AI Data Warehouse

AI Data Warehouse — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  Tinybop

  Tinybop is a Brooklyn based publisher of apps for children. == History == Tinybop is a Brooklyn-based children's media company established in 2011 by Raul Gutierrez. App titles are released in two series: the Explorer's Library - a series of science apps and Digital Toys - series of open-ended construction apps. == Published apps == Explorer's Library Titles: The Human Body – An anatomy app for children. Released 2013. The company's first app was illustrated by Kelli Anderson and has been downloaded millions of times. Selected for the American Library Association's Notable Children's Media List in 2022. Named Apple App Store's Best of 2013. Winner of the Digital Ehon Yuichi Kimura Prize for Children's Digital Media. Plants – An app about biomes around the world. Homes – An app about houses around with world. Illustrated by Tuesday Bassen. Winner of the Parents Gold Choice Award for children's apps. Simple Machines – A children's physics app about simple machines. The Earth – An app for children about the geologic Earth illustrated by Sarah Jacoby. Weather – A children's weather app. Skyscrapers – A children's app about building tall buildings. Space – An interactive solar system. Mammals – A children's app about mammals illustrated by Wenjia Tang. Winner of the Digital Ehon Award for Children's Educational media. Coral Reef – An app about marine ecosystems. Winner of an Excellence in Early Learning Digital Media Honor from the American Library Association. State of Matter – An app covering solids, liquids, and gases. Winner of Excellence in Early Learning Digital Media Honor from the American Library Association. Light and Color – An app about light and color. Selected for The American Library Association's Notable Children's Media List 2023. Winner of the 2022 Yoichi Sakakihara Prize for Children's Media. Digital Toys Titles: The Robot Factory – A robot building app for children illustrated by Owen Davey. Apple named The Robot Factory as iPad App of the Year in 2015. The Everything Machine – A visual coding app for children. The Everything Machine was named Apple's Best of 2015. Monsters – A monster creation app illustrated by Tianhua Mao. The Infinite Arcade – An arcade game building app. Me: A Kids Diary – A digital journal for children. Selected for The American Library Association's Notable Children's Media List 2020. The Creature Garden – An app that allows children to create fantastical animals illustrated by Natasha Durley. Selected for The American Library Association's Notable Children's Media List 2021. Things that Go Bump – A multiplayer game set in an enchanted Japanese house, released on Apple Arcade in 2018.

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  Plaintext

  In cryptography, plaintext usually means unencrypted information pending input into cryptographic algorithms, usually encryption algorithms. This usually refers to data that is transmitted or stored unencrypted. == Overview == With the advent of computing, the term plaintext expanded beyond human-readable documents to mean any data, including binary files, in a form that can be viewed or used without requiring a key or other decryption device. Information—a message, document, file, etc.—if to be communicated or stored in an unencrypted form is referred to as plaintext. Plaintext is used as input to an encryption algorithm; the output is usually termed ciphertext, particularly when the algorithm is a cipher. Codetext is less often used, and almost always only when the algorithm involved is actually a code. Some systems use multiple layers of encryption, with the output of one encryption algorithm becoming "plaintext" input for the next. == Secure handling == Insecure handling of plaintext can introduce weaknesses into a cryptosystem by letting an attacker bypass the cryptography altogether. Plaintext is vulnerable in use and in storage, whether in electronic or paper format. Physical security means the securing of information and its storage media from physical, attack—for instance by someone entering a building to access papers, storage media, or computers. Discarded material, if not disposed of securely, may be a security risk. Even shredded documents and erased magnetic media might be reconstructed with sufficient effort. If plaintext is stored in a computer file, the storage media, the computer and its components, and all backups must be secure. Sensitive data is sometimes processed on computers whose mass storage is removable, in which case physical security of the removed disk is vital. In the case of securing a computer, useful (as opposed to handwaving) security must be physical (e.g., against burglary, brazen removal under cover of supposed repair, installation of covert monitoring devices, etc.), as well as virtual (e.g., operating system modification, illicit network access, Trojan programs). Wide availability of keydrives, which can plug into most modern computers and store large quantities of data, poses another severe security headache. A spy (perhaps posing as a cleaning person) could easily conceal one, and even swallow it if necessary. Discarded computers, disk drives and media are also a potential source of plaintexts. Most operating systems do not actually erase anything— they simply mark the disk space occupied by a deleted file as 'available for use', and remove its entry from the file system directory. The information in a file deleted in this way remains fully present until overwritten at some later time when the operating system reuses the disk space. With even low-end computers commonly sold with many gigabytes of disk space and rising monthly, this 'later time' may be months later, or never. Even overwriting the portion of a disk surface occupied by a deleted file is insufficient in many cases. Peter Gutmann of the University of Auckland wrote a celebrated 1996 paper on the recovery of overwritten information from magnetic disks; areal storage densities have gotten much higher since then, so this sort of recovery is likely to be more difficult than it was when Gutmann wrote. Modern hard drives automatically remap failing sectors, moving data to good sectors. This process makes information on those failing, excluded sectors invisible to the file system and normal applications. Special software, however, can still extract information from them. Some government agencies (e.g., US NSA) require that personnel physically pulverize discarded disk drives and, in some cases, treat them with chemical corrosives. This practice is not widespread outside government, however. Garfinkel and Shelat (2003) analyzed 158 second-hand hard drives they acquired at garage sales and the like, and found that less than 10% had been sufficiently sanitized. The others contained a wide variety of readable personal and confidential information. See data remanence. Physical loss is a serious problem. The US State Department, Department of Defense, and the British Secret Service have all had laptops with secret information, including in plaintext, lost or stolen. Appropriate disk encryption techniques can safeguard data on misappropriated computers or media. On occasion, even when data on host systems is encrypted, media that personnel use to transfer data between systems is plaintext because of poorly designed data policy. For example, in October 2007, HM Revenue and Customs lost CDs that contained the unencrypted records of 25 million child benefit recipients in the United Kingdom. Modern cryptographic systems resist known plaintext or even chosen plaintext attacks, and so may not be entirely compromised when plaintext is lost or stolen. Older systems resisted the effects of plaintext data loss on security with less effective techniques—such as padding and Russian copulation to obscure information in plaintext that could be easily guessed.

