AI Data Privacy Concerns

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

  • Mobile simulator

    Mobile simulator

    A mobile simulator is a software application for a personal computer which creates a virtual machine version of a mobile device, such as a mobile phone, iPhone, other smartphone, or calculator, on the computer. This may sometimes also be termed an emulator. The mobile simulator allows the user to use features and run applications on the virtual mobile on their computer as though it was the actual mobile device. A mobile simulator lets you test a website and determine how well it performs on various types of mobile devices. A good simulator tests mobile content quickly on multiple browsers and emulates several device profiles simultaneously. This allows analysis of mobile content in real-time, locate errors in code, view rendering in an environment that simulates the mobile browser, and optimize the site for performance. Mobile simulators may be developed using programming languages such as Java, .NET and JavaScript.

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  • Herbrand Award

    Herbrand Award

    The Herbrand Award for Distinguished Contributions to Automated Reasoning is an award given by the Conference on Automated Deduction (CADE), Inc., (although it predates the formal incorporation of CADE) to honour persons or groups for important contributions to the field of automated deduction. The award is named after the French scientist Jacques Herbrand and given at most once per CADE or International Joint Conference on Automated Reasoning (IJCAR). It comes with a prize of US$1,000. Anyone can be nominated, the award is awarded after a vote among CADE trustees and former recipients, usually with input from the CADE/IJCAR programme committee. == Recipients == Past award recipients are: === 1990s === Larry Wos (1992) Woody Bledsoe (1994) John Alan Robinson (1996) Wu Wenjun (1997) Gérard Huet (1998) Robert S. Boyer and J Strother Moore (1999) === 2000s === William W. McCune (2000) Donald W. Loveland (2001) Mark E. Stickel (2002). Peter B. Andrews (2003) Harald Ganzinger (2004) Martin Davis (2005) Wolfgang Bibel (2006) Alan Bundy (2007) Edmund M. Clarke (2008) Deepak Kapur (2009) === 2010s === David Plaisted (2010) Nachum Dershowitz (2011) Melvin Fitting (2012) C. Greg Nelson (2013) Robert L. Constable (2014) Andrei Voronkov (2015) Zohar Manna and Richard Waldinger (2016) Lawrence C. Paulson (2017) Bruno Buchberger (2018) Nikolaj Bjørner and Leonardo de Moura (2019) === 2020s === Franz Baader (2020) Tobias Nipkow (2021) Natarajan Shankar (2022) Moshe Vardi (2023) Armin Biere (2024) Aart Middeldorp (2025)

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  • Whisper (speech recognition system)

    Whisper (speech recognition system)

    Whisper is a machine learning model for speech recognition and transcription, created by OpenAI and first released as open-source software in September 2022. It is capable of transcribing speech in English and multiple other languages, and can translate several non-English languages into English. Whisper is a weakly-supervised deep learning acoustic model, made using an encoder-decoder transformer architecture. OpenAI claims that the combination of different training data and post-training filtering used in its development has led to improved recognition of accents, background noise, and jargon compared to previous approaches. While the model does not outperform larger, more specialized models and still experiences AI hallucination, it has been showed to be useful for general sound recognition and has many applications across different industries. == Background == Speech recognition has had a long history in research; the first approaches made use of statistical methods, such as dynamic time warping, and later hidden Markov models. At around the 2010s, deep neural network approaches became more common for speech recognition models, which were enabled by the availability of large datasets ("big data") and increased computational performance. Early approaches to deep learning in speech recognition included convolutional neural networks, which were limited due to their inability to capture sequential data, which later led to developments of Seq2seq approaches, which include recurrent neural networks, which made use of long short-term memory. Transformers, introduced in 2017 by Google, displaced many prior state-of-the-art approaches across a wide range in machine learning, and started becoming the core neural architecture in fields such as language modeling and computer vision. Weakly-supervised approaches to training acoustic models were recognized in the early 2020s as promising for speech recognition approaches using deep neural networks. According to a NYT report, in 2021 OpenAI believed they exhausted sources of higher-quality data to train their large language models and decided to complement scraped web text with transcriptions of YouTube videos and podcasts, and developed Whisper to solve this task. Whisper Large V2 was released on December 8, 2022, followed by Whisper Large V3 being released in November 2023, during the OpenAI Dev Day. In March 2025, OpenAI released new transcription models based on GPT-4o and GPT-4o mini, both of which have lower error rates than Whisper. == Architecture == The Whisper architecture is based on an encoder-decoder transformer. Input audio is resampled to 16,000 Hertz (Hz) and converted to an 80-channel Log-magnitude Mel spectrogram using 25 ms windows with a 10 ms stride. The spectrogram is then normalized to a [-1, 1] range with near-zero mean. The encoder takes this Mel spectrogram as input and processes it. It first passes through two convolutional layers. Sinusoidal positional embeddings are added. It is then processed by a series of Transformer encoder blocks (with pre-activation residual connections). The encoder's output is layer normalized. The decoder is a standard transformer decoder. It has the same width and Transformer blocks as the encoder. It uses learned positional embeddings and tied input-output token representations (using the same weight matrix for both the input and output embeddings). It uses a byte-pair encoding tokenizer, of the same kind as used in GPT-2. English-only models use the GPT-2 vocabulary, while multilingual models employ a re-trained multilingual vocabulary with the same number of words. Special tokens are used to allow the decoder to perform multiple tasks: Tokens that denote language (one unique token per language). Tokens that specify task (<|transcribe|> or <|translate|>). Tokens that specify if no timestamps are present (<|notimestamps|>). If the token is not present, then the decoder predicts timestamps relative to the segment, and quantized to 20 ms intervals. <|nospeech|> for voice activity detection. <|startoftranscript|>, and <|endoftranscript|> . Any text that appears before <|startoftranscript|> is not generated by the decoder, but given to the decoder as context. Loss is only computed over non-contextual parts of the sequence, i.e. tokens between these two special tokens. == Training data == The training dataset consists of 680,000 hours of labeled audio-transcript pairs sourced from the internet using semi-supervised learning. This includes 117,000 hours in 96 non-English languages and 125,000 hours of X→English translation data, where X stands for any non-English language. Preprocessing involved standardization of transcripts, filtering to remove machine-generated transcripts using heuristics (e.g., punctuation, capitalization), language identification and matching with transcripts, fuzzy deduplication, and deduplication with evaluation datasets to avoid data contamination. Speechless segments were also included to allow voice activity detection training. For the files still remaining after the filtering process, audio files were then broken into 30-second segments paired with the subset of the transcript that occurs within that time. If this predicted spoken language differed from the language of the text transcript associated with the audio, that audio-transcript pair was not used for training the speech recognition models, but instead for training translation. The model was trained using the AdamW optimizer with gradient norm clipping and a linear learning rate decay with warmup, with batch size 256 segments. Training proceeded for 1 million updates (approximately 2-3 epochs). No data augmentation or regularization, except for the Large V2 model, which used SpecAugment, Stochastic Depth, and BPE Dropout. The training used data parallelism with float16, dynamic loss scaling, and activation checkpointing. === Post-training filtering === After training the first model, researchers ran it on different subsets of the training data, each representing a distinct source. Data sources were ranked by a combination of their error rate and size. Manual inspection of the top-ranked sources (high error, large size) helped determine if the source was low quality (e.g., partial transcriptions, inaccurate alignment). After training, it was fine-tuned to suppress the prediction of speaker names and low-quality sources were then removed. == Capacity == While Whisper does not outperform models which specialize in the LibriSpeech dataset, when tested across many datasets, it is more robust and makes 55.2% fewer errors than other models. Whisper has a differing error rate with respect to transcribing different languages, with a higher word error rate in languages not well-represented in the training data. The authors found that multi-task learning improved overall performance compared to models specialized to one task. They conjectured that the best Whisper model trained is still underfitting the dataset, and larger models and longer training can result in better models. Third-party evaluations have found varying levels of AI hallucination. A study of transcripts of public meetings found hallucinations in eight out of every 10 transcripts, while an engineer discovered hallucinations in "about half" of 100 hours of transcriptions and a developer identified them in "nearly every one" of 26,000 transcripts. A study of 13,140 short audio segments (averaging 10 seconds) found 187 hallucinations (1.4%), 38% of which generated text that could be harmful because it inserted false references to things like race, non-existent medications, or violent events that were not in the audio. == Applications == The model has been used as the base for many applications, such as a unified model for speech recognition and more general sound recognition. Whisper has also been integrated into the workflow of biomedical research. In 2025, a study on Alzheimer's disease detection used the model to transcribe spontaneous speech recordings. The transcripts that were generated by the model were combined with LLM vector embeddings and traditional classifiers to help classify the patients' health. Another application is when OVALYTICS incorporated Whisper to transcribe YouTube videos and automate content moderation systems, which improved its detection of offensive content. The model has also been used in academic libraries and cultral heritage institutions to generate transcripts and captions for their digitized audiovisual collections. In a 2025 case study, Emory University Libraries found that Whisper reduced the labor used in transcription by around 30-35%, shifting work from text creation to text correction. However, human review is still necessary to make sure accuracy, formatting, and accessibility are all standard.

