ChatScript is a combination Natural Language engine and dialog management system designed initially for creating chatbots, but is currently also used for various forms of NL processing. It is written in C++. The engine is an open source project at SourceForge. and GitHub. ChatScript was written by Bruce Wilcox and originally released in 2011, after Suzette (written in ChatScript) won the 2010 Loebner Prize, fooling one of four human judges. == Features == In general ChatScript aims to author extremely concisely, since the limiting scalability of hand-authored chatbots is how much/fast one can write the script. Because ChatScript is designed for interactive conversation, it automatically maintains user state across volleys. A volley is any number of sentences the user inputs at once and the chatbots response. The basic element of scripting is the rule. A rule consists of a type, a label (optional), a pattern, and an output. There are three types of rules. Gambits are something a chatbot might say when it has control of the conversation. Rejoinders are rules that respond to a user remark tied to what the chatbot just said. Responders are rules that respond to arbitrary user input which is not necessarily tied to what the chatbot just said. Patterns describe conditions under which a rule may fire. Patterns range from extremely simplistic to deeply complex (analogous to Regex but aimed for NL). Heavy use is typically made of concept sets, which are lists of words sharing a meaning. ChatScript contains some 2000 predefined concepts and scripters can easily write their own. Output of a rule intermixes literal words to be sent to the user along with common C-style programming code. Rules are bundled into collections called topics. Topics can have keywords, which allows the engine to automatically search the topic for relevant rules based on user input. == Example code == Words starting with ~ are concept sets. For example, ~fruit is the list of all known fruits. The simple pattern (~fruit) reacts if any fruit is mentioned immediately after the chatbot asks for favorite food. The slightly more complex pattern for the rule labelled WHATMUSIC requires all the words what, music, you and any word or phrase meaning to like, but they may occur in any order. Responders come in three types. ?: rules react to user questions. s: rules react to user statements. u: rules react to either. ChatScript code supports standard if-else, loops, user-defined functions and calls, and variable assignment and access. == Data == Some data in ChatScript is transient, meaning it will disappear at the end of the current volley. Other data is permanent, lasting forever until explicitly killed off. Data can be local to a single user or shared across all users at the bot level. Internally all data is represented as text and is automatically converted to a numeric form as needed. === Variables === User variables come in several kinds. Variables purely local to a topic or function are transient. Global variables can be declared as transient or permanent. A variable is generally declared merely by using it, and its type depends on its prefix ($, $$, $_). === Facts === In addition to variables, ChatScript supports facts – triples of data, which can also be transient or permanent. Functions can query for facts having particular values of some of the fields, making them act like an in-memory database. Fact retrieval is very quick and efficient the number of available in-memory facts is largely constrained to the available memory of the machine running the ChatScript engine. Facts can represent record structures and are how ChatScript represents JSON internally. Tables of information can be defined to generate appropriate facts. The above table links people to what they invented (1 per line) with Einstein getting a list of things he did. == External communication == ChatScript embeds the Curl library and can directly read and write facts in JSON to a website. == Server == A ChatScript engine can run in local or server mode. == Pos-tagging, parsing, and ontology == ChatScript comes with a copy of English WordNet embedded within, including its ontology, and creates and extends its own ontology via concept declarations. It has an English language pos-tagger and parser and supports integration with TreeTagger for pos-tagging a number of other languages (TreeTagger commercial license required). == Databases == In addition to an internal fact database, ChatScript supports PostgreSQL, MySQL, MSSQL and MongoDB both for access by scripts, but also as a central filesystem if desired so ChatScript can be scaled horizontally. A common use case is to use a centralized database to host the user files and multiple servers to scale the ChatScript engine. == JavaScript == ChatScript also embeds DukTape, ECMAScript E5/E5.1 compatibility, with some semantics updated from ES2015+. == Spelling Correction == ChatScript has built-in automatic spell checking, which can be augmented in script as both simple word replacements or context sensitive changes. With appropriate simple rules you can change perfect legal words into other words or delete them. E.g., if you have a concept of ~electronic_goods and don't want an input of Radio Shack (a store name) to be detected as an electronic good, you can get the input to change to Radio_Shack (a single word), or allow the words to remain but block the detection of the concept. This is particularly useful when combined with speech-to-text code that is imperfect, but you are familiar with common failings of it and can compensate for them in script. == Control flow == A chatbot's control flow is managed by the control script. This is merely another ordinary topic of rules, that invokes API functions of the engine. Thus control is fully configurable by the scripter (and functions exist to allow introspection into the engine). There are pre-processing control flow and post-processing control flow options available, for special processing.