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  Data

  Data ( DAY-tə, US also DAT-ə, India: DEE-tə) is a collection of discrete or continuous values that conveys information, describing the quantity, quality, fact, statistics, other basic units of meaning, or simply sequences of symbols that may be further interpreted formally. A data point or datum is an individual value in a collection of data. Data is usually organized into structures such as tables that provide additional context and meaning, and may itself be used as data in larger structures. Data may be used as variables in a computational process. Data may represent abstract ideas or concrete measurements. Data is commonly used in scientific research, economics, and virtually every other form of human organizational activity. Examples of data sets include price indices (such as the consumer price index), unemployment rates, literacy rates, and census data. In this context, data represents the raw facts and figures from which useful information can be extracted. Data is collected using techniques such as measurement, observation, query, or analysis, and is typically represented as numbers or characters that may be further processed. Field data is data that is collected in an uncontrolled, in-situ environment. Experimental data is data that is generated in the course of a controlled scientific experiment. Data is analyzed using techniques such as calculation, reasoning, discussion, presentation, visualization, or other forms of post-analysis. Prior to analysis, raw data (or unprocessed data) is typically cleaned: Outliers are removed, and obvious instrument or data entry errors are corrected. Data can be seen as the smallest unit of factual information that can be used as a basis for calculation, reasoning, or discussion. Data can range from abstract ideas to concrete measurements, including, but not limited to, statistics. Thematically connected data presented in some relevant context can be viewed as information. Contextually connected pieces of information can then be described as data insights or intelligence. The stock of insights and intelligence that accumulate over time, resulting from the synthesis of data into information, can then be described as knowledge. Data has been described as "the new oil of the digital economy". Data, as a general concept, refers to the fact that some existing information or knowledge is represented or coded in some form suitable for better usage or processing. Advances in computing technologies have led to the advent of big data, which generally refers to very large quantities of data, typically at the petabyte scale. If restricted to traditional data analysis methods and computing, working with such large (and growing) datasets is difficult, even impossible. In response, the relatively new field of data science uses machine learning (and other artificial intelligence) methods that allow for efficient applications of analytic methods to big data. == Etymology and terminology == The Latin word data is the plural of datum, "(thing) given," and the neuter past participle of dare, "to give". The first English use of the word "data" is from the 1640s. The word "data" was first used to mean "transmissible and storable computer information" in 1946. The expression "data processing" was first used in 1954. When "data" is used more generally as a synonym for "information", it is treated as a mass noun in singular form. This usage is common in everyday language and in technical and scientific fields such as software development and computer science. One example of this usage is the term "big data". When used more specifically to refer to the processing and analysis of sets of data, the term retains its plural form. This usage is common in the natural sciences, life sciences, social sciences, software development and computer science, and grew in popularity in the 20th and 21st centuries. Some style guides do not recognize the different meanings of the term and simply recommend the form that best suits the target audience of the guide. For example, APA style as of the 7th edition requires "data" to be treated as a plural form. == Meaning == Data, information, knowledge, and wisdom are closely related concepts, but each has its role concerning the other, and each term has its meaning. According to a common view, data is collected and analyzed; data only becomes information suitable for making decisions once it has been analyzed in some fashion. One can say that the extent to which a set of data is informative to someone depends on the extent to which it is unexpected by that person. The amount of information contained in a data stream may be characterized by its Shannon entropy. Knowledge is the awareness of its environment that some entity possesses, whereas data merely communicates that knowledge. For example, the entry in a database specifying the height of Mount Everest is a datum that communicates a precisely measured value. This measurement may be included in a book along with other data on Mount Everest to describe the mountain in a manner useful for those who wish to decide on the best method to climb it. Awareness of the characteristics represented by this data is knowledge. Data are often assumed to be the least abstract concept, information the next least, and knowledge the most abstract. In this view, data becomes information by interpretation; e.g., the height of Mount Everest is generally considered "data", a book on Mount Everest geological characteristics may be considered "information", and a climber's guidebook containing practical information on the best way to reach Mount Everest's peak may be considered "knowledge". "Information" bears a diversity of meanings that range from everyday usage to technical use. This view, however, has also been argued to reverse how data emerges from information, and information from knowledge. Generally speaking, the concept of information is closely related to notions of constraint, communication, control, data, form, instruction, knowledge, meaning, mental stimulus, pattern, perception, and representation. Beynon-Davies uses the concept of a sign to differentiate between data and information; data is a series of symbols, while information occurs when the symbols are used to refer to something. Before the development of computing devices and machines, people had to manually collect data and impose patterns on it. With the development of computing devices and machines, these devices can also collect data. In the 2010s, computers were widely used in many fields to collect data and sort or process it, in disciplines ranging from marketing, analysis of social service usage by citizens to scientific research. These patterns in the data are seen as information that can be used to enhance knowledge. These patterns may be interpreted as "truth" (though "truth" can be a subjective concept) and may be authorized as aesthetic and ethical criteria in some disciplines or cultures. Events that leave behind perceivable physical or virtual remains can be traced back through data. Marks are no longer considered data once the link between the mark and observation is broken. Mechanical computing devices are classified according to how they represent data. An analog computer represents a datum as a voltage, distance, position, or other physical quantity. A digital computer represents a piece of data as a sequence of symbols drawn from a fixed alphabet. The most common digital computers use a binary alphabet, that is, an alphabet of two characters typically denoted "0" and "1". More familiar representations, such as numbers or letters, are then constructed from the binary alphabet. Some special forms of data are distinguished. A computer program is a collection of data, that can be interpreted as instructions. Most computer languages make a distinction between programs and the other data on which programs operate, but in some languages, notably Lisp and similar languages, programs are essentially indistinguishable from other data. It is also useful to distinguish metadata, that is, a description of other data. A similar yet earlier term for metadata is "ancillary data." The prototypical example of metadata is the library catalog, which is a description of the contents of books. == Data sources == With respect to ownership of data collected in the course of marketing or other corporate collection, data has been characterized according to party depending on how close the data is to the source or if it has been generated through additional processing. "Zero-party data" refers to data that customers "intentionally and proactively shares". This kind of data can come from a variety of sources, including: subscriptions, preference centers, quizzes, surveys, pop-up forms, and interactive digital experiences. "First-party data" may be collected by a company directly from its customers. The secure exchange of first-party data among companies can be done using data clean rooms. "S