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  • WebCrow

    WebCrow

    The WebCrow is a research project carried out at the Information Engineering Department of the University of Siena with the purpose of automatically solving crosswords. == The Project == The scientific relevance of the project can be understood considering that cracking crosswords requires human-level knowledge. Unlike chess and related games and there is no closed world configuration space. A first nucleus of technology, such as search engines, information retrieval, and machine learning techniques enable computers to enfold with semantics real-life concepts. The project is based on a software system whose major assumption is to attack crosswords making use of the Web as its primary source of knowledge. WebCrow is very fast and often thrashes human challengers in competitions, especially on multi language crossword schemes. A distinct feature of the WebCrow software system is to combine properly natural language processing (NLP) techniques, the Google web search engine, and constraint satisfaction algorithms from artificial intelligence to acquire knowledge and to fill the schema. The most important component of WebCrow is the Web Search Module (WSM), which implements a domain specific web based question answering algorithm. The way WebCrow approaches crosswords solving is quite different with respect to humans: Whereas we tend to first answer clues we are sure of and then proceed filling the schema by exploiting the already answered clues as hints, WebCrow uses two clearly distinct stages. In the first one, it processes all the clues and tries to answer them all: For each clue it finds many possible candidates and sorts them according to complex ranking models mainly based on a probability criteria. In the second stage, WebCrow uses constraint satisfaction algorithms to fill the grid with the overall most likely combination of clue answers. In order to interact with Google, first of all, WebCrow needs to compose queries on the basis of the given clues. This is done by query expansion, whose purpose is to convert the clue into a query expressed by a simplified and more appropriate language for Google. The retrieved documents are parsed so as to extract a list of word candidates that are congruent with the crossword length constraints. Crosswords can hardly be faced by using encyclopedic knowledge only, since many clues are wordplays or are otherwise purposefully very ambiguous. This enigmatic component of crosswords is faced by a massive use of database of solved crosswords, and by automatic reasoning on a properly organized knowledge base of wired rules. Last but not the least, the final constraint satisfaction step is very effective to fill the correct candidate, even though, unlike humans, the system can not rely on very high confidence on the correctness of the answer. == Competitions == WebCrow speed and effectiveness has been tested many times in man-machine competitions on Italian, English and multi-language crosswords The outcome of the tests is that WebCrow can successfully compete with average human players on single language schemes and reaches expert level performance in multi-language crosswords. However, WebCrow has not reached expert level in single-language crosswords, yet. === ECAI-06 Competition === On August 30, 2006, at the European Conference on Artificial Intelligence (ECAI2006), 25 conference attendees and 53 internet connected crosswords lovers, competed with WebCrow in an official challenge organized within the conference program. The challenge consisted in 5 different crosswords (2 in Italian, 2 in English and one multi-language in Italian and English) and 15 minutes were assigned for each crossword. WebCrow ranked 21 out of 74 participants in the Italian competition, and won both the bilingual and English competitions. === Other Competitions === Several competitions have been held in Florence, Italy within the Creativity Festival in December 2006, and another official conference competition took place in Hyderabad, India in January 2007, within the International Conference of Artificial Intelligence, where it ranked second out of 25 participants.