Inception (deep learning architecture)
Inception is a family of convolutional neural network (CNN) for computer vision, introduced by researchers at Google in 2014 as GoogLeNet (later renamed Inception v1). The series was historically important as an early CNN that separates the stem (data ingest), body (data processing), and head (prediction), an architectural design that persists in all modern CNN. == Version history == === Inception v1 === In 2014, a team at Google developed the GoogLeNet architecture, an instance of which won the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The name came from the LeNet of 1998, since both LeNet and GoogLeNet are CNNs. They also called it "Inception" after a "we need to go deeper" internet meme, a phrase from Inception (2010) the film. Because later, more versions were released, the original Inception architecture was renamed again as "Inception v1". The models and the code were released under Apache 2.0 license on GitHub. The Inception v1 architecture is a deep CNN composed of 22 layers. Most of these layers were "Inception modules". The original paper stated that Inception modules are a "logical culmination" of Network in Network and (Arora et al, 2014). Since Inception v1 is deep, it suffered from the vanishing gradient problem. The team solved it by using two "auxiliary classifiers", which are linear-softmax classifiers inserted at 1/3-deep and 2/3-deep within the network, and the loss function is a weighted sum of all three: L = 0.3 L a u x , 1 + 0.3 L a u x , 2 + L r e a l {\displaystyle L=0.3L_{aux,1}+0.3L_{aux,2}+L_{real}} These were removed after training was complete. This was later solved by the ResNet architecture. The architecture consists of three parts stacked on top of one another: The stem (data ingestion): The first few convolutional layers perform data preprocessing to downscale images to a smaller size. The body (data processing): The next many Inception modules perform the bulk of data processing. The head (prediction): The final fully-connected layer and softmax produces a probability distribution for image classification. This structure is used in most modern CNN architectures. === Inception v2 === Inception v2 was released in 2015, in a paper that is more famous for proposing batch normalization. It had 13.6 million parameters. It improves on Inception v1 by adding batch normalization, and removing dropout and local response normalization which they found became unnecessary when batch normalization is used. === Inception v3 === Inception v3 was released in 2016. It improves on Inception v2 by using factorized convolutions. As an example, a single 5×5 convolution can be factored into 3×3 stacked on top of another 3×3. Both has a receptive field of size 5×5. The 5×5 convolution kernel has 25 parameters, compared to just 18 in the factorized version. Thus, the 5×5 convolution is strictly more powerful than the factorized version. However, this power is not necessarily needed. Empirically, the research team found that factorized convolutions help. It also uses a form of dimension-reduction by concatenating the output from a convolutional layer and a pooling layer. As an example, a tensor of size 35 × 35 × 320 {\displaystyle 35\times 35\times 320} can be downscaled by a convolution with stride 2 to 17 × 17 × 320 {\displaystyle 17\times 17\times 320} , and by maxpooling with pool size 2 × 2 {\displaystyle 2\times 2} to 17 × 17 × 320 {\displaystyle 17\times 17\times 320} . These are then concatenated to 17 × 17 × 640 {\displaystyle 17\times 17\times 640} . Other than this, it also removed the lowest auxiliary classifier during training. They found that the auxiliary head worked as a form of regularization. They also proposed label-smoothing regularization in classification. For an image with label c {\displaystyle c} , instead of making the model to predict the probability distribution δ c = ( 0 , 0 , … , 0 , 1 ⏟ c -th entry , 0 , … , 0 ) {\displaystyle \delta _{c}=(0,0,\dots ,0,\underbrace {1} _{c{\text{-th entry}}},0,\dots ,0)} , they made the model predict the smoothed distribution ( 1 − ϵ ) δ c + ϵ / K {\displaystyle (1-\epsilon )\delta _{c}+\epsilon /K} where K {\displaystyle K} is the total number of classes. === Inception v4 === In 2017, the team released Inception v4, Inception ResNet v1, and Inception ResNet v2. Inception v4 is an incremental update with even more factorized convolutions, and other complications that were empirically found to improve benchmarks. Inception ResNet v1 and v2 are both modifications of Inception v4, where residual connections are added to each Inception module, inspired by the ResNet architecture. === Xception === Xception ("Extreme Inception") was published in 2017. It is a linear stack of depthwise separable convolution layers with residual connections. The design was proposed on the hypothesis that in a CNN, the cross-channels correlations and spatial correlations in the feature maps can be entirely decoupled. Training each network took 3 days on 60 K80 GPUs, or approximately 0.5 petaFLOP-days.