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  Trusted Computing

  Trusted Computing (TC) is a technology developed and promoted by the Trusted Computing Group. The term is taken from the field of trusted systems and has a specialized meaning that is distinct from the field of confidential computing. With Trusted Computing, the computer will consistently behave in expected ways, and those behaviors will be enforced by computer hardware and software. Enforcing this behavior is achieved by loading the hardware with a unique encryption key that is inaccessible to the rest of the system and the owner. TC is controversial as the hardware is not only secured for its owner, but also against its owner, leading opponents of the technology like free software activist Richard Stallman to deride it as "treacherous computing", and certain scholarly articles to use scare quotes when referring to the technology. Trusted Computing proponents such as International Data Corporation, the Enterprise Strategy Group and Endpoint Technologies Associates state that the technology will make computers safer, less prone to viruses and malware, and thus more reliable from an end-user perspective. They also state that Trusted Computing will allow computers and servers to offer improved computer security over that which is currently available. Opponents often state that this technology will be used primarily to enforce digital rights management policies (imposed restrictions to the owner) and not to increase computer security. Chip manufacturers Intel and AMD, hardware manufacturers such as HP and Dell, and operating system providers such as Microsoft include Trusted Computing in their products if enabled. The U.S. Army requires that every new PC it purchases comes with a Trusted Platform Module (TPM). As of July 3, 2007, so does virtually the entire United States Department of Defense. == Key concepts == Trusted Computing encompasses six key technology concepts, of which all are required for a fully Trusted system, that is, a system compliant to the TCG specifications: Endorsement key Secure input and output Memory curtaining / protected execution Sealed storage Remote attestation Trusted Third Party (TTP) === Endorsement key === The endorsement key is a 2048-bit RSA public and private key pair that is created randomly on the chip at manufacture time and cannot be changed. The private key never leaves the chip, while the public key is used for attestation and for encryption of sensitive data sent to the chip, as occurs during the TPM_TakeOwnership command. This key is used to allow the execution of secure transactions: every Trusted Platform Module (TPM) is required to be able to sign a random number (in order to allow the owner to show that he has a genuine trusted computer), using a particular protocol created by the Trusted Computing Group (the direct anonymous attestation protocol) in order to ensure its compliance of the TCG standard and to prove its identity; this makes it impossible for a software TPM emulator with an untrusted endorsement key (for example, a self-generated one) to start a secure transaction with a trusted entity. The TPM should be designed to make the extraction of this key by hardware analysis hard, but tamper resistance is not a strong requirement. === Memory curtaining === Memory curtaining extends common memory protection techniques to provide full isolation of sensitive areas of memory—for example, locations containing cryptographic keys. Even the operating system does not have full access to curtained memory. The exact implementation details are vendor specific. === Sealed storage === Sealed storage protects private information by binding it to platform configuration information including the software and hardware being used. This means the data can be released only to a particular combination of software and hardware. Sealed storage can be used for DRM enforcing. For example, users who keep a song on their computer that has not been licensed to be listened will not be able to play it. Currently, a user can locate the song, listen to it, and send it to someone else, play it in the software of their choice, or back it up (and in some cases, use circumvention software to decrypt it). Alternatively, the user may use software to modify the operating system's DRM routines to have it leak the song data once, say, a temporary license was acquired. Using sealed storage, the song is securely encrypted using a key bound to the trusted platform module so that only the unmodified and untampered music player on his or her computer can play it. In this DRM architecture, this might also prevent people from listening to the song after buying a new computer, or upgrading parts of their current one, except after explicit permission of the vendor of the song. === Remote attestation === Remote attestation allows changes to the user's computer to be detected by authorized parties. For example, software companies can identify unauthorized changes to software, including users modifying their software to circumvent commercial digital rights restrictions. It works by having the hardware generate a certificate stating what software is currently running. The computer can then present this certificate to a remote party to show that unaltered software is currently executing. Numerous remote attestation schemes have been proposed for various computer architectures, including Intel, RISC-V, and ARM. Remote attestation is usually combined with public-key encryption so that the information sent can only be read by the programs that requested the attestation, and not by an eavesdropper. To take the song example again, the user's music player software could send the song to other machines, but only if they could attest that they were running an authorized copy of the music player software. Combined with the other technologies, this provides a more restricted path for the music: encrypted I/O prevents the user from recording it as it is transmitted to the audio subsystem, memory locking prevents it from being dumped to regular disk files as it is being worked on, sealed storage curtails unauthorized access to it when saved to the hard drive, and remote attestation prevents unauthorized software from accessing the song even when it is used on other computers. To preserve the privacy of attestation responders, Direct Anonymous Attestation has been proposed as a solution, which uses a group signature scheme to prevent revealing the identity of individual signers. Proof of space (PoS) have been proposed to be used for malware detection, by determining whether the L1 cache of a processor is empty (e.g., has enough space to evaluate the PoSpace routine without cache misses) or contains a routine that resisted being evicted. === Trusted third party === == Known applications == The Microsoft products Windows Vista, Windows 7, Windows 8 and Windows RT make use of a Trusted Platform Module to facilitate BitLocker Drive Encryption. Other known applications with runtime encryption and the use of secure enclaves include the Signal messenger and the e-prescription service ("E-Rezept") by the German government. == Possible applications == === Digital rights management === Trusted Computing would allow companies to create a digital rights management (DRM) system which would be very hard to circumvent, though not impossible. An example is downloading a music file. Sealed storage could be used to prevent the user from opening the file with an unauthorized player or computer. Remote attestation could be used to authorize play only by music players that enforce the record company's rules. The music would be played from curtained memory, which would prevent the user from making an unrestricted copy of the file while it is playing, and secure I/O would prevent capturing what is being sent to the sound system. Circumventing such a system would require either manipulation of the computer's hardware, capturing the analogue (and thus degraded) signal using a recording device or a microphone, or breaking the security of the system. New business models for use of software (services) over Internet may be boosted by the technology. By strengthening the DRM system, one could base a business model on renting programs for a specific time periods or "pay as you go" models. For instance, one could download a music file which could only be played a certain number of times before it becomes unusable, or the music file could be used only within a certain time period. === Preventing cheating in online games === Trusted Computing could be used to combat cheating in online games. Some players modify their game copy in order to gain unfair advantages in the game; remote attestation, secure I/O and memory curtaining could be used to determine that all players connected to a server were running an unmodified copy of the software. === Verification of remote computation for grid computing === Trusted Computing could be used to guarantee participants in a grid computing sys

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  PCVC Speech Dataset

  The PCVC (Persian Consonant Vowel Combination) Speech Dataset is a Modern Persian speech corpus for speech recognition and also speaker recognition. The dataset contains sound samples of Modern Persian combination of vowel and consonant phonemes from different speakers. Every sound sample contains just one consonant and one vowel So it is somehow labeled in phoneme level. This dataset consists of 23 Persian consonants and 6 vowels. The sound samples are all possible combinations of vowels and consonants (138 samples for each speaker). The sample rate of all speech samples is 48000 which means there are 48000 sound samples in every 1 second. Every sound sample starts with consonant then continues with vowel. In each sample, in average, 0.5 second of each sample is speech and the rest is silence. Each sound sample ends with silence. All of sound samples are denoised with "Adaptive noise reduction" algorithm. Compared to Farsdat speech dataset and Persian speech corpus it is more easy to use because it is prepared in .mat data files. Also it is more based on phoneme based separation and all samples are denoised. == Contents == The corpus is downloadable from its Kaggle web page, and contains the following: .mat data files of sound samples in a 23630000 matrix, in which 23 is number of consonants, 6 is the number of vowels and 30000 is the length of sound sample.

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  Dynamic knowledge repository

  The dynamic knowledge repository (DKR) is a concept developed by Douglas C. Engelbart as a primary strategic focus for allowing humans to address complex problems. He has proposed that a DKR will enable us to develop a collective IQ greater than any individual's IQ. References and discussion of Engelbart's DKR concept are available at the Doug Engelbart Institute. == Definition == A knowledge repository is a computerized system that systematically captures, organizes and categorizes an organization's knowledge. The repository can be searched and data can be quickly retrieved. The effective knowledge repositories include factual, conceptual, procedural and meta-cognitive techniques. The key features of knowledge repositories include communication forums. A knowledge repository can take many forms to "contain" the knowledge it holds. A customer database is a knowledge repository of customer information and insights – or electronic explicit knowledge. A Library is a knowledge repository of books – physical explicit knowledge. A community of experts is a knowledge repository of tacit knowledge or experience. The nature of the repository only changes to contain/manage the type of knowledge it holds. A repository (as opposed to an archive) is designed to get knowledge out. It should therefore have some rules of structure, classification, taxonomy, record management, etc., to facilitate user engagement.