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  • Metadata repository

    Metadata repository

    A metadata repository is a database created to store metadata. Metadata is information about the structures that contain the actual data. Metadata is often said to be "data about data", but this is misleading. Data profiles are an example of actual "data about data". Metadata adds one layer of abstraction to this definition– it is data about the structures that contain data. Metadata may describe the structure of any data, of any subject, stored in any format. A well-designed metadata repository typically contains data far beyond simple definitions of the various data structures. Typical repositories store dozens to hundreds of separate pieces of information about each data structure. Comparing the metadata of a couple data items - one digital and one physical - clarify what metadata is: First, digital: For data stored in a database one may have a table called "Patient" with many columns, each containing data which describes a different attribute of each patient. One of these columns may be named "Patient_Last_Name". What is some of the metadata about the column that contains the actual surnames of patients in the database? We have already used two items: the name of the column that contains the data (Patient_Last_Name) and the name of the table that contains the column (Patient). Other metadata might include the maximum length of last name that may be entered, whether or not last name is required (can we have a patient without Patient_Last_Name?), and whether the database converts any surnames entered in lower case to upper case. Metadata of a security nature may show the restrictions which limit who may view these names. Second, physical: For data stored in a brick and mortar library, one have many volumes and may have various media, including books. Metadata about books would include ISBN, Binding_Type, Page_Count, Author, etc. Within Binding_Type, metadata would include possible bindings, material, etc. This contextual information of business data include meaning and content, policies that govern, technical attributes, specifications that transform, and programs that manipulate. == Definition == The metadata repository is responsible for physically storing and cataloging metadata. Data in a metadata repository should be generic, integrated, current, and historical: Generic Meta model should store the metadata by generic terms instead of storing it by an applications-specific defined way, so that if your data base standard changes from one product to another the physical meta model of the metadata repository would not need to change. Integration of the metadata repository allows all business areas' metadata to be in an integrated fashion: Covering all domains and subject areas of the organization. current and historical The metadata repository should have accessible current and historical metadata. Metadata repositories used to be referred to as a data dictionary. With the transition of needs for the metadata usage for business intelligence has increased so is the scope of the metadata repository increased. Earlier data dictionaries are the closest place to interact technology with business. Data dictionaries are the universe of metadata repository in the initial stages but as the scope increased Business glossary and their tags to variety of status flags emerged in the business side while consumption of the technology metadata, their lineage and linkages made the repository, the source for valuable reports to bring business and technology together and helped data management decisions easier as well as assess the cost of the changes. Metadata repository explores the enterprise wide data governance, data quality and master data management (includes master data and reference data) and integrates this wealth of information with integrated metadata across the organization to provide decision support system for data structures, even though it only reflects the structures consumed from various systems. == Repository vs. registry == Repository has additional functionalities compared with registry. Metadata repository not only stores metadata like Metadata registry but also adds relationships with related metadata types. Metadata when related in a flow from its point of entry into organization up to the deliverables is considered as the lineage of that data point. Metadata when related across other related metadata types is called linkages. By providing the relationships to all the metadata points across the organization and maintaining its integrity with an architecture to handle the changes, metadata repository provides the basic material for understanding the complete data flow and their definitions and their impact. Also the important feature is to maintain the version control though this statement for contrasting is open for discussion. These definitions are still evolving, so the accuracy of the definitions needs refinement. The purpose of registry is to define the metadata element and maintained across the organization. And data models and other data management teams refer to the registry for any changes to follow. While Metadata repository sources metadata from various metadata systems in the organizations and reflects what is in the upstream. Repository never acts as an upstream while registry is used as an upstream for metadata changes. == Reason for use == Metadata repository enables all the structure of the organizations data containers to one integrated place. This opens plethora of resourceful information for making calculated business decisions. This tool uses one generic form of data model to integrate all the models thus brings all the applications and programs of the organization into one format. And on top of it applying the business definitions and business processes brings the business and technology closer that will help organizations make reliable roadmaps with definite goals. With one stop information, business will have more control on the changes, and can do impact analysis of the tool. Usually business spends much time and money to make decisions based on discovery and research on impacts to make changes or to add new data structures or remove structures in data management of the organization. With a structured and well maintained repository, moving the product from ideation to delivery takes the least amount of time (considering other variables are constant). To sum it up: Integration of the metadata across the organization Build relationship between various metadata types Build relationship between various disparate systems Define business golden copy of definitions Version control of the changes at structure level Interaction with Reference data Link view to master data Automatic synchronization with various authorized metadata source systems More control to business decisions Validate the structures by overlapping the models Discovering discrepancies, gaps, lineage, metrics at data structure level Each database management system (DBMS) and database tools have their own language for the metadata components within. Database applications already have their own repositories or registries that are expected to provide all of the necessary functionality to access the data stored within. Vendors do not want other companies to be capable of easily migrating data away from their products and into competitors products, so they are proprietary with the way they handle metadata. CASE tools, DBMS dictionaries, ETL tools, data cleansing tools, OLAP tools, and data mining tools all handle and store metadata differently. Only a metadata repository can be designed to store the metadata components from all of these tools. == Design == Metadata repositories should store metadata in four classifications: ownership, descriptive characteristics, rules and policies, and physical characteristics. Ownership, showing the data owner and the application owner. The descriptive characteristics, define the names, types and lengths, and definitions describing business data or business processes. Rules and policies, will define security, data cleanliness, timelines for data, and relationships. Physical characteristics define the origin or source, and physical location. Like building a logical data model for creating a database, a logical meta model can help identify the metadata requirements for business data. The metadata repository will be centralized, decentralized, or distributed. A centralized design means that there is one database for the metadata repository that stores metadata for all applications business wide. A centralized metadata repository has the same advantages and disadvantages of a centralized database. Easier to manage because all the data is in one database, but the disadvantage is that bottlenecks may occur. A decentralized metadata repository stores metadata in multiple databases, either separated by location and or departments of the business. This makes management of the repository more involved than a centraliz