Outline of electronics
The following outline is provided as an overview of and topical guide to electronics: Electronics – branch of physics, engineering and technology dealing with electrical circuits that involve active semiconductor components and associated passive interconnection technologies. == Branches == === Classical electronics === Analog electronics Digital electronics Electronic instrumentation Electronic engineering Microelectronics Optoelectronics Power electronics Printed electronics Semiconductor technology Schematic capture Thermal management Automation Electronics === Advanced topics === Atomtronics Bioelectronics Failure modes of electronics Flexible electronics Low-power electronics Microelectromechanical systems (MEMS) Molecular electronics Nanoelectronics Organic electronics Photonics Piezotronics Quantum electronics Spintronics === History of electronics === History of electronic engineering History of radar History of radio History of television == General concepts == === Data converters === Analog-to-digital converters (ADC) Aliasing Successive approximation ADC Dual-slope ADC Quantization Sensor resolution Sampling Delta-sigma ADC Digital-to-analog converters (DAC) Digital potentiometer Binary weighted resistor converter Charge distribution DAC Pulse width modulator Reconstruction filter The R2R ladder === Digital electronics === Binary decision diagrams Boolean algebra Combinational logic Counters (digital) De Morgan's laws Digital circuit Formal verification Karnaugh maps Logic families Logic gate Logic minimization Logic simulation Logic synthesis Registers Sequential logic State machines Truth tables Transparent latch === Electrical element/discretes === Passive elements: Capacitor Inductor Memristor Resistor Transformer Active elements: Diode Zener diode Light-emitting diode PIN diode Schottky diode Avalanche diode Laser diode Microcontroller Operational amplifier Thyristor DIAC TRIAC IGBT Transistor Bipolar transistor (BJT) Field effect transistor (FET) Darlington transistor Other components Aural devices Battery (electricity) Crystal oscillator Electromechanical devices Sensors Surface acoustic wave (SAW) === Electronics analysis === Electronic packaging Electronic circuit simulation Electronic design automation Electronic noise Mathematical methods in electronics Thermal management of electronic devices and systems === Electronic circuits === Amplifiers Differential amplifiers Feedback amplifiers Power amplifiers Comparators Converters Filters Active filters Passive filters Digital filters Oscillators Phase-locked loops Timers === Electronic equipment === Air conditioner Breathalyzer Central heating Clothes dryer Computer/Notebook Dishwasher Freezer Home robot Home entertainment system Information technologies Cooker Microwave oven Refrigerator Robotic vacuum cleaner Tablet Telephone Water heater Washing machine === Television === Analog television History of television Television show Television broadcaster Timeline of the introduction of television in countries Mechanical television Color television Digital television Digital television transition Smart television Streaming television Internet Protocol television 3D television Terrestrial television ==== Television broadcasting ==== === Electronic instrumentation === Ammeter Capacitance meter Distortionmeter Electric energy meter LCR meter Microwave power meter Multimeter Network analyzer Ohmmeter Oscilloscope Psophometer Q meter Signal analyzer Signal generator Spectrum analyzer Transistor tester Tube tester Wattmeter Vectorscope Video signal generator Voltmeter VU meter === Memory technology === Flash memory Hard drive systems Optical storage Probe Storage Programmable read-only memory Read-only memory Solid-state drive (SSD) Volatile memory === Microcontrollers === Features Analog-to-digital converter Central processing unit (CPU) Clock generator (Quartz timing crystal, resonator or RC circuit) Debugging support Digital-to-analog converters Discrete input and output bits In-circuit programming Non-volatile memory (ROM, EPROM, EEPROM or Flash) Peripherals (Timers, event counters, PWM generators, and watchdog) Serial interface (Input/output such as serial ports (UARTs)) Serial communications (I²C, Serial Peripheral Interface and Controller Area Network) Volatile memory (RAM) 8-bit microcontroller families: AVR - PIC - COP8 - MCS-48 - MCS-51 - Z8 - eZ80 - HC08 - HC11 - H8 - PSoC Some notable suppliers: ARM Atmel Cypress Semiconductor Freescale Intel MIPS Microchip Technology NXP Semiconductors Parallax Propeller PowerPC Rabbit 2000 Renesas RX, V850 Silicon Laboratories STMicroelectronics Texas Instruments Toshiba TLCS === Optoelectronics === Optical fiber Optical properties Optical receivers Optical system design Optical transmitters === Physical laws === Ampère's law Coulomb's law Faraday's law of induction/Faraday-Lenz law Gauss's law Kirchhoff's circuit laws Current law Voltage law Maxwell's equations Gauss's law Faraday's law of induction Ampère's law Ohm's law === Power electronics === Power Devices Gate turn-off thyristor MOS-controlled thyristor (MCT) Power BJT/MOSFET Static induction devices Electric power conversion DC to DC DC to DC converter Voltage stabiliser Linear regulator AC to DC Rectifier Mains power supply unit (PSU) Switched-mode power supply DC to AC Inverter AC to AC Cycloconverter Transformer Variable frequency transformer Voltage converter Voltage regulator Power applications Automotive applications Capacitor charging applications Electronic ballasts Energy harvesting technologies Flexible AC transmission systems (FACTS) High frequency inverters HVDC transmission Motor controller Photovoltaic system Conversion Power factor correction circuits Power supply Renewable energy sources Switching power converters Uninterruptible power supply Wind power === Programmable devices === Application-specific integrated circuit (ASIC) Complex programmable logic device (CPLD) Erasable programmable logic device (EPLD) Simple programmable logic device (SPLD) Macrocell array Programmable array logic (PAL) Programmable logic array (PLA) Programmable logic device (PLD) Field-programmable gate array (FPGA) VHSIC Hardware Description Language (VHDL) Verilog Hardware Description Language Some notable suppliers: Altera - Atmel - Cypress Semiconductor - Lattice Semiconductor - Xilinx === Semiconductors theory === Properties Bipolar junction transistors Capacitance voltage profiling Charge carrier Charge-transfer complex Deep-level transient spectroscopy Depletion region Density of states Diode modelling Direct band gap Electronic band structure Energy level Exciton Field-effect transistors Metal–semiconductor junction MOSFETs N-type semiconductor Organic semiconductors P–n junction P-type semiconductor Photoelectric effect Quantum tunneling Semiconductor chip Semiconductor detector Solar cell Transistor model Thin film Tight-binding model Device Fabrication Semiconductor device fabrication Semiconductor industry Semiconductor consolidation == Applications == Audio electronics Automotive electronics Avionics Control Systems Consumer electronics Data acquisition E-health Electronic book Electronics industry Electronic warfare Embedded systems Home automation Integrated circuits Marine electronics Microwave technology Military electronics Multimedia Nuclear electronics Open hardware Radar and Radionavigation Radio electronics Terahertz technology Video hardware Wired and Wireless Communications
Full30
Full30 was an American online video-sharing platform primarily dedicated to firearms and shooting sports-related content. The service was established in 2014 by Tim Harmsen and Mark Hammonds as a result of YouTube's increasing restrictions on gun-related videos. == History == After the 2018 Parkland high school shooting, many companies attempted to distance themselves from any association with the firearms industry. As a result, YouTube began demonetizing and sometimes outright deleting firearms-related videos, and in one case, popular YouTube poster Hickok45's channel was completely deleted but later restored. In response, Harmsen, who operates the Military Arms Channel on YouTube, decided to create his own video-hosting website to allow himself and other firearms content creators a platform free from such restrictions; he named the website Full30 — a reference to the popular 30-round STANAG magazine. In July 2020, site representatives announced the site had new ownership. By the end of 2022, the site began to be redirected to a series of other websites. By 2025, it was largely deactivated with the front page replaced by a form to be filled out to receive "updates", with no other explanation. == Contributors == Hickok45 Military Arms Channel Forgotten Weapons Bavarian Shooter Liberty Doll CloverTac
Ambient awareness
Ambient awareness (AmA) is a term used by social scientists to describe a form of peripheral social awareness through social media. This awareness is propagated from relatively constant contact with one's friends and colleagues via social networking platforms on the Internet. The term essentially defines the sort of omnipresent knowledge one experiences by being a regular user of these media outlets that allow a constant connection with one's social circle. According to Clive Thompson of The New York Times, ambient awareness is "very much like being physically near someone and picking up on mood through the little things; body language, sighs, stray comments". Academic Andreas Kaplan defines ambient awareness as "awareness created through