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  Sumazi

  Sumazi is a social media and social intelligence platform for enterprises, brands, and celebrities. Its technology performs social data analysis across social networking services including Facebook, Twitter and LinkedIn, to identify key people in his/her network who are experts, influencers or are located in a specific area for marketing, advertising or sales campaigns. The technology company was founded in 2011 by former Sun Microsystems employee Sumaya Kazi. The company was headquartered in San Francisco, California. The company was out of business by 2017. == Reception == Sumazi was one of 25 startups selected out of more than 1,200 to compete at TechCrunch Disrupt Startup Battlefield, where it won the Omidyar Network award for the startup "Most Likely to Change the World." Sumazi, which was based out of San Francisco, California, had been profiled in The New York Times as well as USA Today, which commented the advantages of the startup's location in the Silicon Valley. American Express OPEN Forum also featured Sumazi as a "Startup of the Week". Sumazi has additionally been mentioned in articles by Mashable, The Wall Street Journal, Current Editorials, Harvard Business Review, Smashing Magazine, and TechCrunch.

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  Data stream management system

  A data stream management system (DSMS) is a computer software system to manage continuous data streams. It is similar to a database management system (DBMS), which is, however, designed for static data in conventional databases. A DBMS also offers a flexible query processing so that the information needed can be expressed using queries. However, in contrast to a DBMS, a DSMS executes a continuous query that is not only performed once, but is permanently installed. Therefore, the query is continuously executed until it is explicitly uninstalled. Since most DSMS are data-driven, a continuous query produces new results as long as new data arrive at the system. This basic concept is similar to complex event processing so that both technologies are partially coalescing. == Functional principle == One important feature of a DSMS is the possibility to handle potentially infinite and rapidly changing data streams by offering flexible processing at the same time, although there are only limited resources such as main memory. The following table provides various principles of DSMS and compares them to traditional DBMS. == Processing and streaming models == One of the biggest challenges for a DSMS is to handle potentially infinite data streams using a fixed amount of memory and no random access to the data. There are different approaches to limit the amount of data in one pass, which can be divided into two classes. For the one hand, there are compression techniques that try to summarize the data and for the other hand there are window techniques that try to portion the data into (finite) parts. === Synopses === The idea behind compression techniques is to maintain only a synopsis of the data, but not all (raw) data points of the data stream. The algorithms range from selecting random data points called sampling to summarization using histograms, wavelets or sketching. One simple example of a compression is the continuous calculation of an average. Instead of memorizing each data point, the synopsis only holds the sum and the number of items. The average can be calculated by dividing the sum by the number. However, it should be mentioned that synopses cannot reflect the data accurately. Thus, a processing that is based on synopses may produce inaccurate results. === Windows === Instead of using synopses to compress the characteristics of the whole data streams, window techniques only look on a portion of the data. This approach is motivated by the idea that only the most recent data are relevant. Therefore, a window continuously cuts out a part of the data stream, e.g. the last ten data stream elements, and only considers these elements during the processing. There are different kinds of such windows like sliding windows that are similar to FIFO lists or tumbling windows that cut out disjoint parts. Furthermore, the windows can also be differentiated into element-based windows, e.g., to consider the last ten elements, or time-based windows, e.g., to consider the last ten seconds of data. There are also different approaches to implementing windows. There are, for example, approaches that use timestamps or time intervals for system-wide windows or buffer-based windows for each single processing step. Sliding-window query processing is also suitable to being implemented in parallel processors by exploiting parallelism between different windows and/or within each window extent. == Query processing == Since there are a lot of prototypes, there is no standardized architecture. However, most DSMS are based on the query processing in DBMS by using declarative languages to express queries, which are translated into a plan of operators. These plans can be optimized and executed. A query processing often consists of the following steps. === Formulation of continuous queries === The formulation of queries is mostly done using declarative languages like SQL in DBMS. Since there are no standardized query languages to express continuous queries, there are a lot of languages and variations. However, most of them are based on SQL, such as the Continuous Query Language (CQL), StreamSQL and ESP. There are also graphical approaches where each processing step is a box and the processing flow is expressed by arrows between the boxes. The language strongly depends on the processing model. For example, if windows are used for the processing, the definition of a window has to be expressed. In StreamSQL, a query with a sliding window for the last 10 elements looks like follows: This stream continuously calculates the average value of "price" of the last 10 tuples, but only considers those tuples whose prices are greater than 100.0. In the next step, the declarative query is translated into a logical query plan. A query plan is a directed graph where the nodes are operators and the edges describe the processing flow. Each operator in the query plan encapsulates the semantic of a specific operation, such as filtering or aggregation. In DSMSs that process relational data streams, the operators are equal or similar to the operators of the Relational algebra, so that there are operators for selection, projection, join, and set operations. This operator concept allows the very flexible and versatile processing of a DSMS. === Optimization of queries === The logical query plan can be optimized, which strongly depends on the streaming model. The basic concepts for optimizing continuous queries are equal to those from database systems. If there are relational data streams and the logical query plan is based on relational operators from the Relational algebra, a query optimizer can use the algebraic equivalences to optimize the plan. These may be, for example, to push selection operators down to the sources, because they are not so computationally intensive like join operators. Furthermore, there are also cost-based optimization techniques like in DBMS, where a query plan with the lowest costs is chosen from different equivalent query plans. One example is to choose the order of two successive join operators. In DBMS this decision is mostly done by certain statistics of the involved databases. But, since the data of a data streams is unknown in advance, there are no such statistics in a DSMS. However, it is possible to observe a data stream for a certain time to obtain some statistics. Using these statistics, the query can also be optimized later. So, in contrast to a DBMS, some DSMS allows to optimize the query even during runtime. Therefore, a DSMS needs some plan migration strategies to replace a running query plan with a new one. === Transformation of queries === Since a logical operator is only responsible for the semantics of an operation but does not consist of any algorithms, the logical query plan must be transformed into an executable counterpart. This is called a physical query plan. The distinction between a logical and a physical operator plan allows more than one implementation for the same logical operator. The join, for example, is logically the same, although it can be implemented by different algorithms like a Nested loop join or a Sort-merge join. Notice, these algorithms also strongly depend on the used stream and processing model. Finally, the query is available as a physical query plan. === Execution of queries === Since the physical query plan consists of executable algorithms, it can be directly executed. For this, the physical query plan is installed into the system. The bottom of the graph (of the query plan) is connected to the incoming sources, which can be everything like connectors to sensors. The top of the graph is connected to the outgoing sinks, which may be for example a visualization. Since most DSMSs are data-driven, a query is executed by pushing the incoming data elements from the source through the query plan to the sink. Each time when a data element passes an operator, the operator performs its specific operation on the data element and forwards the result to all successive operators. == Examples == AURORA, StreamBase Systems, Inc. Archived 23 March 2009 at the Wayback Machine Hortonworks DataFlow IBM Streams NIAGARA Query Engine NiagaraST: A Research Data Stream Management System at Portland State University Odysseus, an open source Java-based framework for Data Stream Management Systems Pipeline DB PIPES Archived 24 December 2016 at the Wayback Machine, webMethods Business Events QStream SAS Event Stream Processing SQLstream STREAM StreamGlobe StreamInsight TelegraphCQ WSO2 Stream Processor