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  • Revelation Space series

    Revelation Space series

    The Revelation Space series is a book series created by Alastair Reynolds. The fictional universe is used as the setting for a number of his novels and stories. Its fictional history follows the human species through various conflicts from the relatively near future (roughly 2200) to approximately 40,000 AD (all the novels to date are set between 2427 and 2858, although certain stories extend beyond this period). It takes its name from Revelation Space (2000), which was the first published novel set in the universe. == Universe == The Revelation Space universe is a fictional universe set in a future version of our world, with the addition of a number of extraterrestrial species and advanced technologies that are not necessarily grounded in current science. It is, nonetheless, somewhat "harder" than most examples of space opera, relying to a considerable extent on science Reynolds believes to be possible; in particular, faster-than-light travel is largely absent. Reynolds has said he prefers to keep the science in his fiction plausible, but he will adopt science he believes to be impossible when it is necessary for the story. The name "Revelation Space universe" has been used by Alastair Reynolds in both the introductory text in the collections Diamond Dogs, Turquoise Days and Galactic North, and also on several editions of the novels set in the universe. He considered calling it the "Exordium universe" after a key plot device, but found that the name was already in use. While a great deal of science fiction reflects either very optimistic or dystopian visions of the human future, the Revelation Space universe is notable in that human societies have not developed to either positive or negative extremes. Instead, despite their dramatically advanced technology, they are similar to those of today in terms of their moral ambiguity and mixture of cruelty and decency, corruption and opportunity. The Revelation Space universe contains elements of Lovecraftian horror, with one posthuman entity stating explicitly that some things in the universe are fundamentally beyond human or transhuman understanding. Nevertheless, the main storyline is essentially optimistic, with humans continuing to survive even in a universe that seems fundamentally hostile to intelligent life. The name "Revelation Space" appears in the novel of the same name during Philip Lascaille's account of his visit to Lascaille's Shroud, an anomalous region of the local universe. Lascaille says that "the key" to something momentous "was explained to me [. . .] while I was in Revelation Space." === Chronology === The chronology of the Revelation Space universe extends to roughly one billion years into the past, when the "Dawn War" — a galaxy-spanning conflict over the availability of various natural resources — resulted in almost all sentient life in the galaxy being wiped out. One race of survivors, later termed the Inhibitors, having converted itself to machine form, predicted that the impending Andromeda–Milky Way collision, roughly 3 billion years in our future, may severely damage the capacity of either galaxy to support life. Consequently, they planned to adjust the positions of stars in order to limit the damage the collision would cause. Also central to the Inhibitor project was the eradication of all species above a certain technological level until the crisis was over, as they believed no organic species would be capable of co-operating on such a large-scale project (an in-universe solution to the Fermi paradox). Whilst they were relatively successful, certain advanced species were able to hide from Inhibitor forces, or even fight back. In human history, during the 21st and 22nd centuries, numerous wars occurred, and a flotilla of generation ships was deployed to colonise a planet orbiting the star 61 Cygni (which becomes a major segment of the plot of Chasm City). The flotilla later reached a planet termed Sky's Edge, which was to be embroiled in war until human civilisation there was eradicated. Meanwhile, in the Solar System in 2190, a faction known as the Conjoiners emerged as a result of increased experimentation with neural implants. In response, the Coalition for Neural Purity was formed, opposed to the Conjoiners. Nevil Clavain, one of the series's primary protagonists, fought on the side of the Coalition in the ensuing war, but defected later on after being betrayed. Clavain, and the Conjoiners, succeeded in escaping the Solar System and left for surrounding stars. For the next few centuries, the so-called Belle Epoque, humanity enjoyed a period of relative peace and prosperity, with several planets being colonised. The most successful planet of all was Yellowstone, a planet orbiting the star Epsilon Eridani, site of the Glitter Band / Rust Belt and Chasm City. Technologies developed included the Conjoiner Drive, a gift from the Conjoiners (who resumed contact with humanity at an unknown time), advanced nanotechnology, and numerous other devices. With the exception of an attempted takeover of the Glitter Band, no major incidents affected humanity during this time. The Belle Epoque was terminated by the advent of the Melding Plague in 2510, a nanotechnological virus that destroyed all other nanotechnology it came into contact with. Only the Conjoiners were unaffected by this disaster, which devastated the civilisation around Yellowstone. War between the Conjoiners and the Demarchists, a rival faction, erupted as a result of the plague. Meanwhile, activities around a far-flung human colony on the planet Resurgam, orbiting the star Delta Pavonis, inadvertently attracted the attention of the Inhibitors. The Conjoiners, also made aware of this event, sent Clavain to recover the exceedingly powerful "Cache Weapons" from this system (said weapons having been stolen from the Conjoiners centuries before) so that they could be used to fend off the Inhibitors while the Conjoiners escaped. Clavain instead defected from the Conjoiners, intending to use the weapons to protect all of humanity. Skade, another Conjoiner, was sent to stop him and recover the weapons. They fought around the Resurgam system, with Clavain and his allies eventually obtaining the weapons. Clavain's ally Remontoire agreed to seek out alien assistance from the Hades Matrix, a nearby alien computer disguised as a neutron star, whilst Clavain sheltered refugees from Resurgam on another planet, later termed Ararat. Remontoire returned in 2675, only a few days after Clavain's death at the hands of Skade, who had arrived with him. Remontoire and his allies were now at war with the Inhibitors, assisted by alien technology obtained from Hades. Even so, it was realised that the humans would not last indefinitely, and Clavain's people, now led by Scorpio, decided to seek out the mysterious "Shadows": a race believed to be near a moon called Hela, site of a theocracy. Aura, daughter of Ana Khouri (an ally of Remontoire) infiltrated the theocracy under the pseudonym Rashmika Els. After considerable conflict, Scorpio and Aura realised that contacting the Shadows was inadvisable. With the later assistance of the Conjoiner known as Glass, and of Clavain's estranged brother Warren, Scorpio and Aura (now going by the name Lady Arek) instead succeeded in contacting the Nestbuilders, an alien race who provided them with weapons capable of defeating the Inhibitors. As such, the Inhibitors were effectively eradicated from human space, with buffer zones and frontiers established to keep them at bay. Humanity then enjoyed a second, 400-year-long golden age. After this, however, came the Greenfly outbreak, in which human civilisation was destroyed by a rogue terraforming system of human origin that destroyed planets and converted them to millions of orbiting, vegetation-filled habitats. The Greenfly began to subsume most of human space, with all efforts to stop them failing, due to the Greenfly having assimilated aspects of both the Melding Plague and Inhibitor technology. The storyline of the Revelation Space universe thus far concludes with humanity leaving the Milky Way galaxy in an attempt to set up a new civilisation elsewhere. == Books and stories set in the universe == All short stories and novellas in this universe to date are collected in Galactic North and Diamond Dogs, Turquoise Days, with the exception of "Monkey Suit", "The Last Log of the Lachrimosa", "Night Passage", "Open and Shut", and "Plague Music". === The Inhibitor Sequence === Revelation Space. London: Gollancz, 2000. ISBN 978-0-575-06875-9. Redemption Ark. London: Gollancz, 2002. ISBN 978-0-575-06879-7. Absolution Gap. London: Gollancz, 2003. ISBN 978-0-575-07434-7. Inhibitor Phase. London: Gollancz, 2021. ISBN 978-0-575-09075-0. === Prefect Dreyfus Emergencies === The Prefect. London: Gollancz, 2007, ISBN 978-0-575-07716-4. (Re-released as Aurora Rising in 2017, ISBN 978-1-473-22336-3) Elysium Fire. London: Gollancz, 2018, ISBN 978-0-575-09059-0.

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  • Fragile Dreams: Farewell Ruins of the Moon