regular and constant reception, and/or exchange of information fragments through social media". Two friends who regularly follow one another's digital information can already be aware of each other's lives without actually being physically present to have had a conversation. == Social == Socially speaking, ambient awareness and social media are products of the new generations who are being born or growing up in the digital age, starting circa 1998 and running to current times. Social media is personal media (what you're doing in the moment, how you feel, a picture of where you are) combined with social communication. Social media is the lattice work for ambient awareness. Without social media the state of ambient awareness cannot exist. 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 user's interactions and experiences within social networks. ASNI is expected to evolve rapidly, influencing how we interact online and shaping their digital experiences. Transparency, ethical considerations, media influence bias, and user control over data will be crucial to ensure responsible development and positive impact. A significant feature of social media is that it is created by those who also consume it. Mostly, those participating in this phenomenon are adolescents, college age, or young adult professionals. According to Dr. Mimi Ito, a cultural anthropologist and Professor in Residence at the University of California at Irvine, the mobile device is the greatest proxy device used to create and distribute Social Media. She reportedly states that "teenagers capture and produce their own media, and stay in constant ambient contact with each other..." using mobile devices. Usually while doing this they are consuming other forms of media such as music or video content via their smart phones, tablets, or other similar devices. Effectively this has led social scientists to believe that learning and multitasking will have a new face as the products of the digital generation enter the work force and begin to integrate their learning methods into the standard preexisting business models of today. Professors Kaplan and Haenlein see ambient awareness as one of the major reasons for the success of such microblogging sites as Twitter. == Origins == The earliest available technology that could be used for constant social contact is the cell phone. For the first time, people could be contacted readily and at will beyond the confines of their work or homes. Then later, with the additional service of texting, one can see the somewhat primitive form of the status update. Since the text message only allows for 160 characters to transmit pertinent information it paved the way for the status update as we know it today. The transition from only having a few points of regular long distance contact, to being constantly available via cell phone, is what primed society for social networking websites. Perhaps the first instance where these websites created the possibility of larger scale ambient awareness was when Facebook installed the news feed. The news feed automatically sends compiled information on all of a users contacts activities directly to them so that they can access all of the happenings in their world from one location. For the first time, becoming someone's Facebook friend was the equivalent of subscribing to a feed of their daily minutiae. Since this innovation, a new wave of micro-blogging services have emerged, such as Twitter or Tumblr. Although these services have often been criticized as containing seemingly meaningless snippets of information, when a follower gathers a certain amount of information, they begin to obtain an ambient understanding of who they are following. This has led to the mass usage of social media as not only a social tool but also as a marketing and business tool. == Uses in marketing == Websites such as Twitter, YouTube, Facebook, and Myspace, among many others, have been used by people in all forms of business to create a closer digital/ambient bond with their clientele base. This is most notably seen in the music industry where social media networking has become the mainstay of all advertising for independent and major artists. The effect of this type of ambient marketing is that the consumer begins to get a sense of the artist's life style and personality. In this way social media outlets and ambient awareness have managed to tighten the gap between consumers and producers in all areas of business. == Uses in business processes == As web-based collaboration tools and social project management suites proliferate, the addition of activity streams to those products help to create business context-specific ambient awareness, and produce a new class of products, such as social project management platforms.