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  Artificial wisdom

  Artificial wisdom (AW) is an artificial intelligence (AI) system which is able to display the human traits of wisdom and morals while being able to contemplate its own “endpoint”. Artificial wisdom can be described as artificial intelligence reaching the top-level of decision-making when confronted with the most complex challenging situations. The term artificial wisdom is used when the "intelligence" is based on more than by chance collecting and interpreting data, but by design enriched with smart and conscience strategies that wise people would use. == Overview == The goal of artificial wisdom is to create artificial intelligence that can successfully replicate the “uniquely human trait[s]” of having wisdom and morals as closely as possible. Thus, artificial wisdom, must “incorporate [the] ethical and moral considerations” of the data it uses. There are also many significant ethical and legal implications of AW which are compounded by the rapid advances in AI and related technologies alongside the lack of the development of ethics, guidelines, and regulations without the oversight of any kind of overarching advisory board. Additionally, there are challenges in how to develop, test, and implement AW in real world scenarios. Existing tests do not test the internal thought process by which a computer system reaches its conclusion, only the result of said process. When examining computer-aided wisdom; the partnership of artificial intelligence and contemplative neuroscience, concerns regarding the future of artificial intelligence shift to a more optimistic viewpoint. This artificial wisdom forms the basis of Louis Molnar's monographic article on artificial philosophy, where he coined the term and proposes how artificial intelligence might view its place in the grand scheme of things. == Definitions == There are no universal or standardized definitions for human intelligence, artificial intelligence, human wisdom, or artificial wisdom. However, the DIKW pyramid, describes the continuum of relationship between data, information, knowledge, and wisdom, puts wisdom at the highest level in its hierarchy. Gottfredson defines intelligence as “the ability to reason, plan, solve problems, think abstractly, comprehend complex ideas, learn quickly, and learn from experience”. Definitions for wisdom typically include requiring: The ability for emotional regulation, Pro-social behaviors (e.g., empathy, compassion, and altruism), Self-reflection, “A balance between decisiveness and acceptance of uncertainty and diversity of perspectives, and social advising.” As previously defined, Artificial Wisdom would then be an AI system which is able to solve problems via “an understanding of…context, ethics and moral principles,” rather than simple pre-defined inputs or “learned patterns.” Some scientists have also considered the field of artificial consciousness. However, Jeste states that “…it is generally agreed that only humans can have consciousness, autonomy, will, and theory of mind.” An artificially wise system must also be able to contemplate its end goal and recognize its own ignorance. Additionally, to contemplate its end goal, a wise system must have a “correct conception of worthwhile goals (broadly speaking) or well-being (narrowly speaking)”. "Stephen Grimm further suggests that the following three types of knowledge are individually necessary for wisdom: first, "knowledge of what is good or important for well-being", second, "knowledge of one’s standing, relative to what is good or important for well-being", and third, "knowledge of a strategy for obtaining what is good or important for wellbeing."" == Problems == There are notable problems with attempting to create an artificially wise system. Consciousness, autonomy, and will are considered strictly human features. === Values === There are significant ethical and philosophical issues when attempting to create an intelligent or a wise system. Notably, whose moral values will be used to train the system to be wise. Differing moral values and prejudice can already be seen from various organizations and governments in artificial intelligence. Deployment strategies and values of Artificial Wisdom will conflict between leaders, companies, and countries. Nusbaum states, “When values are in conflict, leaders often make choices that are clever or smart about their own needs, but are often not wise.” === Ethics === Science fiction author Isaac Asimov realized the need to control the technology in the 1940s when he wrote the three laws of robotics as follows: A robot may not injure a human directly or indirectly. A robot must obey human’s orders. A robot should seek to protect its own existence. Additionally, the pace at which technology is rapidly advancing artificial intelligence and thus the need for artificial wisdom may “have outpaced the development of societal guidelines have raised serious questions about the ethics and morality of AI, and called for international oversight and regulations to ensure safety.” === Principal impossibility === One argument, coined by Tsai as the “argument against AW,” or AAAW, postulates the principal impossibility of Artificial Wisdom. The argument is based on the philosophical differences between practical wisdom, also called phronesis, and practical intelligence. Said difference isn’t in “selecting the correct means, but reasoning correctly about what ends to follow”. Tsai puts the argument into a logical proposition as follows: “(P1) An agent is genuinely wise only if the agent can deliberate about the final goal of the domain in which the agent is situated.” “(P2) An intelligent agent cannot deliberate about the final goal of the domain in which the agent is situated.” “(C1) An intelligent agent cannot be genuinely wise.” “(P3) An AW is, at its core, intelligent.” “(C2) An AW cannot be genuinely wise.”

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  Personal web page

  Personal web pages are World Wide Web pages created by an individual to contain content of a personal nature rather than content pertaining to a company, organization or institution. Personal web pages are primarily used for informative or entertainment purposes but can also be used for personal career marketing (by containing a list of the individual's skills, experience and a CV), social networking with other people with shared interests, or as a space for personal expression. These terms do not usually refer to just a single "page" or HTML file, but to a website—a collection of webpages and related files under a common URL or Web address. In strictly technical terms, a site's actual home page (index page) often only contains sparse content with some catchy introductory material and serves mostly as a pointer or table of contents to the more content-rich pages inside, such as résumés, family, hobbies, family genealogy, a web log/diary ("blog"), opinions, online journals and diaries or other writing, examples of written work, digital audio sound clips, digital video clips, digital photos, or information about a user's other interests. Many personal pages only include information of interest to friends and family of the author. However, some webpages set up by hobbyists or enthusiasts of certain subject areas can be valuable topical web directories. == History == In the 1990s, most Internet service providers (ISPs) provided a free small personal, user-created webpage along with free Usenet News service. These were all considered part of full Internet service. Also several free web hosting services such as GeoCities provided free web space for personal web pages. These free web hosting services would typically include web-based site management and a few pre-configured scripts to easily integrate an input form or guestbook script into the user's site. Early personal web pages were often called "home pages" and were intended to be set as a default page in a web browser's preferences, usually by their owner. These pages would often contain links, to-do lists, and other information their author found useful. In the days when search engines were in their infancy, these pages (and the links they contained) could be an important resource in navigating the web. Since the early 2000s, the rise of blogging and the development of user friendly web page designing software made it easier for amateur users who did not have computer programming or website designer training to create personal web pages. Some website design websites provided free ready-made blogging scripts, where all the user had to do was input their content into a template. At the same time, a personal web presence became easier with the increased popularity of social networking services, some with blogging platforms such as LiveJournal and Blogger. These websites provided an attractive and easy-to-use content management system for regular users. Most of the early personal websites were Web 1.0 style, in which a static display of text and images or photos was displayed to individuals who came to the page. About the only interaction that was possible on these early websites was signing the virtual "guestbook". With the collapse of the dot-com bubble in the late 1990s, the ISP industry consolidated, and the focus of web hosting services shifted away from the surviving ISP companies to independent Internet hosting services and to ones with other affiliations. For example, many university departments provided personal pages for professors and television broadcasters provided them for their on-air personalities. These free webpages served as a perquisite ("perk") for staff, while at the same time boosting the Web visibility of the parent organization. Web hosting companies either charge a monthly fee, or provide service that is "free" (advertising based) for personal web pages. These are priced or limited according to the total size of all files in bytes on the host's hard drive, or by bandwidth, (traffic), or by some combination of both. For those customers who continue to use their ISP for these services, national ISPs commonly continue to provide both disk space and help including ready-made drop-in scripts. With the rise of Web 2.0-style websites, both professional websites and user-created, amateur websites tended to contain interactive features, such as "clickable" links to online newspaper articles or favourite websites, the option to comment on content displayed on the website, the option to "tag" images, videos or links on the site, the option of "clicking" on an image to enlarge it or find out more information, the option of user participation for website guests to evaluate or review the pages, or even the option to create new user-generated content for others to see. A key difference between Web 1.0 personal webpages and Web 2.0 personal pages was while the former tended to be created by hackers, computer programmers and computer hobbyists, the latter were created by a much wider variety of users, including individuals whose main interests lay in hobbies or topics outside of computers (e.g., indie music fans, political activists, and social entrepreneurs). == Motivations == In a study done by Zinkhan, participants had four main reasons to create personal web pages. First, people use personal web pages as a portrayal of self, in a sense marketing themselves, since creators have the freedom to portray their own identities. Second, personal web pages are a way to interact with people who have similar interests as the creator, possible employers, or colleagues. Third, personal web pages can gain social acceptance with groups that the creator is interested in depending on the information that the creator reveals about themselves. Fourth, personal web pages can give creators a sense of connection to the world since these web pages are public and a way to introduce oneself to other people around the globe. People may maintain personal web pages to serve as a showcase for their skills in professional life, creative skills or self promotion of their business, charity or band. The use of personal web pages to display an individual's professional life has become more common in the 21st century. Mary Madden, an expert researcher on privacy and technology, did a study that found a tenth of American jobs require Personal web pages that advertise an individual online. Personal web pages have become a source of initial impression of possible employees used by employers. It can also be used to express opinions on issues ranging from news and politics to movies. Others may use their personal web page as a communication method. For example, an aspiring artist might give out business cards with their personal web page, and invite people to visit their page and see their artwork, "like" their page or sign their guestbook. A personal web page gives the owner generally more control on presence in search results and how they wish to be viewed online. It also allows more freedom in types and quantity of content than a social network profile offers, and can link various social media profiles with each other. It can be used to correct the record on something, or clear up potential confusion between you and someone with the same name. In the 2010s, some amateur writers, bands and filmmakers release digital versions of their stories, songs and short films online, with the aim of gaining an audience and becoming more well-known. While the huge number of aspiring artists posting their work online makes it unlikely for individuals and groups to become popular via the Internet, there are a small number of YouTube stars who were unknown until their online performances garnered them a huge audience. == Sites of academics == Academic professionals (especially at the college and university level), including professors and researchers, are often given online space for creating and storing personal web documents, including personal web pages, CVs and a list of their books, academic papers and conference presentations, on the websites of their employers. This goes back to the early decade of the World Wide Web and its original purpose of providing a quick and easy way for academics to share research papers and data. Researchers may have a personal website to share more information about themselves, about their academic activities and for sharing (unpublished) results of their research. This has been noted as part of the success of open-access repositories such as arXiv.