    Fragile Dreams: Farewell Ruins of the Moon

    Fragile Dreams: Farewell Ruins of the Moon (フラジール ~さよなら月の廃墟~, Furajīru: Sayonara Tsuki no Haikyo; known in Japan as Fragile) is an action role-playing game for the Wii developed by Namco Bandai Games in co-operation with Tri-Crescendo. The game was released by Namco Bandai Games in Japan on January 22, 2009. It was later published by Xseed Games in North America on March 16, 2010, and in Europe by Rising Star Games on March 19, 2010, followed by its release in Australia on April 1, 2010. == Gameplay == In Fragile Dreams, the player character, Seto, must traverse the ruins of Tokyo and the surrounding areas, fighting off ghosts that lurk within these ruins. The game's heads-up display includes a mini-map and HP gauge for Seto's location and health, respectively. Seto will fall unconscious if his HP reaches zero, resulting in a game over. The player controls Seto from a third-person perspective with the Wii Remote and Nunchuk. Seto can use his flashlight (controlled by the Wii Remote pointer) to illuminate his surroundings or solve puzzles and interact with the environment. When searching for certain objectives or hidden enemies, pointing Seto's light in their direction picks up and plays their sounds through the Wii Remote's mini speaker. The Wii Nunchuk, meanwhile, directly controls Seto's movement: aside of basic movement, he can crouch to hide and crawl through small spaces. Seto will often come across damaged floors, which require slow movement (and for heavily damaged floors, crouching) to cross without falling through. As Seto, the player can use weapons found throughout the world to fight off ghosts, ranging from slingshots and golf clubs to crossbows and katanas. Each weapon can only take a certain amount of use: once a weapon reaches its limit, it will break after battle. The player can also find other usable and collectable items in the field, marked with fireflies. The player can only save their game by resting at small fire pits scattered throughout the world: used fire pits are marked with a bonfire. The player can also examine and identify Mystery Items, organize their inventory, as well as after encountering the Merchant, buy and sell items. As stated by the producer of the game, Kentarō Kawashima, Fragile Dreams is not strictly a survival horror: rather, its story focuses on human drama. In Fragile Dreams, aside of the main story, the player can find and examine objects and graffiti throughout the world. Objects called memory items (ranging from origami and stones to cell phones and books) hold the memories of their former owners (only accessible at bonfires), while the graffiti contains messages only seen by pointing at them in first-person. By examining these messages, the player can piece together hints to the game's backstory. == Story == === Setting and characters === Fragile Dreams is set in a post-apocalyptic version of Earth in the near-future. Almost all the world's population has vanished, leaving the surviving buildings and structures abandoned. The game is set in and near the ruins of Tokyo, Japan, where the event that nearly wiped out humanity may have originated. The protagonist, Seto, is a 15-year-old boy who searches the world for other living humans. He encounters Ren, a silver-haired girl who often leaves behind large, cryptic drawings. Other characters include: Sai, the ghost of a young woman; Crow, a mischievous and straightforward amnesiac boy; Personal Frame (P.F.), a portable computer who loves having conversations more than anything else; Chiyo, the ghost of a little girl; and the Merchant, a mysterious yet merry man who trades various goods. The game's host of enemies mainly consist of ghosts, but also include humanoid robots and security proxies. The main antagonist, Shin, is the AI of a scientist who considers speech to be an inferior means of communication. Various memory items include a greater set of characters, each giving hints to the game's backstory. === Plot === At the end of Seto's fifteenth summer, his grandfather dies. Seto buries him in front of their home, an old observatory, and that from then on he became "truly alone". At night, he searches for anything the old man had left for him and discovers a letter, along with a strange blue stone in a locket. Suddenly, a mask-like ghost appears and attacks Seto. After driving the creature off, Seto reads the old man's letter, who tells him to "reach a tall red tower" east of the observatory, where he might find other survivors. After departing for the tower, Seto reaches an old subway entrance in the Azabudai district and finds Ren sitting on a collapsed pillar, singing to the stars. He accidentally startles her and the frightened Ren flees into the subway station: getting over the shock of meeting another person, Seto follows her. While searching the station, he discovers a Personal Frame, who guides him towards Ren. Unfortunately, just as they reach the exit, P.F.'s battery dies out: Seto buries the device, keeping a screw from it in his locket. From the underground, Seto finds himself at an abandoned amusement park and encounters Crow, who steals Seto's locket. After a long chase across the park and another encounter with the masked ghost, Crow returns Seto's locket and directs him to a hotel nearby, where he saw a girl who might know something about Ren. Crow also gives Seto his skull ring to keep in his locket and kisses him. At the hotel, Seto encounters Sai and fights the masked ghost again. After laying to rest the spirit of an old woman named Chiyo, the two discover Ren's drawings by a sewer. Returning to the underground, Seto and Sai find themselves at a hydropower dam. While searching for Ren, Seto discovers that Crow is actually a robot, but his battery begins to fail and Seto mourns for him as he "die[s]". Finally, they encounter Ren in a cell: although glad to see him again, Ren runs off after Shin calls. Sai explains to Seto that most of humanity died because of a "human empathy expansion project" called Glass Cage. The project was meant to make human thoughts transparent, meaning that no one would need words to communicate. However, after Glass Cage activated, people who went to sleep never woke up again. Sai reveals that she was Glass Cage's first catalyst: this time, Shin intends to use Ren as the catalyst. After exiting the dam, a demolition crane attempts to destroy it. Hearing both Shin's and the masked ghost's voices from the crane — saying, "Any threat to the project must be eliminated." — the player realizes both are manifestations of Glass Cage. After Seto destroys the crane, Sai leads him to the facility where Ren was taken to. Entering the laboratory, Seto and Sai are confronted by Shin, who coldly dismisses Sai's attempts at reasoning with him and is adamant about proceeding with his plans. As they traverse the laboratory, they overhear a voice announcing "Glass Cage Launch Preparations Complete", strengthening their resolve to save Ren. Making it into the room where Ren is being held, Shin tells them of his intention to use Glass Cage to "obliterate corporeal beings". After Seto defeats him, Shin disappears and Seto releases Ren from the device holding her. Their reunion is cut short as Sai tells them that the backup system has "finished copying her psyche to the AI", allowing Glass Cage to proceed. Ren reveals Shin has escaped to the top of the Tokyo Tower and Seto asks Ren to wait at the base of the tower and for Sai to accompany her. On his way up the tower, Seto hears the voices of P.F., Chiyo and Crow wishing him luck. He confronts and defeats Shin a second time, who reveals his motivations: he had secretly used himself as the first test subject of the human empathy expansion project and gained the ability to hear the thoughts of those around him. Despite his initial belief in the project as a way for humans to empathize with one another, all he heard around him was "jealousy and contempt" and he soon grew disillusioned with the world as even his parents turned against him. Believing no person loved him, Shin wants to put an end to humanity. His words meet with a vehement response from Sai, as she tells him that she loves him, having developed those feelings while she was the catalyst and all she ever wanted was to be part of his life. Hearing this, Shin finds peace, tossing the AI mainframe away so Glass Cage can never be reactivated and vanishes together with Sai, hand-in-hand, after thanking Seto. Descending from the tower, Seto finally learns Ren's name and they resolve to look for other survivors together. == Development == Fragile Dreams was developed by the team at Namco Bandai Games. Director and producer Kentarō Kawashima came up with the concept for the game in 2003, before the Wii console was revealed. When the Wii was unveiled, it became the obvious choice as the game's platform as the Wii remote could be used to control the flashlight. Kawashima wrote the main scenario for the title, w

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  • Dominic Harris

    Dominic Harris

    Dominic Harris (born 16 November 1976) is a British artist known for integrating modern technology and classical design in his interactive artworks. == Background == Dominic Harris was born in London on 16 November 1976, and grew up in London, Brussels, and Michigan before returning to London in 1995. Harris attended the Cranbrook Kingswood Upper School, and then trained as an architect at the Bartlett School of Architecture, and has been ARB registered since 2011. Harris designs and fabricates his artworks at Dominic Harris Studio, a multi-disciplinary practice he founded in 2007. This studio consists of 25 people with diverse backgrounds including architecture, product design, electronics, programming, graphic design, and workshop skills. Harris uses the resources of his studio for the ongoing development, prototyping and production of his artworks. Harris also oversees the studio's international projects where his fascinations are translated into larger scale projects that span residential, retail, and public art projects. In 2015, Harris was granted permission by the Walt Disney Company to use their Intellectual Property for the purpose of making new interactive artworks. Harris is the only artist to gain permission to use Disney's back catalogue of characters, and led him to creating his interactive versions of "Snow White and the Seven Dwarfs" and "Mickey and Minnie: An Interactive Diptych". Harris is fascinated by the idea of using data streams, algorithms, and computer code to generate dynamic and ever-changing artworks. He sees data as a raw material that can be transformed into visual poetry. Many of his installations and sculptures are interactive, responding to the presence and movement of viewers/participants. This creates an immersive experience where the observer becomes part of the artwork itself. Harris is also the founding partner of a sister studio in London called Cinimod Studio that creates large commissioned installations, interactive events and lighting designs for large brands. == Works == == Exhibitions == The works of Dominic Harris have been exhibited internationally, both through direct and gallery representation. Solo shows: "Feeding Consciousness" at Halcyon Gallery, Mayfair, London, UK – 2023 "US: NOW" at Halcyon Gallery, Mayfair, London, UK – 2020 "Imagine" at Halcyon Gallery, Mayfair, London, UK – 2019 "5 Year Celebration", Priveekollektie Contemporary Art | Design, London, UK – 2016. "Moments of Reflection" at PHOS ART + DESIGN, Mayfair, London, UK – 2015 Recent exhibitions include: In Plain Sight, 2024 Halcyon Gallery Victoria & Albert Museum Dublin Science Museum Design Miami / Basel Design Miami Art Miami Art 14, London PAD Paris PAD London Art Geneva == Gallery Representation == 2010 to 2019: Dominic Harris was represented by Priveekollektie Contemporary Art | Design, a Dutch gallery based in Heusden, the Netherlands, and with a regular presence on the international art and design circuits. 2015: Dominic Harris was shown with PHOS ART + DESIGN Gallery, in Mayfair, London, UK. 2019 – ongoing: Dominic Harris is exclusively represented by the Halcyon Gallery, an established international gallery based in Mayfair, London. == Collections == The majority of Harris's work has been bought by private collectors. Since 2012 Harris's work is also being acquired by several large institutional collections, including the Borusan Contemporary Art Collection in Istanbul. Harris's artworks include some of the biggest and most respected international art collectors and are also displayed in public spaces. == Books == Dominic Harris: Feeding Consciousness. Halcyon Gallery, 2023. Imagine: Dominic Harris (exhibition catalogue). Halcyon Gallery, 2019. A Touch Of Code: Documents the "Beacon" art installation and "Flutter" artwork (ISBN 978-3899553314) Dominic Harris, Artworks, Edition Eight. (ISBN 978-0957306325) Digital Real: Kunst & Nachhaltigkeit Vol 8.