Data preprocessing
Data preprocessing can refer to manipulation, filtration or augmentation of data before it is analyzed, and is often an important step in the data mining process. Data collection methods are often loosely controlled, resulting in out-of-range values, impossible data combinations, and missing values, amongst other issues. Preprocessing is the process by which unstructured data is transformed into intelligible representations suitable for machine-learning models. This phase of model deals with noise in order to arrive at better and improved results from the original data set which was noisy. This dataset also has some level of missing value present in it. The preprocessing pipeline used can often have large effects on the conclusions drawn from the downstream analysis. Thus, representation and quality of data is necessary before running any analysis. If there is a high proportion of irrelevant and redundant information present or noisy and unreliable data, then knowledge discovery during the training phase may be more difficult. Data preparation and filtering steps can take a considerable amount of processing time. Examples of methods used in data preprocessing include cleaning, instance selection, normalization, one-hot encoding, data transformation, feature extraction and feature selection. == Applications == === Data mining === Data preprocessing allows for the removal of unwanted data with the use of data cleaning, this allows the user to have a dataset to contain more valuable information after the preprocessing stage for data manipulation later in the data mining process. Editing such dataset to either correct data corruption or human error is a crucial step to get accurate quantifiers like true positives, true negatives, false positives and false negatives found in a confusion matrix that are commonly used for a medical diagnosis. Users are able to join data files together and use preprocessing to filter any unnecessary noise from the data which can allow for higher accuracy. Users use Python programming scripts accompanied by the pandas library which gives them the ability to import data from a comma-separated values as a data-frame. The data-frame is then used to manipulate data that can be challenging otherwise to do in Excel. Pandas (software) which is a powerful tool that allows for data analysis and manipulation; which makes data visualizations, statistical operations and much more, a lot easier. Many also use the R programming language to do such tasks as well. The reason why a user transforms existing files into a new one is because of many reasons. Aspects of data preprocessing may include imputing missing values, aggregating numerical quantities and transforming continuous data into categories (data binning). More advanced techniques like principal component analysis and feature selection are working with statistical formulas and are applied to complex datasets which are recorded by GPS trackers and motion capture devices. === Semantic data preprocessing === Semantic data mining is a subset of data mining that specifically seeks to incorporate domain knowledge, such as formal semantics, into the data mining process. Domain knowledge is the knowledge of the environment the data was processed in. Domain knowledge can have a positive influence on many aspects of data mining, such as filtering out redundant or inconsistent data during the preprocessing phase. Domain knowledge also works as constraint. It does this by using working as set of prior knowledge to reduce the space required for searching and acting as a guide to the data. Simply put, semantic preprocessing seeks to filter data using the original environment of said data more correctly and efficiently. There are increasingly complex problems which are asking to be solved by more elaborate techniques to better analyze existing information. Instead of creating a simple script for aggregating different numerical values into a single value, it make sense to focus on semantic based data preprocessing. The idea is to build a dedicated ontology, which explains on a higher level what the problem is about. In regards to semantic data mining and semantic pre-processing, ontologies are a way to conceptualize and formally define semantic knowledge and data. The Protégé (software) is the standard tool for constructing an ontology. In general, the use of ontologies bridges the gaps between data, applications, algorithms, and results that occur from semantic mismatches. As a result, semantic data mining combined with ontology has many applications where semantic ambiguity can impact the usefulness and efficiency of data systems. Applications include the medical field, language processing, banking, and even tutoring, among many more. There are various strengths to using a semantic data mining and ontological based approach. As previously mentioned, these tools can help during the per-processing phase by