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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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  Brooklyn Bridge (software)

  The Brooklyn Bridge from White Crane Systems was a data transfer enabler. Although it came with some hardware, it was the software which was the basis of the product. It also could transform the data's format. == Overview == The New York Times described its category as being among "communications packages used to transfer files." In an era of 300 baud, Brooklyn Bridge operated at "115,200 baud" so that a transfer which "at 300 baud took 4 minutes and 36 seconds" only needed 5 seconds. Unlike some communications packages, this one retains the original version-date, so as not to alarm people when they seem to have what looks like an update, when it's not. == Description == Once the software is installed, users comfortable with typing the word "COPY" can do so as readily as they sneakernet. An earlier review described it as "less cumbersome than conventional communications software" The use of neither specialized hardware nor specialized software is ideal in an era when this can be done using online or other "outside" services.

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  Oversampled binary image sensor

  An oversampled binary image sensor is an image sensor with non-linear response capabilities reminiscent of traditional photographic film. Each pixel in the sensor has a binary response, giving only a one-bit quantized measurement of the local light intensity. The response function of the image sensor is non-linear and similar to a logarithmic function, which makes the sensor suitable for high dynamic range imaging. == Working principle == Before the advent of digital image sensors, photography, for the most part of its history, used film to record light information. At the heart of every photographic film are a large number of light-sensitive grains of silver-halide crystals. During exposure, each micron-sized grain has a binary fate: Either it is struck by some incident photons and becomes "exposed", or it is missed by the photon bombardment and remains "unexposed". In the subsequent film development process, exposed grains, due to their altered chemical properties, are converted to silver metal, contributing to opaque spots on the film; unexposed grains are washed away in a chemical bath, leaving behind the transparent regions on the film. Thus, in essence, photographic film is a binary imaging medium, using local densities of opaque silver grains to encode the original light intensity information. Thanks to the small size and large number of these grains, one hardly notices this quantized nature of film when viewing it at a distance, observing only a continuous gray tone. The oversampled binary image sensor is reminiscent of photographic film. Each pixel in the sensor has a binary response, giving only a one-bit quantized measurement of the local light intensity. At the start of the exposure period, all pixels are set to 0. A pixel is then set to 1 if the number of photons reaching it during the exposure is at least equal to a given threshold q. One way to build such binary sensors is to modify standard memory chip technology, where each memory bit cell is designed to be sensitive to visible light. With current CMOS technology, the level of integration of such systems can exceed 109~1010 (i.e., 1 giga to 10 giga) pixels per chip. In this case, the corresponding pixel sizes (around 50~nm ) are far below the diffraction limit of light, and thus the image sensor is oversampling the optical resolution of the light field. Intuitively, one can exploit this spatial redundancy to compensate for the information loss due to one-bit quantizations, as is classic in oversampling delta-sigma converters. Building a binary sensor that emulates the photographic film process was first envisioned by Fossum, who coined the name digital film sensor (now referred to as a quanta image sensor). The original motivation was mainly out of technical necessity. The miniaturization of camera systems calls for the continuous shrinking of pixel sizes. At a certain point, however, the limited full-well capacity (i.e., the maximum photon-electrons a pixel can hold) of small pixels becomes a bottleneck, yielding very low signal-to-noise ratios (SNRs) and poor dynamic ranges. In contrast, a binary sensor whose pixels need to detect only a few photon-electrons around a small threshold q has much less requirement for full-well capacities, allowing pixel sizes to shrink further. == Imaging model == === Lens === Consider a simplified camera model shown in Fig.1. The λ 0 ( x ) {\displaystyle \lambda _{0}(x)} is the incoming light intensity field. By assuming that light intensities remain constant within a short exposure period, the field can be modeled as only a function of the spatial variable x {\displaystyle x} . After passing through the optical system, the original light field λ 0 ( x ) {\displaystyle \lambda _{0}(x)} gets filtered by the lens, which acts like a linear system with a given impulse response. Due to imperfections (e.g., aberrations) in the lens, the impulse response, a.k.a. the point spread function (PSF) of the optical system, cannot be a Dirac delta, thus, imposing a limit on the resolution of the observable light field. However, a more fundamental physical limit is due to light diffraction. As a result, even if the lens is ideal, the PSF is still unavoidably a small blurry spot. In optics, such diffraction-limited spot is often called the Airy disk, whose radius R a {\displaystyle R_{a}} can be computed as R a = 1.22 w f , {\displaystyle R_{a}=1.22\,wf,} where w {\displaystyle w} is the wavelength of the light and f {\displaystyle f} is the F-number of the optical system. Due to the lowpass (smoothing) nature of the PSF, the resulting λ ( x ) {\displaystyle \lambda (x)} has a finite spatial-resolution, i.e., it has a finite number of degrees of freedom per unit space. === Sensor === Fig.2 illustrates the binary sensor model. The s m {\displaystyle s_{m}} denote the exposure values accumulated by the sensor pixels. Depending on the local values of s m {\displaystyle s_{m}} , each pixel (depicted as "buckets" in the figure) collects a different number of photons hitting on its surface. y m {\displaystyle y_{m}} is the number of photons impinging on the surface of the m {\displaystyle m} th pixel during an exposure period. The relation between s m {\displaystyle s_{m}} and the photon count y m {\displaystyle y_{m}} is stochastic. More specifically, y m {\displaystyle y_{m}} can be modeled as realizations of a Poisson random variable, whose intensity parameter is equal to s m {\displaystyle s_{m}} , As a photosensitive device, each pixel in the image sensor converts photons to electrical signals, whose amplitude is proportional to the number of photons impinging on that pixel. In a conventional sensor design, the analog electrical signals are then quantized by an A/D converter into 8 to 14 bits (usually the more bits the better). But in the binary sensor, the quantizer is 1 bit. In Fig.2, b m {\displaystyle b_{m}} is the quantized output of the m {\displaystyle m} th pixel. Since the photon counts y m {\displaystyle y_{m}} are drawn from random variables, so are the binary sensor output b m {\displaystyle b_{m}} . === Spatial and temporal oversampling === If it is allowed to have temporal oversampling, i.e., taking multiple consecutive and independent frames without changing the total exposure time τ {\displaystyle \tau } , the performance of the binary sensor is equivalent to the sensor with same number of spatial oversampling under certain condition. It means that people can make trade off between spatial oversampling and temporal oversampling. This is quite important, since technology usually gives limitation on the size of the pixels and the exposure time. == Advantages over traditional sensors == Due to the limited full-well capacity of conventional image pixel, the pixel will saturate when the light intensity is too strong. This is the reason that the dynamic range of the pixel is low. For the oversampled binary image sensor, the dynamic range is not defined for a single pixel, but a group of pixels, which makes the dynamic range high. == Reconstruction == One of the most important challenges with the use of an oversampled binary image sensor is the reconstruction of the light intensity λ ( x ) {\displaystyle \lambda (x)} from the binary measurement b m {\displaystyle b_{m}} . Maximum likelihood estimation can be used for solving this problem. Fig. 4 shows the results of reconstructing the light intensity from 4096 binary images taken by single photon avalanche diodes (SPADs) camera. A better reconstruction quality with fewer temporal measurements and faster, hardware friendly implementation, can be achieved by more sophisticated algorithms.