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  • EasyA

    EasyA

    EasyA is a web3 technology company and education platform based in London (United Kingdom), founded in 2022 by Phil Kwok and Dom Kwok. EasyA was officially launched in 2022, focusing on web3 technologies. This community was influenced by the founders' experiences during the COVID-19 pandemic and early collaborations with universities and other educational institutions. Subsequently, the community was used as a foundation for developing Web3-related initiatives, including the organisation of EasyA's first Web3 hackathon in 2022. The EasyA app has over one million users and provides educational content on various blockchain technologies. EasyA Labs is a separate initiative focused on developing products intended to improve accessibility to cryptocurrency for a broader audience.

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  • Pop music automation

    Pop music automation

    Pop music automation is a field of study among musicians and computer scientists with a goal of producing successful pop music algorithmically. It is often based on the premise that pop music is especially formulaic, unchanging, and easy to compose. The idea of automating pop music composition is related to many ideas in algorithmic music, artificial intelligence (AI) and computational creativity. == History of automation in music == Algorithms (or, at the very least, formal sets of rules) have been used to compose music for centuries; the procedures used to plot voice-leading in counterpoint, for example, can often be reduced to algorithmic determinant. Now the term is usually reserved, however, for the use of formal procedures to make music without human intervention. Classical music automation software exists that generates music in the style of Mozart and Bach and jazz. Most notably, David Cope has written a software system called "Experiments in Musical Intelligence" (or "EMI") that is capable of analyzing and generalizing from existing music by a human composer to generate novel musical compositions in the same style. EMI's output is convincing enough to persuade human listeners that its music is human-generated to a high level of competence. Creativity research in jazz has focused on the process of improvisation and the cognitive demands that this places on a musical agent: reasoning about time, remembering and conceptualizing what has already been played, and planning ahead for what might be played next. Inevitably associated with pop music automation is pop music analysis. Projects in pop music automation may include, but are not limited to, ideas in melody creation and song development, vocal generation or improvement, automatic accompaniment and lyric composition. == Automatic accompaniment == Some systems exist that automatically choose chords to accompany a vocal melody in real-time. A user with no musical experience can create a song with instrumental accompaniment just by singing into a microphone. An example is a Microsoft Research project called Songsmith, which trains a Hidden Markov model using a music database and uses that model to select chords for new melodies. == Melody generation == Automatic melody generation is often done with a Markov chain, the states of the system become note or pitch values, and a probability vector for each note is constructed, completing a transition probability matrix (see below). An algorithm is constructed to produce an output note values based on the transition matrix weightings, which could be MIDI note values, frequency (Hz), or any other desirable metric. A second-order Markov chain can be introduced by considering the current state and also the previous state, as indicated in the second table. Higher, nth-order chains tend to "group" particular notes together, while 'breaking off' into other patterns and sequences occasionally. These higher-order chains tend to generate results with a sense of phrasal structure, rather than the 'aimless wandering' produced by a first-order system. == Lyric composition == Automated lyric creating software may take forms such as: Selecting words according to their rhythm The Tra-la-Lyrics system produces song lyrics, in Portuguese, for a given melody. This not only involves matching each word syllable with a note in the melody, but also matching the word's stress with the strong beats of the melody. Parsing existing pop music (e.g. for content or word choice) This involves natural language processing. Pablo Gervás has developed a noteworthy system called ASPERA that employs a case-based reasoning (CBR) approach to generating poetic formulations of a given input text via a composition of poetic fragments that are retrieved from a case-base of existing poems. Each poem fragment in the ASPERA case-base is annotated with a prose string that expresses the meaning of the fragment, and this prose string is used as the retrieval key for each fragment. Metrical rules are then used to combine these fragments into a well-formed poetic structure. Automatic analogy or story creation Programs like TALE-SPIN and The MINSTREL system represent a complex elaboration of this basis approach, distinguishing a range of character-level goals in the story from a range of author-level goals for the story. Systems like Bringsjord's BRUTUS can create stories with complex interpersonal themes like betrayal. On-line metaphor generation systems like 'Sardonicus' or 'Aristotle' can suggest lexical metaphors for a given descriptive goal (e.g., to describe a supermodel as skinny, the source terms “pencil”, “whip”, “whippet”, “rope”, “stick-insect” and “snake” are suggested). Free association of grouped words Using a language database (such as wordnet) one can create musings on a subject that may be weak grammatically but are still sensical. See such projects as the Flowerewolf automatic poetry generator or the Dada engine. == Software == === More or less free === BreathCube by xoxos. Simple lyrical vocal content is generated with simple music. CubeBreath by xoxos. Audio input is vocoded in tune with the music. Midi Internet Algorithmic Composition infno, infinite generator of electronic dance music and synth-pop. Algorithmic Trap, trap beat generator. === Commercial === Band in a box generates any element, potentially creates whole new songs from scratch. Musical Palette - Melody Composing Tool SongSmith: Automatic accompaniment for vocal melodies Ludwig 3.0 automatic accompaniment, writes arrangements for given instruments, plays its own songs for an infinitely long time. Automated Composing System creates music in many different styles

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  • Content-based image retrieval

