service." It allowed people to create a profile, connect with other people, and post videos, pictures, and songs. As MySpace grew in popularity, it attracted interest from companies wishing to promote their brands on the social platform. They were joined by Facebook and in 2010 by Instagram. Financial service firms were initially slow to adapt to promotion via social media but soon joined other big firms after they saw the success other industries had in engaging with younger people. == Uses == === Branding === While companies are able to connect with more people remotely through providing online financial services, their branding strategy has shifted from customized to standardized. Prior to the outbreak of technology, most banks used customized branding where they targeted only customers in their regions. Businesses can now use technology to operate beyond their geographic location and maintain a consistent image across multiple countries with standardized branding. By being able to extend a consistent brand reputation across a wider geographic location, financial services companies can take advantage of economies of scale in advertising cost, lower administrative complexity, lower entry into new markets, and improved cross-border learning within the company. === Customer engagement === Online banking reduced face-to-face interaction between customers and their banks. Most banking transactions can now be conducted online or through mobile devices, rather than at a local branch with a teller. Social media provides a channel for firms to maintain personal contact with customers, replicating some of the interaction that was previously available at local branches. For example, a bank's Facebook page may feature an employee profile describing their job duties, which serves to present a more human face for larger institutions. === Lending === Social media is a core marketing channel for online peer-to-peer lending as well as small business lenders. Since these companies operate exclusively online, it makes sense for them to market online through social media channels. They are able to grow and find new lenders and buyers by utilizing social networks. === Trading === Social trading is an alternative way of analyzing financial data by looking at what other traders are doing and comparing, copying and discussing their techniques and strategies. Prior to the advent of social trading, investors and traders were relying on fundamental or technical analysis to form their investment decisions. Using social trading investors and traders could integrate into their investment decision-process social indicators from trading data-feeds of other traders. Investors also use platform like Reddit, Signal messaging or WeChat to create social communities to discuss investments and finance. In some cases they use this to join together using meme stocks to move financial markets, such as the 2021 GameStop short squeeze incident. They can also use social groups to launch and promote new products such as cryptocurrencies. Investing application like WeBull incorporate a forum style messaging system on each stock that is available for trading. Financial brokers such as Fidelity Investments, Interactive Brokers, and E-Trade have moved to incorporate community features in their investment apps. == Regulations == The use of social media by investors and financial services professionals for business purposes is subject to regulatory oversight, in the United States this is done primarily by the Financial Industry Regulatory Authority (FINRA). FINRA's rules, designed to protect investors from misleading information in all communications and this also applies to social media communications. This includes ensuring that social media posts follow financial advertising rules, such as being fair and balanced and not providing misleading information, and that advice is not provided by unqualified personnel, such as influencers and bank staff acting in a personal capacity. Financial firms have to maintain books and records of all interaction with customers and this includes social media. == New products and services == Social media has created entirely new products for the financial services sector, revolutionizing products and developing new industries through the merging of social technology and financial services. Fintech startups use social media to promote products to get them established. Several developing nations have used social media to leapfrog traditional financial technology; for example, WeChat Pay, which developed from the Chinese WeChat social media platform, became a major payment system in China within a few years. In 2015, according to consulting firm Accenture, 390 million people in China had registered to use mobile banking. This figure is more than the population of the United States. In the United States, the fintech company Venmo combines technology and financial services on a social platform. Other financial technology companies that have used social media to develop or promote financial products include: Lending Club – One of the first peer-to-peer lending businesses OnDeck Capital – A US online-only lending business Funding Circle – A UK-based online lending company Wise – A global online money transfers company Kabbage – A US online unsecured loan company later acquired by American Express Avant – A US online unsecured loan company Zopa – A UK online neobank providing peer-to-peer lending == Risks == === Reputational damage === Due to the real-time nature of social media, financial services companies can be impacted by potential reputational issues. Any negative experience by customers can easily be shared online and could become a viral phenomenon, those comments could likely have a detrimental effect on the company’s stock price and reputation. On the other hand, any positive experience a customer has can also be shared online. However, positive experiences are much less likely to become viral. === Scams === The nature of social media makes it easy to target individuals without being seen by the wider community, this allows scammers to target individuals. Example include romance scams such as the pig butchering scam where an individual is tricked to transfer funds or assets to the scammer over social media making it hard for law enforcement to track them or recover funds. === Customer privacy === Customer privacy is important for the financial services industry. It is critical that customer information such as a bank account numbers and other personal information is kept private. However, this information can be leaked if for example, a customer is unhappy with a bank’s service, they may tweet at the bank expressing their frustrations and include their name and account number.

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  Media contacts database

  In public relations (PR) and marketing, a media contacts database is a resource which catalogs the names, contact information, and other details about people who work in various media professions. These include journalists, reporters, editors, publishers, contributors, freelance journalists, opinion writers, social media personalities/ influencers, TV show anchors, radio show hosts, DJs, and others. A media contacts database usually contains the following information: Full name of the media contact, The publication or channel they work for Designations (past and present) Topics they cover, or their beat Contact information found in public domains Online presence like blogs and other social networking sites Education Information == Overview == A media contacts database is a public relations tool that is maintained and used by PR professionals to pitch stories on a particular topic, product, or company to a specific group of journalists. These journalists would then write or speak about the particular topic in a relevant issue or episode of their shows. A media contacts database allows a PR professional to gain easy access to hundreds of journalists within a short span of time. Media contacts database are created and sold by many media research companies that offer such PR software for professionals.

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