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  Data room

  Data rooms are secure spaces used for housing data, usually of a privileged or confidential nature. They can be physical data rooms, virtual data rooms (VDRs), or data centers. They are primarily used for a variety of corporate purposes, including data storage, document exchange, file sharing, financial transactions, and legal proceedings. Today, data rooms are central to workflows in mergers and acquisitions, venture capital, and corporate restructuring, increasingly utilizing artificial intelligence to securely manage and review large datasets. Historically, data rooms were strictly physical locations heavily guarded and monitored. Today, the vast majority of corporate data rooms are hosted virtually on secure cloud platforms, though physical rooms are still occasionally used for highly sensitive government or proprietary intelligence. == Physical Data Rooms == In mergers and acquisitions (M&A), the traditional data room genuinely consists of a physically secured and continually monitored room, normally in the vendor's offices or those of their legal counsel. Bidders and their advisers visit this room in order to inspect and report on various documents, legal contracts, and financial statements made available during the due diligence process. Historically, physical data rooms presented significant logistical challenges. Often, only one bidder at a time was allowed to enter to maintain document integrity and confidentiality. If new documents or new versions of documents were required, they had to be brought in by courier as hardcopies. Teams involved in large due diligence processes typically had to be flown in from many regions or countries and remain available throughout the process. Because these teams comprised a number of experts in different fields—such as legal counsel, forensic accountants, and industry specialists—the overall cost of keeping such groups on call near the physical data room was often extremely high. == Virtual Data Rooms (VDRs) == To address the costs and logistical bottlenecks of physical data rooms, virtual data rooms (VDRs) were developed to provide secure, online dissemination of confidential information. A VDR is essentially a secure cloud repository with strictly controlled access. Access is managed through secure log-ons supplied by the vendor or authority, which can be disabled at any time if a bidder withdraws from a transaction. Because much of the information released during corporate transactions is highly confidential, VDRs utilize digital rights management (DRM) to control information. Restrictions are applied to the viewers' ability to release data to third parties by disabling forwarding, copying, or printing capabilities. Modern VDRs also employ dynamic watermarking and detailed auditing capabilities. Detailed auditing is required for legal reasons so that a precise digital footprint is kept of who has viewed which version of each document, and for how long. Furthermore, modern VDR platforms are typically built to comply with stringent information security standards such as ISO 27001 and SOC 2. Transitioning from sequential physical data rooms to parallel virtual data rooms has been shown to significantly reduce the duration of M&A transactions while allowing sellers to field multiple bidders simultaneously. == Key Applications == Data rooms are commonly used by legal, accounting, investment banking, and private equity firms. Primary applications include: Mergers and Acquisitions (M&A): VDRs are central to the sell-side M&A process. After potential buyers sign a Non-Disclosure Agreement (NDA) and review a Confidential Information Memorandum (CIM), they are granted data room access to perform deep financial due diligence, such as Quality of Earnings (QoE) analysis and legal liability assessments. Venture Capital and Startups: Startups use data rooms as a centralized location for key operational data, capitalization tables, and financial projections to streamline due diligence for angel investors and venture capital firms during fundraising rounds. Initial Public Offerings (IPOs): Taking a company public requires intense regulatory scrutiny. Data rooms are used to securely share company histories and financial audits with investment bankers, legal teams, and regulatory bodies. Corporate Restructuring and Insolvency: During bankruptcies or corporate carve-outs, data rooms are used to organize outstanding debt profiles, creditor agreements, and operational liabilities. == Emerging Technologies == In recent years, the management of virtual data rooms has increasingly incorporated Artificial Intelligence (AI) and Machine Learning (ML). Generative AI and Natural Language Processing (NLP) tools are now integrated into VDRs to automatically index thousands of documents, perform auto-redaction of personally identifiable information (PII), and assist buy-side analysts in identifying hidden liabilities within unstructured text data during the due diligence phase. Modern AI algorithms can extract line items from financial statements to instantly populate structured databases.