    Content-based image retrieval

    Content-based image retrieval, also known as query by image content (QBIC) and content-based visual information retrieval (CBVIR), is the application of computer vision techniques to the image retrieval problem, that is, the problem of searching for digital images in large databases (see this survey for a scientific overview of the CBIR field). Content-based image retrieval is opposed to traditional concept-based approaches (see Concept-based image indexing). "Content-based" means that the search analyzes the contents of the image rather than the metadata such as keywords, tags, or descriptions associated with the image. The term "content" in this context might refer to colors, shapes, textures, or any other information that can be derived from the image itself. CBIR is desirable because searches that rely purely on metadata are dependent on annotation quality and completeness. == Comparison with metadata searching == An image meta search requires humans to have manually annotated images by entering keywords or metadata in a large database, which can be time-consuming and may not capture the keywords desired to describe the image. The evaluation of the effectiveness of keyword image search is subjective and has not been well-defined. In the same regard, CBIR systems have similar challenges in defining success. "Keywords also limit the scope of queries to the set of predetermined criteria." and, "having been set up" are less reliable than using the content itself. == History == The term "content-based image retrieval" seems to have originated in 1992 when it was used by Japanese Electrotechnical Laboratory engineer Toshikazu Kato to describe experiments into automatic retrieval of images from a database, based on the colors and shapes present. Since then, the term has been used to describe the process of retrieving desired images from a large collection on the basis of syntactical image features. The techniques, tools, and algorithms that are used originate from fields such as statistics, pattern recognition, signal processing, and computer vision. === QBIC - Query By Image Content === The earliest commercial CBIR system was developed by IBM and was called QBIC (Query By Image Content). Recent network- and graph-based approaches have presented a simple and attractive alternative to existing methods. While the storing of multiple images as part of a single entity preceded the term BLOB (Binary Large OBject), the ability to fully search by content, rather than by description, had to await IBM's QBIC. === VisualRank === == Technical progress == The interest in CBIR has grown because of the limitations inherent in metadata-based systems, as well as the large range of possible uses for efficient image retrieval. Textual information about images can be easily searched using existing technology, but this requires humans to manually describe each image in the database. This can be impractical for very large databases or for images that are generated automatically, e.g. those from surveillance cameras. It is also possible to miss images that use different synonyms in their descriptions. Systems based on categorizing images in semantic classes like "cat" as a subclass of "animal" can avoid the miscategorization problem, but will require more effort by a user to find images that might be "cats", but are only classified as an "animal". Many standards have been developed to categorize images, but all still face scaling and miscategorization issues. Initial CBIR systems were developed to search databases based on image color, texture, and shape properties. After these systems were developed, the need for user-friendly interfaces became apparent. Therefore, efforts in the CBIR field started to include human-centered design that tried to meet the needs of the user performing the search. This typically means inclusion of: query methods that may allow descriptive semantics, queries that may involve user feedback, systems that may include machine learning, and systems that may understand user satisfaction levels. == Techniques == Many CBIR systems have been developed, but as of 2006, the problem of retrieving images on the basis of their pixel content remains largely unsolved. Different query techniques and implementations of CBIR make use of different types of user queries. === Query By Example === QBE (Query By Example) is a query technique that involves providing the CBIR system with an example image that it will then base its search upon. The underlying search algorithms may vary depending on the application, but result images should all share common elements with the provided example. Options for providing example images to the system include: A preexisting image may be supplied by the user or chosen from a random set. The user draws a rough approximation of the image they are looking for, for example with blobs of color or general shapes. This query technique removes the difficulties that can arise when trying to describe images with words. === Semantic retrieval === Semantic retrieval starts with a user making a request like "find pictures of Abraham Lincoln". This type of open-ended task is very difficult for computers to perform - Lincoln may not always be facing the camera or in the same pose. Many CBIR systems therefore generally make use of lower-level features like texture, color, and shape. These features are either used in combination with interfaces that allow easier input of the criteria or with databases that have already been trained to match features (such as faces, fingerprints, or shape matching). However, in general, image retrieval requires human feedback in order to identify higher-level concepts. === Relevance feedback (human interaction) === Combining CBIR search techniques available with the wide range of potential users and their intent can be a difficult task. An aspect of making CBIR successful relies entirely on the ability to understand the user intent. CBIR systems can make use of relevance feedback, where the user progressively refines the search results by marking images in the results as "relevant", "not relevant", or "neutral" to the search query, then repeating the search with the new information. Examples of this type of interface have been developed. === Iterative/machine learning === Machine learning and application of iterative techniques are becoming more common in CBIR. === Other query methods === Other query methods include browsing for example images, navigating customized/hierarchical categories, querying by image region (rather than the entire image), querying by multiple example images, querying by visual sketch, querying by direct specification of image features, and multimodal queries (e.g. combining touch, voice, etc.) == Content comparison using image distance measures == The most common method for comparing two images in content-based image retrieval (typically an example image and an image from the database) is using an image distance measure. An image distance measure compares the similarity of two images in various dimensions such as color, texture, shape, and others. For example, a distance of 0 signifies an exact match with the query, with respect to the dimensions that were considered. As one may intuitively gather, a value greater than 0 indicates various degrees of similarities between the images. Search results then can be sorted based on their distance to the queried image. Many measures of image distance (Similarity Models) have been developed. === Color === Computing distance measures based on color similarity is achieved by computing a color histogram for each image that identifies the proportion of pixels within an image holding specific values. Examining images based on the colors they contain is one of the most widely used techniques because it can be completed without regard to image size or orientation. However, research has also attempted to segment color proportion by region and by spatial relationship among several color regions. === Texture === Texture measures look for visual patterns in images and how they are spatially defined. Textures are represented by texels which are then placed into a number of sets, depending on how many textures are detected in the image. These sets not only define the texture, but also where in the image the texture is located. Texture is a difficult concept to represent. The identification of specific textures in an image is achieved primarily by modeling texture as a two-dimensional gray level variation. The relative brightness of pairs of pixels is computed such that degree of contrast, regularity, coarseness and directionality may be estimated. The problem is in identifying patterns of co-pixel variation and associating them with particular classes of textures such as silky, or rough. Other methods of classifying textures include: Co-occurrence matrix Laws texture energy Wavelet transform Orthogonal transforms (discrete Chebyshev moments) =

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  • The Raimones

    The Raimones

    The Raimones (stylized as THE RAiMONES) is a 2017 generative music project that utilized artificial intelligence to compose music in the style of the American punk rock band The Ramones. Developed by Matthias Frey, a researcher at Sony CSL Tokyo, the project was an early experiment in applying deep learning to high-energy, minimalist musical genres. == Technical Development == The project utilized Long short-term memory (LSTM) recurrent neural networks to generate musical structures and lyrics. The model was trained on a dataset consisting of 130 Ramones songs in MIDI format and the band's complete lyrical catalog. The technical framework was built using Python and Jupyter Notebook, drawing influence from the character-level RNN text generation models popularized by Andrej Karpathy. Unlike contemporary AI music projects that focused on the harmonic complexities of classical or pop music, THE RAiMONES sought to determine if neural networks could replicate the "1-2-3-4" rhythmic consistency and formulaic nature of early punk. == "I'm Alive" == The primary output of the project was the song "I'm Alive," released in 2017. The work is described as a form of "augmented intelligence," a hybrid approach where the AI provides the compositional foundation and human musicians handle the arrangement and performance. The song was recorded by the musician Mr. Ratboy (Gilbert Avondet). Avondet's involvement provided a stylistic link to the subject material, as he had previously served as a touring guitarist for Marky Ramone and the Intruders in 1996. The project's discography has since been made available on major streaming platforms, including Apple Music. == Reception and Significance == The project has been cited as a "proof of concept" for AI's ability to tackle "noisy" and aggressive aesthetics. In 2019, the Belgian magazine Knack Focus profiled the project alongside other AI pioneers such as Holly Herndon, noting the project's attempt to recreate the sound of "deceased legends" while maintaining a distinct, machine-like quality. It has also been featured in academic settings, such as at UC Santa Cruz, as a case study for AI-driven genre mimicry.