filtering out non-desirable data from the data set. Additionally, well-structured formal semantics integrated into well designed ontologies can return powerful data that can be easily read and processed by machines. A specifically useful example of this exists in the medical use of semantic data processing. As an example, a patient is having a medical emergency and is being rushed to hospital. The emergency responders are trying to figure out the best medicine to administer to help the patient. Under normal data processing, scouring all the patient’s medical data to ensure they are getting the best treatment could take too long and risk the patients’ health or even life. However, using semantically processed ontologies, the first responders could save the patient’s life. Tools like a semantic reasoner can use ontology to infer the what best medicine to administer to the patient is based on their medical history, such as if they have a certain cancer or other conditions, simply by examining the natural language used in the patient's medical records. This would allow the first responders to quickly and efficiently search for medicine without having worry about the patient’s medical history themselves, as the semantic reasoner would already have analyzed this data and found solutions. In general, this illustrates the incredible strength of using semantic data mining and ontologies. They allow for quicker and more efficient data extraction on the user side, as the user has fewer variables to account for, since the semantically pre-processed data and ontology built for the data have already accounted for many of these variables. However, there are some drawbacks to this approach. Namely, it requires a high amount of computational power and complexity, even with relatively small data sets. This could result in higher costs and increased difficulties in building and maintaining semantic data processing systems. This can be mitigated somewhat if the data set is already well organized and formatted, but even then, the complexity is still higher when compared to standard data processing. Below is a simple a diagram combining some of the processes, in particular semantic data mining and their use in ontology. The diagram depicts a data set being broken up into two parts: the characteristics of its domain, or domain knowledge, and then the actual acquired data. The domain characteristics are then processed to become user understood domain knowledge that can be applied to the data. Meanwhile, the data set is processed and stored so that the domain knowledge can applied to it, so that the process may continue. This application forms the ontology. From there, the ontology can be used to analyze data and process results. Fuzzy preprocessing is another, more advanced technique for solving complex problems. Fuzzy preprocessing and fuzzy data mining make use of fuzzy sets. These data sets are composed of two elements: a set and a membership function for the set which comprises 0 and 1. Fuzzy preprocessing uses this fuzzy data set to ground numerical values with linguistic information. Raw data is then transformed into natural language. Ultimately, fuzzy data mining's goal is to help deal with inexact information, such as an incomplete database. Currently fuzzy preprocessing, as well as other fuzzy based data mining techniques see frequent use with neural networks and artificial intelligence.
Influencer speak
Influencer speak is a speech pattern commonly associated with English-speaking digital content creators, particularly on platforms such as TikTok. This style is characterized by linguistic features such as uptalk, where intonation rises at the end of declarative sentences, and vocal fry, a low, creaky vibration in speech. These features are often used to engage audiences. == Characteristics == Influencer speak is commonly associated with: Uptalk – a rising intonation at the end of statements Vocal fry – a creaky sound often occurring at the end of sentences Use of filler words and slang – contributes to a conversational tone that resonates with audiences == Origins == The origins of "influencer speak" are linked to the "Valley Girl" accent, which became prominent in the 1980s. This earlier style included features such as uptalk and vocal fry, which have been adapted for digital platforms. Linguists have noted that these patterns are often led by young women, who are recognized as linguistic innovators in sociolinguistic research. == Sociolinguistic significance == "Influencer speak" is used to maintain audience engagement. Features such as uptalk help speakers retain the "conversational floor," ensuring continuous attention from listeners. A study conducted by UCLA researchers has shown that creators adjust their speech styles based on the platform and audience. For example, a comedic tone may be emphasized on TikTok, while a more professional tone may be used on platforms such as LinkedIn or YouTube.