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  IEBus

  IEBus (Inter Equipment Bus) is a communication bus specification "between equipments within a vehicle or a chassis" of Renesas Electronics. It defines OSI model layer 1 and layer 2 specification. IEBus is mainly used for car audio and car navigations, which established de facto standard in Japan, though SAE J1850 is major in United States. IEBus is also used in some vending machines, which major customer is Fuji Electric. Each button on the vending machine has an IEBus ID, i.e. has a controller. Detailed specification is disclosed to licensees only, but protocol analyzers are provided from some test equipment vendors. Its modulation method is PWM (Pulse-Width Modulation) with 6.00 MHz base clock originally, but most of automotive customers use 6.291 MHz, and physical layer is a pair of differential signalling harness. Its physical layer adopts half-duplex, asynchronous, and multi-master communication with carrier-sense multiple access with collision detection (CSMA/CD) for medium access control. It allows for up to fifty units on one bus over a maximum length of 150 meters. Two differential signalling lines are used with Bus+ / Bus− naming, sometimes labeled as Data(+) / Data(−). It is sometimes described as "IE-BUS", "IE-Bus," or "IE Bus," but these are incorrect. In formal, it is "IEBus." IEBus® and Inter Equipment Bus® are registered trademark symbols of Renesas Electronics Corporation, formerly NEC Electronics Corporation, (JPO: Reg. No.2552418 and 2552419, respectively). == History == In the middle of '80s, semiconductor unit of NEC Corporation, currently Renesas Electronics, started the study for increasing demands for automotive audio systems. IEBus is introduced as a solution for the distributed control system. In the late 1980s, several similar specifications, including the Domestic Digital Bus (D2B), the Japanese Home Bus (HBS), and the European Home System (EHS) are proposed by different companies or organizations. These were once discussed as IEC 61030, but it was withdrawn in 2006. IEBus is also a similar specification (refer to "Transfer signal format" section), but not listed in these criteria. As the result, IEBus becomes a de facto standard of car audio in Japan. Regarding the Domestic Digital Bus (D2B), it is re-defined as D2B Optical by Mercedes-Benz independently. As for Japanese Home Bus System (HBS), it is defined in 1988 as Home Bus System Standard Specification, ET-2101 by JEITA and REEA (Radio Engineering & Electronics Assiation) in Japan. It is being used by several Japanese air conditioner manufacturers (for example, M-Net from Mitsubishi and the P1/P2 or F1/F2 bus from Daikin). Fujitsu provided HBPC (Home Bus Protocol Controller) chip as MB86046B. But it is unclear whether Fujitsu (currently, Cypress) still manufactures this HBPC LSI as of 2018. Mitsumi Electric provides the MM1007 and MM1192 driver ICs for HBS. The HBS specification is also discussed in the Echonet Consortium. In 2014, a utility model patent for protocol converter from HBS to RS-485 is granted in China as "CN204006496U." Regarding the replacement of IEBus, a paper by Hyundai Autonet, currently Hyundai Mobis, describes as follows. "In communication methods for digital input capable amplifiers, Inter Equipment Bus (IEBus) was used in early times, but for now, Controller Area Network (CAN) is mainly used." == Protocol overview == A master talks to a slave. Each unit has a master and a slave address register. Only one device can talk on the bus at any given time. There is a pecking order for the types of communications which will take precedence over another. Each communication from master to slave must be replied to by the slave going back to the master with acknowledge bits each of those show ACK or NAK. If the master does not receive the ACK within a predefined time allowance for a mode, it drops the communication and returns to its standby (listen) mode. Detailed specification of OSI model layer 2 is disclosed to licensees only, but protocol analyzers are provided from some test equipment vendors. In 2012, one of Chinese manufacturer's patent is granted as "CN202841169U". An open-source software emulator called "IEBus Studio" exists on a repository of SourceForge, but the last update was on 2008-02-24. Another open-source analyzer software called "IEBusAnalyzer" is available on GitHub repository. Some hobbyist made some tools also. === Physical layer (OSI model layer 1) specification overview === From μPD6708 data sheet. and μPD78098B Subseries user's manual, hardware. Communication system Half-duplex asynchronous communication Multi-master system All the units connected to the IEBus can transfer data to the other units. Broadcast communication function (communication between one unit and multiple units) Normally, communication is individually carried out from one unit to another. By using the broadcast communication function, however, communication can be executed from one unit to plural units as follows: Group broadcast communication: Broadcast communication to group units Simultaneous broadcast communication: Broadcast communication to all units Effective transmission rate The effective transmission rate can be selected from the following three communication modes: Mixture of the plural of modes in the same bus line is not allowed. Correct communication between different base clock is not possible. Access control CSMA/CD (Carrier Sense Multiple Access with Collision Detection) The priority of occupying IEBus is as follows: «1» Broadcast communication takes precedence over individual communication. «2» The lower the master address, the higher the priority. Communication scale Number of units: 50 MAX. Cable length: 150 m MAX. (when a twisted pair cable is used) Load capacity: MAX. 8000 pF; between Bus+ and Bus−, (6.000000 MHz base clock) MAX. 7100 pF; between Bus+ and Bus−, (6.291456 MHz base clock) Terminating resistor: 120 Ω Logic level Logic 1: Low level. Voltage difference between Bus+ and Bus− is under 20mV Logic 0: High Level. Voltage difference between Bus+ and Bus− is over 120mV In-phase input voltage high: Bus+ ≤ (VDD-1.0) V, Bus− ≥ 1.0 V === Transfer signal format === From μPD6708 data sheet. and μPD78098B Subseries user's manual, hardware. This frame format is much similar to that of Domestic Digital Bus (D2B). All fields are MSB first. ==== Functions of Control bits ==== === Bit format === Each IEBus bit consists of four periods. Preparation period: The first or subsequent low-level (logic "1") period Synchronization period: Next high-level (logic "0") period Data period: Period indicating value of bit; ether low-level (logic "1") or high-level (logic "0") Stop period: The last low-level (logic "1") period Synchronization is done by each bit. Time lengths of the synchronization period and data period are almost the same. The time of the entire bits' and each bit's specification, related to the time of each period allocated to it, differ depending both on the type of the transmit bit and on whether the unit is the master or a slave unit. == Automotive manufacturers using IEBus == Each manufacturer has its own name, but it is not an alias of IEBus. Those are specifications of wire harness which comprise control cables based on IEBus, OSI model layer 3 and above communication protocol, audio cables, interconnection couplers, and so on. === Pioneer === Pioneer Corporation employed IEBus for its original branded car audio in early '90s. In its earlier stage, it was used just for control bus between the head unit in dashboard and the CD changer usually placed in trunk room. Nowadays, the specification includes connection between head units, navigation systems, rear speaker systems, and so on. IP-Bus: Wire harness specification. === Toyota === Pioneer Corporation pushed Toyota Motor Corporation to adopt IEBus as the genuine parts. In 1994, Toyota decided to employ IEBus for its genuine specification, but it is slightly different from that of Pioneer. It is named as AVC-LAN. AVC-LAN: Wire harness specification, based on mode 2. === Honda/Acura === Pioneer Corporation also pushed Honda Motor. Honda also decided to adopt IEBus as its genuine parts specification just after Toyota do so. GA-NET II: Wire harness specification. Honda Music Link: Honda genuine gadget to connect Apple Inc. products. A hobbyist made touch screen controller on Acura TSX for a Car PC installed in the trunk. === Sirius XM Satellite Radio === Sirius XM Satellite Radio is a satellite broadcasting radio operator in US. Its digital media receiver equipment utilizes IEBus. == Evaluation boards == === SAKURA board === GR-SAKUKRA board and GR-SAKURA-FULL board are Renesas official promotion boards of RX63N chip, which enables IEBus mode 0 and 1, but not mode 2, i.e. not available for Toyota AVC-LAN. They are an Arduino pin compatible low-price ones, suitable for hobbyists. Their color of printed circuit board is SAKURA in Japanese, which means cherry blossom. To e

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