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  • Intel Threat Detection Technology

    Intel Threat Detection Technology

    Intel Threat Detection Technology (TDT) is a CPU-level technology created by Intel in 2018 to enable host endpoint protections to use a CPU's low-level access to detect threats to a system. TDT consists of multiple components including Accelerated Memory Scanning, which uses the CPU's integrated GPU to scan memory, and Advanced Platform Telemetry, which uses processor-level activity monitoring to detect unusual activity. It is supported on sixth-generation or newer Intel Core CPUs and additional capabilities were added to the 11th generation Core processors. Intel TDT is integrated into several third-party anti-malware solutions including Microsoft Defender, Check Point Harmony Endpoint, CrowdStrike Falcon, and others. == Accelerated Memory Scanning == Accelerated Memory Scanning (also referred to as "Advanced Memory Scanning") uses the CPU's integrated GPU to scan memory for malicious code, instead of using the CPU directly. This improves system responsiveness during anti-malware scanning. and lowers power consumption. Features include pattern matching, using random forest decision trees, string extraction, entropy calculation, and Euclidean clustering. == Advanced Platform Telemetry == Advanced Platform Telemetry collects CPU-level telemetry to detect uncommon activity patterns which might be indicative of malware. The telemetry data is collected from the CPU performance monitoring unit (PMU) and doesn't require a large signature database to detect malware. Instead, it uses machine-learning based correlations to identify indicators of attack For example, Microsoft Defender is able to use TDT's Advanced Platform Telemetry features to detect processor usage patterns indicative of ransomware and cryptojacking with TDT so it can detect them.

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  • Fuzzy mathematics

    Fuzzy mathematics

    Fuzzy mathematics is a branch of mathematics that extends classical set theory and logic to model reasoning under uncertainty. Initiated by Lotfi Asker Zadeh in 1965 with the introduction of fuzzy sets, the field has since evolved to include fuzzy set theory, fuzzy logic, and various fuzzy analogues of traditional mathematic structures. Unlike classical mathematics, which usually relies on binary membership (an element either belongs to a set or it does not), fuzzy mathematics allows elements to partially belong to a set, with degrees of membership represented by values in the interval [0, 1]. This framework enables more flexible modeling of imprecise or vague concepts. Fuzzy mathematics has found applications in numerous domains, including control theory, artificial intelligence, decision theory, pattern recognition, and linguistics, where the modeling of gradations and uncertainty is essential. == Definition == A fuzzy subset A of a set X is defined by a function A: X → L, where L is typically the interval [0, 1]. This function is called the membership function of the fuzzy subset and assigns to each element x in X a degree of membership A(x) in the fuzzy set A. In classical set theory, a subset of X can be represented by an indicator function (also known as a characteristic function), which maps elements to either 0 or 1, indicating non-membership or full membership, respectively. Fuzzy subsets generalize this concept by allowing any real value between 0 and 1, thereby enabling partial membership. More generally, the codomain L of the membership function can be replaced with any complete lattice, resulting in the broader framework of L-fuzzy sets. == Fuzzification == The development of fuzzification in mathematics can be broadly divided into three historical stages: Initial, straightforward fuzzifications (1960s–1970s), Expansion of generalization techniques (1980s), Standardization, axiomatization, and L-fuzzification (1990s). Fuzzification generally involves extending classical mathematical concepts from binary (crisp) logic, where membership is determined by characteristic functions, to fuzzy logic, where membership is expressed by values in the interval [0, 1] via membership functions. Let A and B be fuzzy subsets of a set X. The fuzzy versions of set-theoretic operations are commonly defined as: ( A ∩ B ) ( x ) = min ( A ( x ) , B ( x ) ) {\displaystyle (A\cap B)(x)=\min(A(x),B(x))} ( A ∪ B ) ( x ) = max ( A ( x ) , B ( x ) ) {\displaystyle (A\cup B)(x)=\max(A(x),B(x))} for all x ∈ X {\displaystyle x\in X} . These operations can be generalized using t-norms and t-conorms, respectively. For example, the minimum operation can be replaced by multiplication: ( A ∩ B ) ( x ) = A ( x ) ⋅ B ( x ) {\displaystyle (A\cap B)(x)=A(x)\cdot B(x)} Fuzzification of algebraic structures often relies on generalizing the closure property. Let ∗ {\displaystyle } be a binary operation on X, and let A be a fuzzy subset of X. Then A is said to satisfy fuzzy closure if: A ( x ∗ y ) ≥ min ( A ( x ) , A ( y ) ) {\displaystyle A(xy)\geq \min(A(x),A(y))} for all x , y ∈ X {\displaystyle x,y\in X} . If ( G , ∗ ) {\displaystyle (G,)} is a group, then a fuzzy subset A of G is a fuzzy subgroup if: A ( x ∗ y − 1 ) ≥ min ( A ( x ) , A ( y − 1 ) ) {\displaystyle A(xy^{-1})\geq \min(A(x),A(y^{-1}))} for all x , y ∈ G {\displaystyle x,y\in G} . Similar generalizations apply to relational properties. For example, for example, for fuzzification of the transitivity property, a fuzzy relation R {\displaystyle R} on X {\displaystyle X} (i.e., a fuzzy subset of X × X {\displaystyle X\times X} ) is said to be fuzzy transitive if: R ( x , z ) ≥ min ( R ( x , y ) , R ( y , z ) ) {\displaystyle R(x,z)\geq \min(R(x,y),R(y,z))} for all x , y , z ∈ X {\displaystyle x,y,z\in X} . == Fuzzy analogues == Fuzzy subgroupoids and fuzzy subgroups were introduced in 1971 by A. Rosenfeld. Analogues of other mathematical subjects have been translated to fuzzy mathematics, such as fuzzy field theory and fuzzy Galois theory, fuzzy topology, fuzzy geometry, fuzzy orderings, and fuzzy graphs.

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  • A.I.s

    A.I.s

    A.I.s is a themed anthology of science fiction short works edited by American writers Jack Dann and Gardner Dozois. It was first published in paperback by Ace Books in December 2004. It was reissued as an ebook by Baen Books in June 2013. The book collects ten novelettes and short stories by various science fiction authors, together with a preface by the editors. == Contents == "Preface" (Jack Dann and Gardner Dozois) "Antibodies" (Charles Stross) "Trojan Horse" (Michael Swanwick) "Birth Day" (Robert Reed) "The Hydrogen Wall" (Gregory Benford) "The Turing Test" (Chris Beckett) "Dante Dreams" (Stephen Baxter) "The Names of All the Spirits" (J. R. Dunn) "From the Corner of My Eye" (Alexander Glass) "Halfjack" (Roger Zelazny) "Computer Virus" (Nancy Kress)

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